Pristine graphene based biosensor for biomarker detection and related core particles, materials compositions methods and systems

Abstract

This article provides graphene biosensors and related core particles, composition methods, and systems, wherein more than one original graphene sheet is coated with an organic or inorganic material to provide a core graphene particle, and a detectable component comprising a detectable portion and a peptide bond is connected to the core graphene particle via the peptide bond.

Description

[Technical Field]

[0001] This disclosure relates to the detection of biomarkers and related biosensors. In particular, this disclosure relates to pristine graphene biosensors for the detection of biomarkers, as well as related core particles, material compositions, methods, and systems. [Previous Technology]

[0002] The detection of biomarkers is central to many research fields, such as medicine and basic biology.

[0003] In particular, the detection of biomarkers is key to gaining an understanding of several biological events in a variety of organisms, including the occurrence of a variety of conditions ranging from cancer to viral infections to other events such as blood clotting and inflammation.

[0004] Despite progress in this field, robust and reproducible detection of nanomoles or lower concentrations of biomarkers, particularly those associated with disease, in samples remains challenging. [Summary of the Invention]

[0005] As those skilled in the art will understand, this disclosure relates to graphene-based biosensors (also referred to herein as graphene biosensors or biosensors) and related core particles, materials, composition methods, and systems that are so stable in samples in the sense of this disclosure that, in several embodiments, this allows for the detection of biomarkers of pimole or femole concentrations with high reproducibility and signal-to-noise ratio.

[0006] According to a first aspect, graphene core particles are described. The graphene core particle comprises more than one original graphene nanosheet coated with an organic and / or inorganic compound having a solubility in water of 1% by weight at 25°C. In the graphene core particle, the more than one original graphene nanosheet and / or the organic and / or inorganic compound of the coating are configured to provide functional groups capable of binding corresponding functional groups in aqueous solution.

[0007] According to a second aspect, a biosensor is described, configured to detect a target biomarker. The biosensor comprises: a graphene core particle of the present disclosure, and a detectable portion capable of emitting a fluorescent signal, the detectable portion being connected to the core particle via peptide bonds comprising a recognition sequence specific to the target biomarker. In the biosensor, the coating and peptide bonds are configured to set a quenching distance between the graphene nanosheet and the detectable portion, wherein the graphene nanosheet quenches the fluorescent signal of the detectable portion. In the biosensor, the peptide bonds are also configured to set an emission distance between the graphene nanosheet and the detectable portion after the recognition sequence is recognized by the target biomarker, wherein the graphene nanosheet does not quench the fluorescent signal of the detectable portion. In the biosensor, the target biomarker-specific peptide bonds and the detectable portion thus form a target-specific detectable component, which is configured to emit a detectable fluorescent signal after the peptide bonds are modified by the target biomarker.

[0008] According to a third aspect, a multiple biosensor is described, comprising: a core particle of the present disclosure and multiple target-specific detectable components connected to the graphene core particle. In the multiple biosensor, each target-specific detectable component is formed of a detectable portion capable of emitting a fluorescent signal in an excited state and having an excitation spectrum and an emission spectrum, the detectable portion being connected to a peptide bond containing a recognition sequence specific to a target biomarker. In each target-specific detectable component, the peptide bond is configured, in combination with a coating of the core particle, to provide a quenching distance between the detectable portion and the graphene sheet core particle, wherein the graphene nanosheet quenches the fluorescent signal of the detectable portion. In the biosensor, the recognition sequence for the target biomarker in each detectable component is configured to specifically detect the target biomarker, and multiple detectable components are selected to specifically detect multiple different target biomarkers. In the multiple target-specific detectable components of the biosensor, each detectable portion has an excitation spectrum with minimal overlap with the emission spectrum of another detectable portion of the multiple target-specific detectable components.

[0009] According to a fourth aspect, compositions are described comprising one or more graphene core particles and / or one or more biosensors as described herein, in combination with suitable adjuvants, such as carriers, diluents, excipients, etc. In some embodiments, the composition is a pharmaceutical composition and the adjuvant is a pharmaceutically acceptable adjuvant. In some embodiments in which the composition comprises two or more graphene biosensors, the graphene biosensors having two or more detectable components specific to different target biomarkers, wherein the excitation spectrum of each detectable portion of the two or more detectable components has minimal overlap with the emission spectrum of another detectable portion of the two or more detectable components.

[0010] According to a fifth aspect, methods and systems for preparing the core particles described herein are described. The method includes providing a pristine graphene nanosheet having a graphene surface; and coating the graphene surface with a layer of organic and / or inorganic molecules. The system for preparing the core particles described herein comprises a pristine graphene nanosheet having a graphene surface and a reagent, thereby chemically transforming the graphene surface to provide a pristine graphene nanosheet coated with a coating.

[0011] According to a sixth aspect, a method and system for preparing the biosensor described herein are described. The method includes providing the core particle described herein and attaching a detectable portion to a graphene surface via peptide bonds. The system for preparing the biosensor described herein comprises a pristine graphene nanosheet having a graphene surface, peptide bonds, a detectable portion, and reagents for attaching the detectable portion to the graphene surface via peptide bonds. In the system for preparing the biosensor described herein, the components are configured for simultaneous or sequential use in the method for preparing the biosensor described herein.

[0012] According to a seventh aspect, a method and system for detecting the activity of a biomarker in a biological sample are described. The method includes: contacting the biological sample with any biosensor according to this disclosure to generate a reaction solution; and detecting changes in the reaction solution. The system comprises the biosensor described herein and reagents used simultaneously or sequentially in the method for detecting the activity of a biomarker in a biological sample described herein.

[0013] According to an eighth aspect, a method and system for multiplex detection of the activity of multiple biomarkers in a biological sample are described. The method includes: contacting a biological sample with a multiplex biosensor according to the present disclosure, the multiplex biosensor being configured to provide detectable components specific to multiple biomarkers, contacting to generate a reaction solution; and detecting changes in the reaction solution. Alternatively, the method may include contacting the biological sample with multiplex biosensors, each of which provides a peptide bond specific to one of the multiple biomarkers. A system for multiplex detection of the activity of multiple biomarkers in a biological sample includes the multiplex biosensors described herein and / or biosensors each providing a peptide bond specific to one of the multiple biomarkers, and optionally multiple reagents used simultaneously or sequentially in the methods described herein for detecting the activity of biomarkers in a biological sample.

[0014] According to the ninth aspect, in the methods and systems used for detecting the target biomarker, the sample and / or test solution are heated prior to detection. In particular, it has been unexpectedly found that pretreatment of serum and / or running the protease assay at a selected temperature using both individual and additive mechanisms improves the signal-to-noise ratio.

[0015] The biosensors described herein, along with related graphene core particles, materials, composition methods, and systems, provide, in several embodiments, highly stable graphene-based biosensors in aqueous samples. They can maintain a suspension of at least 95% of the particles for several months, with a change in colloidal stability of -24% or less at OD650.

[0016] The biosensors and related core particles, materials, composition methods and systems described herein allow for the detection of biomarkers at pimole or femole concentrations with high reproducibility in several embodiments, wherein in some embodiments, the coefficient of variation of the signals detected between different preparation lots or batches is less than 10%.

[0017] As those skilled in the art will understand after reading this disclosure, the biosensors and related core particles, materials, composition methods and systems described herein, in several embodiments, allow for the detection of biomarkers of pimole or femole concentrations in aqueous solutions with a signal-to-noise ratio of 25 or higher among multiple sensors.

[0018] As those skilled in the art will understand upon reading this disclosure, the biosensors and related core particles, materials, composition methods and systems described herein, in several embodiments, allow for the detection of multiple biomarkers in aqueous samples with high multiplicity, where the number of different detectable components provided on the same graphene particles or in the same solution is up to 10 in the UV / Vis spectrum or up to 50 in the entire electromagnetic spectrum.

[0019] The biosensors described herein, along with related core particles, materials, composition methods, and systems, in several embodiments allow for the production of robust biosensors that can be dispersed, dried, and redispersed in aqueous form for up to 10 dispersion cycles. Therefore, as those skilled in the art will understand, the biosensors of this disclosure can be included in compositions and systems in a dried form.

[0020] The biosensors and related core particles, materials, composition methods, and systems described herein can be used in conjunction with a variety of applications where it is desirable to repeatedly detect one or more biomarkers, particularly when present at very low concentrations, such as pimozyl or femozyl concentrations. For example, the biosensors and related core particles, materials, composition methods, and systems described herein can be used in medical and diagnostic applications for visualizing disease pathological onset and responses to drug treatment and therapy, facilitating the development of cellular agents for diagnosis and therapy, and for visualizing biological events such as cell signaling, homeostasis, cell migration, responses to external stimuli such as temperature and pH, as well as other advantages that can be determined by those skilled in the art.

[0021] Other exemplary applications include the use of the biosensors and related core particles, materials, composition methods and systems described herein in several fields including: basic biological research, applied biology, bioengineering, etiology, medical research, medical diagnostics, therapeutics, in drug research and development of diagnostic and therapeutic methods and tools, and other fields that a person skilled in the art may identify after reading this disclosure.

[0022] Details of one or more embodiments of this disclosure are set forth in the accompanying drawings and the following description. Other features, objects, and advantages will be apparent from the specification, the drawings, and the claims.

Implementation Method

[0024] This disclosure relates to graphene-based biosensors and related core particles, materials, compositions, methods and systems for non-invasive detection and quantitative determination of a variety of biomarkers.

[0025] As used herein, the term "graphene" refers to a carbon allotrope composed of a single layer of atoms arranged in a two-dimensional honeycomb lattice nanostructure. The name derives from "graphite" and the suffix "-ene," reflecting the fact that graphene allotropes of carbon contain numerous double bonds. Specifically, in the sense of this disclosure, graphene refers to a single layer of at least 1000 carbon atoms in an sp2 hybrid arrangement in a hexagonal pattern. Graphene aggregates or graphene particles, used interchangeably herein, refer to at least two layers of graphene stacked together.

[0026] Therefore, in the sense of this disclosure, graphene contains at least 98% carbon atoms organized in sheets. In a graphene sheet, each atom is connected to its three nearest neighboring atoms by σ bonds and contributes an electron to the conduction band that extends throughout the sheet. ([3]) (See schematic diagrams and characteristics discussed in Examples 1 and 2).

[0027] In the sense of this disclosure, the typical apparent static contact angle θ* of a typical graphene surface is θ*=150±15°, which is critically superhydrophobic (θ*≥150°). Based on this finding and the fact that graphene is mainly composed of carbon (at least 99%±1%), it can be determined that graphene is one of the most hydrophobic materials known. Therefore, its dispersibility in water is <1×10-8 mg / mL. [4]

[0028] Graphene absorbs light in the ultraviolet, visible and near-infrared regions with an absorption coefficient exceeding that of graphite[5]. Therefore, in the sense of this disclosure, graphene is a material configured to absorb light of all visible wavelengths. ([3]) Stacked graphene has light absorption properties across the entire wavelength range from far ultraviolet to mid-infrared (100 to 1500 nm)[6]. For exploded graphene (5 to 7 layers), an absorption coefficient α at 660 nm was found to be >5,000 ml mg-1m-1, which is highly consistent with reference [7]. In general, the absorption coefficient α at 660 nm of graphene from various sources is between 1,000 and 5,000.

[0029] In graphene for the purposes of this disclosure, direct excitation occurs in the deep / far ultraviolet and the surface plasmas of graphene in the visible light range have a variety of intensities. Direct excitation and surface plasmas of graphene cannot effectively excite most detectable portions (e.g., fluorescent dyes or particles) that are capable of emitting fluorescent signals in the excited state. As those skilled in the art will understand, this indication applies to the entire visible light range of the electromagnetic spectrum (400 to 730 nm). [6]

[0030] In the sense of this disclosure, graphene includes virgin graphene (in its initial, pure, unoxidized form) as well as oxidized or surface-modified graphene, (surface-modified) graphene, and other forms of graphene that can be identified by a person skilled in the art.

[0031] Primitive graphene can be identified using techniques that are identifiable by a person skilled in the art, such as XRD (layer spacing), TEM and SEM (morphological), RAMAN (layer spacing and turbine layer characteristics, if present), and FTIR (lacking characteristic organic functional groups except for carbon-carbon double bonds and aromatic overtones). In particular, FTIR and RAMAN spectroscopy can be used to identify primitive graphene on graphene that has only undergone surface oxidation or chemical modification, because, as a person skilled in the art will understand, the relevant spectra will vary depending on the oxidation / chemical modification of the graphene surface. Given that variations in the relevant spectra can also be identified by a person skilled in the art, XRD, TEM, SEM, RAMAN, and FTIR can be used to identify primitive graphene that has undergone oxidation / chemical modification that disrupts the graphene layer structure. Furthermore, as a person skilled in the art will understand, in all cases, elemental analysis will determine that the graphene material is >98% carbon.

[0032] In the embodiments described herein, in the sense of this disclosure, pristine graphene may be used as a component of the graphene core particles described herein, wherein more than one pristine graphene sheet forms graphene particles, which are coated with a coating material configured to connect to a detectable component that is specific to a target biomarker.

[0033] As used herein, the term "particle" or "microparticle" means a portion of a substance having different physical or chemical properties, such as size, volume, density, or mass. Exemplary particles of various materials are described in U.S. Provisional Patent Application Serial No. 63 / 132,058, filed December 30, 2020, entitled "Optical Nanobiosensors for Rapid Identification of Lung Conditions," and in the corresponding PCT application No. __________, filed December 29, 2021, entitled "Nanosensors For Rapid Identification of Lung Conditions," each disclosure of which is incorporated herein by reference in its entirety.

[0034] Particles in the sense of this disclosure have a size of 1 to 5000 nanometers. Specifically, particles in the sense of this disclosure may have a size of 1 to 250 nanometers, and a size of 50 to 150 nm in nanoscale mesoscopic / systems. The size used herein is the maximum physical length between any two points of the particle. In the sense of this disclosure, the size of a particle refers to the longest distance between any two points of the particle. For example, an irregularly shaped graphene particle has the longest distance between any two carbons on the irregularly shaped graphene particle. In spherical particles, the size of the particle is the diameter of the spherical particle.

[0035] In the graphene particles of this disclosure, the quality distribution of the graphene particles can be detected by centrifugation and measuring the time required for sedimentation, as well as by other techniques that can be identified by a person skilled in the art. The actual size of the graphene particles in air (or N2, Ar) and a suitable hydrophobic solvent (e.g., chloroform) can be determined by dynamic light scattering (DLS). The surface charge of the particles can be determined by measuring the zeta potential.

[0036] Graphene particles may have layers arranged in an AB or Bernal stacking configuration, wherein half of the atoms are located directly above the hexagonal center in the lower graphene sheet, and the other half of the atoms are located above the lower hexagonal center. Graphene particles may also have layers arranged in an AA configuration, wherein the layers are precisely aligned, and each carbon atom in one layer is precisely positioned on top of another carbon atom in an adjacent graphene layer. As those skilled in the art will understand, in disordered stacked particles (AA and AB stacks), the individual layers can move relative to each other.

[0037] The graphene core particles of this disclosure contain original graphene sheets aggregated by van der Waals stacking in a configuration containing more than one graphene layer.

[0038] In the context of this disclosure, "sheet" or "layer" refers to a two-dimensional nanostructure with a thickness on the scale of 1 to 150 nm. As those skilled in the art will understand, a monolayer of carbon atoms in a hexagonal lattice of graphene material is 0.34 nm thick. Since a monolayer of graphene absorbs 2.3% of incident light and allows approximately 97.7% to pass through, the inclusion of two or more sheets in the graphene core particles of this disclosure allows for enhanced light absorption (blackness), which is caused by the electronic interactions of the stacked graphene layers, particularly when the stacked graphene layers are in a few-layer graphene structure.

[0039] As used herein, the term “multilayer graphene” means that the number of graphene layers is about 2 to about 100 graphene sheets, preferably 3 to 50 graphene sheets, and even more preferably 5 to 25 sheets. In multilayer graphene as disclosed herein, the minimum number of layers is 2, the maximum number of stacked graphene layers is 100, and the interlayer spacing in graphene is 0.348 ± 0.003 nm compared to the interlayer spacing in graphite of 0.337 nm. [8]. The maximum diameter of the stacked graphene sheets is limited to a lower limit of 10 nm and an upper limit of 5,000 nm.

[0040] In the sense of this disclosure, the number of nanosheets in graphene particles can be detected by transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy and other techniques that can be identified by a person skilled in the art.

[0041] In the graphene particles of this disclosure, the graphene nanosheets typically have a size of about 20 to about 500 nm (e.g., a size of 200 nm or 150 nm detected by transmission electron microscopy (TEM) images), where “size” refers to the maximum point-to-point distance in the graphene core particle (e.g., the diameter in the case of spherical particles, or the diagonal distance in a rectangular shape).

[0042] In some preferred embodiments, as those skilled in the art will understand after reading this disclosure, the graphene particles of this disclosure are multilayer graphene particles having 2 to 100 graphene sheets to allow for light absorption characteristics that can effectively quench the fluorescent signals emitted by the detectable portion of this disclosure.

[0043] In some preferred embodiments of this disclosure, the size of the graphene particles is further 900 nm or smaller, preferably 500 nm or smaller, more preferably 350 nm or smaller, and even more preferably 50 nm to 250 nm, to allow the formation of stable core particles of this disclosure in combination with coating materials and to detect target biomarkers at pimole or even femole concentrations.

[0044] In particular, the original graphene sheets forming the graphene particles described herein may be provided using methods and techniques that are identifiable by those skilled in the art.

[0045] For example, virgin graphene can be produced by the explosion of an acetylene / oxygen mixture in an explosion chamber. The resulting graphene nanosheets aggregate in the gas phase and form an aerogel, which can be harvested according to methods identifiable by a person skilled in the art to provide graphene that is also identified as detonation graphene or explosive graphene.

[0046] Pristine graphene can also be obtained from graphite by oxidative exfoliation. According to relevant methods, graphite is oxidized under chemically very harsh conditions (e.g., the Hummers process) and thermally to produce oxidized monolayer graphene (called graphene oxide). The graphene oxide is then reduced to several layers of pristine graphene using hydrazine or other reducing agents according to methods identifiable by a person skilled in the art. [9]

[0047] Primitive graphene can also be obtained through flash synthesis. Therefore, flash graphene can be obtained by applying direct current to convert organic materials into several layers of graphene using methods identifiable by those skilled in the art. [10,11]

[0048] Pristine graphene can also be obtained by CVD (chemical vapor deposition), which produces a graphene monolayer on a substrate (not suitable for sensitive optical biosensors) to provide CVD graphene according to methods identifiable by a person skilled in the art. [9]

[0049] In some embodiments of the graphene core particles described herein, the original graphene starting material is preferably prepared using detonation-synthesized graphene or flash-synthesized graphene prepared by processes and methods that can be identified by a person skilled in the art (see, for example,

[12] , [8],

[13] ,

[14] , which are incorporated herein by reference in their entirety).

[0050] The preparation of detonation-sized graphene requires a one-step process, which involves the controlled detonation of a carbon-containing material, such as a solid, liquid, or gas, with an oxidant or oxygen source (e.g., O2, N2O, NO) at relatively high temperatures in a reaction vessel to produce pristine graphene nanosheets and branched fractal aggregates of these nanosheets without the use of a catalytic material. Generally, the reaction vessel is loaded with a desired amount of reactants, and a spark is used to achieve the detonation of the material. This produces an aerosol that typically undergoes gelation to form an aerosol gel containing graphene particles. (

[15] and (

[16] ).

[0051] In a scale-up method, the apparatus includes a reaction chamber, a vacuum source operatively connected to the reaction chamber, and an ignition element. The reaction chamber is operatively coupled to a carbon-containing material source and an oxidant source. The vacuum source is operable to selectively evacuate at least a portion of the contents of the reaction chamber, particularly after the generation of particulate material. The ignition element is also operatively connected to the reaction chamber and configured to initiate combustion of a quantity of carbon-containing material and a quantity of oxidant delivered to the reaction chamber from their respective sources. The ignition element includes a pair of electrodes operable to generate an ionized arc therebetween, each electrode being contained in a respective housing removably housed within an ignition assembly. The scale-up apparatus and synthesis process for the mass production of pristine graphene are described in Co-Pending U.S. Serial No. 63 / 039,087, filed June 15, 2020, and in the corresponding PCT / US2021 / 037321, filed June 15, 2021, both of which are incorporated herein by reference.

[0052] Some exemplary carbon-containing materials used in the reaction include carbon-rich precursors, gases, gas mixtures, powders, aerosols, and other injectable materials. The carbon-containing material may comprise a single material or compound, or a mixture of carbon-containing compounds. Some examples include coal, petroleum coke, biochar, carbon black, biowaste (e.g., food or agricultural waste), biomass, or organic waste (e.g., cellulose materials, grasses, etc.). It should be understood that materials with higher carbon content are particularly preferred. The starting material may comprise any hydrocarbon compound, and particularly saturated or unsaturated C1 to C12 hydrocarbon compounds, with highly saturated C1 to C12 hydrocarbons being particularly preferred. In some specific embodiments, preferred hydrocarbon materials comprise acetylene, ethylene, toluene, butadiene, and / or isoprene. Temperature and / or pressure parameters may be adjusted depending on the selected material.

[0053] In one or more embodiments, the combustion reaction occurs at a temperature of at least 3000 K, at least 3500 K, or at least 4000 K. In some specific embodiments, the combustion reaction occurs at temperatures of about 3000 K to about 5000 K, about 3500 K to about 4500 K, or about 4000 K. It has been found that combustion of carbonaceous materials and oxidants at these temperatures favors the formation of highly ordered graphene microparticles rather than graphitic soot. If desired, an inert gas material such as helium, neon, argon, or nitrogen may be included in the reaction mixture loaded into the reaction vessel to indicate temperature control during combustion. Furthermore, in some embodiments, particularly in those in which the combustion reaction is a detonation, the combustion of the reaction mixture proceeds very rapidly. Detonation typically involves an ultrasonic exothermic front that is accelerated by the medium and ultimately drives a shock wave front that propagates directly in front of it. In some embodiments, the combustion has a duration of about 5 to about 100 milliseconds, about 10 to about 75 milliseconds, or about 20 to about 50 milliseconds.

[0054] The ratio of oxidant to carbon-containing material present in the reaction vessel prior to detonation can contribute to the characterization of the graphene microparticles formed after the detonation of the reaction mixture. In some embodiments, the molar ratio of oxidant to carbon-containing material is 1.5 or less, preferably 1.0 or less, and even more preferably 0.5 or less. In some specific embodiments, the ratio of oxidant to carbon-containing material is about 0.1 to about 1.5, about 0.18 to about 1.2, about 0.18 to about 1.0, or about 0.18 to about 0.8, or about 0.18 to about 0.5. This process allows for the synthesis of large quantities or large quantities of graphene with extremely high purity. For the preferred purpose of producing graphene nanosheet microparticles in the size range desired herein, the ratio of oxidant to carbon-containing material is preferably about 0.2 to about 0.5, wherein about 0.3, 0.35, or 0.4 are particularly preferred ratios. The preferred size of the original graphene microparticles is about 20 to about 500 nm, wherein about 150 nm + / - 20 nm is the target size range. It is noteworthy that, for the biosensors of this disclosure, the resulting microparticles need not be monodisperse. Instead, polydisperse mixtures of graphene microparticles can be readily used to fabricate biosensors. In fact, according to light scattering experiments, graphene aggregates exhibit a superaggregate fractal morphology with a fractal size of Df = 2.5 ± 0.1 at the micrometer scale, consisting of smaller aggregates with a fractal size of Df = 1.8 ± 0.1. This behavior has not been previously observed for metal, metal oxide, metal / metal oxide, or semiconductor particles in (nano) biosensors.

[0055] Alternative methods for synthesizing pristine graphene include flash graphene (PCT / US2019 / 047967, filed August 23, 2019, WIPO Publication WO2020 / 051000, which is incorporated herein by reference), which uses flash Joule heating of various carbon-based materials or other carbon sources, such as coal, petroleum coke, biochar, carbon black, food waste, rubber tires, and mixed plastic waste, to convert the material into graphene. The carbon source is slightly compressed between two electrodes in a reaction vessel, and a high-voltage discharge from a capacitor bank causes the carbon source material in the reaction vessel to reach at least 3000 K in less than 100 milliseconds. The process converts amorphous carbon in the carbon source into flash graphene. The yield in this process depends largely on the carbon content of the starting material. In some embodiments, the carbon source material may be mixed with carbon black or other similar conductive materials to improve the conductivity of the material. In some embodiments, the average particle size of the flash graphene is less than 20 nm. In some implementations, flash-evaporated graphene is produced in the form of relatively large flakes with an average size of 0.5 to 1.2 μm. Such flakes can be suitable as alternatives to sensors in which graphene nanosheets are deposited as a monolayer on a solid surface, rather than used in the form of particles.

[0056] The original graphene produced by these new synthesis processes can be used in a variety of forms, but is preferably in the form of branched fractal aggregates, nanosheets, crystalline flakes, nanoplatelets, and platelet chains, as monolayer or multilayer graphene, and can generally be macroscopically characterized as a fluffy or fuzzy high-purity (≥98.5% carbon) black powder or particulate matter.

[0057] The particles can be observed under an electron microscope as thin monolayers entangled with each other with overlapping edges, or as more ordered stacks of nanosheets comprising or consisting of: two to three layers, but possibly up to 25 layers, possibly up to 10 layers, for example 2 to 25 layers, and even more preferably about 5 to about 10 layers (where the total size is 20 nm to 500 nm).

[0058] Therefore, the term "primitive" graphene as used herein is specifically used to refer to high-purity graphene produced by converting a carbon source into graphene at high temperatures (e.g., about >3000 K) (e.g., rapidly heating or burning amorphous carbon into detonated graphene, flash graphene, etc.) compared to graphene prepared by organic synthesis, chemical vapor deposition, or exfoliation from graphite. In other words, primitive graphene is preferably substantially free of graphite or graphite oxide. Thus, primitive graphene being of high purity means that it is substantially free of (less than 0.5% w / w, preferably less than 0.1% w / w) foreign matter and impurities, wherein the carbon content is at least about 98.5%, and preferably at least 99% (and conversely, the oxygen content is less than 1%). A discussion of the detection of detonated graphene by transmission electron microscopy (TEM) is provided in Example 21 and in the related Figures 19A and 19B, which show some exemplary TEM images of primitive detonated graphene.

[0059] In some embodiments involving biosensors based on pristine graphene nanosheet particles and associated core particles, the material comprises detonated graphene (preferably 0.25, 0.30 or 0.35).

[0060] In some of the most preferred embodiments, the graphene nanoparticles comprise graphene aggregates in a fractal form.

[0061] As used herein, the object-related term “fractal” refers to an object that exhibits a property known as self-similarity, which can be reduced to the geometry of smaller parts, each of which is a scaled-down copy of the whole. Fractal size is a statistic describing how a fractal appears to fill space.

[17]

[0062] Therefore, as those skilled in the art will understand, the fractal size of stacked graphene layers in a fractal form is typically Df = 2.5 ± 0.3 (upper limit) to Df = 1.8 ± 0.4 (lower limit) [8] (see also Example 1).

[0063] The fractal properties of graphene compared to other nanomaterials used in biosensors can be determined by light scattering methods (e.g., SEM (scanning electron microscopy)

[18] ) and other techniques that can be identified by a person skilled in the art (e.g., light scattering methods

[19] ) (see also Example 1).

[0064] In the graphene core particles described herein, more than one graphene nanosheet is coated with a coating of organic and / or inorganic materials. Therefore, in order to allow coating, the graphene nanosheets are functionalized or derivatized to provide functional groups on the surface capable of incorporating corresponding functional groups provided on the organic and / or inorganic materials to allow coating formation.

[0065] As used herein, the term "functional group" refers to a specific group of atoms within a molecular structure that is responsible for the characteristic chemical reactions of that structure. Some exemplary functional groups include hydrocarbons, groups containing double or triple bonds, groups containing halogens, groups containing oxygen, groups containing nitrogen, and groups containing phosphorus and sulfur, all of which are identifiable by those skilled in the art.

[0066] The term "corresponding" used in conjunction with elements such as functional groups refers to two or more elements that are capable of reacting with each other under suitable conditions. Generally speaking, the reaction between the corresponding part and, in particular, the functional group results in the combination of the two elements.

[0067] As used herein, the terms "bonding" and its variations, "association," refer to an attractive interaction between two elements that results in stable association, where the elements are very close to each other. If each element is contained within a molecule, the result of bonding is typically the formation of a molecular complex. Attractive interactions in the sense of this disclosure include both non-covalent and covalent bonding. Covalent bonding indicates a chemical bond characterized by the sharing of electron pairs between atoms or between atoms and other covalent bonds. For example, the attractive-repulsive stability formed between atoms when they share electrons is called covalent bonding. Covalent bonding includes a variety of interactions, including σ-bonding, π-bonding, metal-nonmetal bonding, atomic interactions, and three-center two-electron bonds. Non-covalent bonding as used herein indicates, for example, the type of chemical bond in protein-protein interactions, which does not involve the sharing of electron pairs but rather a more dispersed variation of electromagnetic interactions. Non-covalent bonding includes ionic bonds, hydrophobic interactions, electrostatic interactions, hydrogen bonds, and dipole-dipole bonds. Electrostatic interactions involve association between two entities with opposite charges.

[0068] Some exemplary pairs of functional groups and corresponding coupling chemicals are shown in the following schemes (I) to (IX). Further corresponding functional group pairs will be identifiable to those skilled in the art in the context of this disclosure.

[0069] In the embodiments described herein, the pristine graphene nanosheets obtained by the methods described herein can be functionalized with suitable functional groups as described herein to facilitate the preparation of derivative compounds suitable for use as core particles in biosensors. Graphene can be functionalized by covalent functionalization using chemical modification of organic groups. Another approach involves the Fenton oxidation process described in detail in PCT / US2020 / 038055, filed June 17, 2020, which is incorporated herein by reference. In this process, the surface of stacked graphene is oxidized using ferrous sulfate and hydrogen peroxide as oxidants, resulting in >95% carboxylic acid functional groups on the surface. All other possible functional groups, such as hydroxides, aldehydes, ketones, or ethylene oxides, are present in <5%.

[0070] This method typically involves reacting pristine graphene microparticles with hydrogen peroxide in an aqueous reaction solution at a low pH (<5.0) in the presence of an iron source. The reaction solution is stirred or agitated for a period of time, allowing the hydrogen peroxide radicals generated in the reaction solution to react with the graphene microparticles, oxidizing the outer surface of the pristine graphene microparticles in the solution and producing graphene / graphene oxide microparticles. The resulting graphene / graphene oxide microparticles contain a graphene core with a thin graphene oxide surface coating or shell, which can be readily modified by a variety of reactions.

[0071] In one specific embodiment, a preferred graphene derivative having a high-density surface -COOH groups, referred to herein as "carboxylated graphene," can be synthesized using pristine graphene and surface functionalization based on the generation of stable azinon ring anchors. In a specific process, pristine graphene nanoparticles are dispersed at room temperature in a suitable organic polar solvent system, such as anhydrous dimethylformamide (DMF), dimethylacetamide (DMA), dimethyl sulfoxide (DMSO), acetone, acetonitrile, or chloroform. After dispersion, a carboxylic acid having a primary or secondary halogen (e.g., chloroacetic acid, 3-iodopropionic acid, 4-bromobutyric acid, 4-bromopentanoic acid, 5-bromopentanoic acid, etc.) is added, followed by the addition of anhydrous azidoic acid or its azide derivatives, such as anhydrous sodium azide (NaN3), lithium azide, and / or potassium azide. The temperature of the reaction solution is then slowly increased (approximately 1°C / min) to approximately 80°C. A nucleophilic substitution (SN) reaction occurs between 20°C and 40°C, in which an organic azide and a solvent-soluble sodium halide are formed. The organic azide releases dinitrogen (N₂) and forms a nitride intermediate at temperatures above approximately 50°C. The nitride is characterized by having 6 electrons instead of 8 in the outer shell of nitrogen. This reactive intermediate undergoes cycloaddition with π-π double bonds at the graphene cellular lattice surface to form a stable aziridine anchor (three-membered CNC ring). Aziridine cycloaddition provides the opportunity to add a customized amount of carboxylic acid to the graphene surface without impairing the optical and electrical properties of the graphene itself. In this case, a high density of carboxylic acid groups can be added to the surface of the graphene nanosheet particles for further functionalization and / or interaction with water-soluble polymer coatings (see Examples 2 to 9).

[0072] In some embodiments, further functionalization modifications of graphene may be described to allow for further reactions and the formation of coatings according to this disclosure. In this regard, although aliphatic bonding is used herein for functionalization, aromatic reactions for chemical amination of graphene surfaces have also been described in the literature. (

[20] is incorporated herein by reference (see Example 10).

[0073] Furthermore, as those skilled in the art will understand after reading this disclosure, additional modifications can be made to reduce the surface charge of graphene, making it suitable for combining cationic polymers in place of or in addition to the aforementioned functionalizations.

[0074] In the graphene core particles described herein, the graphene nanosheets are coated with a coating of organic and / or inorganic materials.

[0075] As used herein, the term "coating" refers to the sheet, quantity, or thickness of material covering a surface or body. In particular, in the sense of this disclosure, a coating refers to the thickness of organic and / or inorganic material covering the surface of graphene particles. In the graphene core particles of this disclosure, the coating has two main functions: a) allowing the graphene to disperse in an aqueous buffer, and b) maintaining a distance of <20 nm between the detectable portions of the disclosed connections to facilitate effective quenching in its tethered state.

[0076] In particular, in some embodiments described herein, the coating is applied by a hydrophilic organic and / or inorganic compound having a solubility in water of 1% by weight at 25°C.

[0077] As used herein, the term "organic compound" means any chemical compound that contains carbon-hydrogen bonds and is capable of promoting and / or accepting hydrogen bonds from water. All or some of the hydrogen atoms in an organic compound may be substituted with other atoms.

[0078] As used herein, the term "inorganic compound" refers to a chemical substance that does not contain CH bonds. It is typically an inorganic compound having at least one element that is heavier than fluorine.

[0079] In particular, as those skilled in the art will understand, the organic and / or inorganic compounds suitable for providing the biosensors described herein have a solubility in water of at least 10 g / L H2O and are capable of being covalently connected to or electrostatically connected to graphene as described herein.

[0080] In some embodiments, the organic and / or inorganic compounds suitable for providing the biosensors described herein are characterized by consistent logP (consensus log P, or cLogP) < 0.

[0081] In the sense of this disclosure, as is known to those skilled in the art, the cLogP value refers to the logarithm of its partition coefficient between n-octanol and water, log(Coctanol / Cwater). High lipophilicity corresponds to a high cLogP value. As will be understood by those skilled in the art upon reading this disclosure, the organic molecules of the coatings of this disclosure can be configured to have a specific cLogP value using a combination of moiety and / or substituents.

[0082] The cLogP range of the coating material according to this disclosure is expected to be 1 to -2.5, preferably 0 to -2.

[0083] In some embodiments, the inorganic compound includes mesoporous silica, mesoporous silazane, alumina, tin oxide, titanium dioxide, zirconium dioxide, oxides of lanthanides, alkoxides and alkoxyamines, and amine precursors (e.g., Si(NH-CH3)4; Si(O-CH2-CH3)3(CH2-CH2-CH2-NH2); M(O-CH2-CH3)4 where M = Si, Sn, Ti, Zn; or M(O-CH2-CH3)3 where (M = Al or lanthanide (III) cations), which react with hydroxyl, carboxylic acid or epoxy rings on the surface of graphene or chemically modified graphene to form stable O-metallic bonds. Subsequently, the alkoxy, alkoxyamine and amine bonds around the graphene-linked metal are hydrolyzed, producing a semiconductor oxide and metal oxide layer around the graphene.

[21]

[0084] In particular, in some embodiments, the inorganic compound may be mesoporous silica in the form of silica having pores with a diameter of 2 to 50 nm. Mesoporous silica can be prepared using, for example, tetraethyl orthosilicate (TEOS) or (3-mercaptopropyl)trimethoxysilane, commonly abbreviated as MPTMS, as starting materials. In one exemplary method, the silica precursor is deposited in a tetrahydrofuran / water mixture (THF / H2O: 99%:1% to 95%:5% by weight) by stirring at room temperature for 24 hours, followed by centrifugation at 5,000 RPM for 10 minutes, redispersing in H2O, and then collecting by centrifugation (5,000 RPM for 15 minutes). As those skilled in the art will understand, the thickness of the mesoporous silica layer surrounding the (stacked) graphene will be determined by the weight ratio of TEOS or MPTMS to graphene (0.001 / 1.0 to 0.1 / 1).

[0085] In some embodiments where the inorganic compound is mesoporous silica, the functionalization of the silica surface with polar Si-OH groups (silanols) can be achieved, for example, by one of the following steps: - Formation of silyl ethers: Si-OH + XR → Si-OR + HX (X = halogen), followed by organic chemistry. For this purpose, the organic group R gas contains a polar functional group. - Linking the C-terminus of the peptide sequence or a carboxylic ester group in its side chain to an amine group contained in the coating via a stable amide bond. - Reacting the mesoporous silica layer (or metal oxide layer) surrounding the graphene with SiCl4, SiHCl3, SiH2Cl2, or Si(CH3)2Cl2, and subsequently chemisorbing the N-terminus of the peptide onto the Si-Cl groups on the surface. This can also be achieved by reacting polar hydroxyl, thiol, or carboxylic acid groups. - The polar Si-Cl groups on the surface can react with NaN3 (sodium azide) to form silicon azide, which undergoes a click reaction.

[0086] In some embodiments, the organic compounds include aliphatic amines, aromatic amines, oligoethylene glycols, aliphatic oligoamines, ethylene oligoimides, aliphatic carboxylic acids, aromatic carboxylic acids, aliphatic alcohols, aliphatic sulfonic acids, aromatic sulfonic acids, and combinations thereof.

[0087] In some embodiments, the inorganic and / or organic compound forming the coating comprises one or more polymers. The term “polymer” as used herein means a substance or material consisting of very large molecules or macromolecules composed of many repeating subunits. Polymers can be organic or inorganic, synthetic and natural, and are typically produced by the polymerization of many small molecules called monomers. Thus, as those skilled in the art will understand, their macromolecular qualities relative to small molecule compounds produce unique physical properties, including toughness, high elasticity, viscoelasticity, and a tendency to form amorphous and semi-crystalline structures rather than crystals.

[22]

[0088] In one or more embodiments, the coating organic polymer may be represented by formula (I). Wherein M1 to Mm are polymeric monomer portions formed from the same or different polymerizable organic monomers, wherein m is 1 to 10 and y1, y2 to ym are each independently 1 to 1,000.

[0089] In particular, some exemplary polymerizable organic monomers include aziridine propylene oxide, ethylene glycol, acrylic acid, methacrylic acid, 2-hydroxyethyl methacrylate, methacrylamide, N-isopropylacrylamide, 2-hydroxypropyl methacrylate, N-vinylpyrrolidone, 2-vinylpyridine, 2-vinylpyridine N-oxide, and 2-ethyl-2-oxazoline.

[0090] Therefore, in one or more embodiments, one or more water-soluble organic polymers may subsequently be added to the surface of the graphene derivative (e.g., carboxylated graphene). Some examples of suitable water-soluble polymers of formula (I) include polyethyleneimine, polycaprolactone, polyethylene glycol, polyvinyl alcohol, polyvinylpyrrolidone, (meth)acrylate polymers and (meth)acrylamide polymers, polystyrene-carboxylic acid, polystyrene-amine, and natural biopolymers (e.g., dextrose, pachycarboxylic acid, chitosan, alginate, cellulose, and hyaluronic acid), and mixtures or copolymers thereof.

[0091] In some embodiments, the polymer comprises or is composed of homopolymers, such as polyethyleneimine, polycaprolactone, polyvinyl alcohol, polyglycolate, polyvinylpyrrolidone, (meth)acrylate polymers and (meth)acrylamide polymers, polystyrene-carboxylic acid, polystyrene-amine, polystyrene-sulfonic acid, dextrose, pachymonopolysaccharide, chitosan, alginate, cellulose and hyaluronic acid, and mixtures or copolymers thereof.

[0092] In some embodiments, the polymer comprises or is composed of block copolymers. In those embodiments where block copolymers are used, the hydrophobic polymer, such as polystyrene, polyisobutylene, polyvinyl chloride, polyethylene, polypropylene, and others, may be a component of the block copolymer, provided that the block copolymer is characterized by a consistent logP < 0, or a solubility in water > 1.0 × 10³ g / L H₂O.

[23] It is desirable that the hydrophobic polymer adsorbs onto the surface of the original graphene, while its hydrophilic component mediates the water dispersibility. In some of these embodiments, the surface charge of the graphene is positive (ζ potential = +60 mV) (e.g., in exploded graphene), [1, 8] which allows for a combination of hydrophobicity and charge attraction when the polymer is negatively charged, for example, polystyrene-sulfonic acid used in block copolymers of polystyrene and other organic copolymers that can be identified by a person skilled in the art.

[0093] In some embodiments where a hydrophobic-hydrophilic block copolymer is used, the block copolymer is dissolved in a solvent (e.g., dimethylformamide (DMF)) that also disperses graphene well. Under continuous acoustic treatment, H2O is added to the mixture, forcing the hydrophobic ends of the block copolymer to bond with the graphene. As those skilled in the art will understand, this bonding is strengthened due to the presence of negatively charged groups in the hydrophobic-hydrophilic block copolymer.

[0094] In some embodiments, preferred organic polymers include one or more of polyethyleneimine, polycaprolactone, polyethylene glycol, polyvinyl alcohol, polyvinylpyrrolidone, (meth)acrylate polymers and (meth)acrylamide polymers, as well as polystyrene-carboxylic acid and polystyrene-amine. Other preferred coatings are chemically modified oligoethylene glycol and oligoimide with star-shaped radial dendritic molecules.

[0095] In some embodiments, graphene particles may be coated with a single layer of water-soluble organic molecules, such as oligoethylene glycol, that can be chained to the graphene surface (see Example 32).

[0096] Aliphatic oligoimides, aliphatic sulfides, star-shaped dendritic molecules, water-soluble peptides (except for the common sequence), carbohydrates (e.g., galactose, glucose, mannose, fructose, sialic acid, hyaluronic acid, dextrose, glycan-like Gb3 trisaccharides, galabiose). Note that all carbohydrates can be amination at multiple sites.

[0097] In some embodiments, graphene particles may be coated with an inorganic water-soluble shell (e.g., a mesoporous silicon dioxide nanolayer). Mesoporous silicon dioxide, mesoporous amino silicon dioxide, and titanium dioxide are preferred.

[0098] In the graphene core particles of this disclosure, one or more organic and / or inorganic compounds react with functional groups (e.g., carboxyl groups, see Examples 3 to 9) provided on the surface of the graphene nanosheets to form a thin and dense polymer layer (as opposed to a thick layer or halo) around the graphene aggregates. (See Examples 30, 32, and 35).

[24]

[0099] In one embodiment of this disclosure, it was unexpectedly found that graphene core particles comprising more than one pristine graphene nanosheet are stable in aqueous solution and are capable of being linked to detectable components that are specific to the target biomarker, the pristine graphene nanosheet being coated with a coating of an organic and / or inorganic compound having a solubility of 1% by weight in water at 25°C.

[0100] It has been unexpectedly found that graphene particles with the above characteristics exhibit stability in aqueous solutions for more than 20 days or even 30 days. In addition, as those skilled in the art will understand, it has been found that, depending on the concentration, possible sedimentation of such coated graphene particles can be resolved by dispersing the particles (e.g., by ultrasonic oscillation treatment), so that a solution containing more than 95% particles can be obtained.

[0101] In particular, the biosensor as described is chemically and physically stable in the buffer suspension, which is defined by the reduction of light absorption of the detectable portion in the buffer suspension by equal to or less than 20%, preferably equal to or less than 10%, and more preferably equal to or less than 1% within 30 days.

[0102] The terms “stable” and “stability” as used herein refer to chemical stability and colloidal stability.

[0103] As used herein, the term "chemical stability" refers to maintaining the chemical identity of a biosensor. As those skilled in the art will understand, a biosensor as described herein comprises chemical bonds that link each component of the biosensor together, including graphene, coating materials, peptides, and detectable portions. It should also be understood that each of the graphene, coating materials, peptides, and detectable portions comprises chemical bonds that link atoms together to form the graphene, coating materials, peptides, and detectable portions. Maintaining the chemical identity of a biosensor means that the chemical bonds constituting the biosensor are not broken and no new chemical bonds are formed. Such chemical integrity can be determined by measurements including, but not limited to, elemental analysis, ultraviolet-visible absorption spectroscopy, magnetic resonance spectroscopy, Raman spectroscopy, infrared spectroscopy, and fluorescence spectroscopy.

[0104] The term "colloidal stability" as used herein refers to the physical state of a colloidal suspension that maintains a biosensor. Colloidal stability depends on several types of interactions, including van der Waals and electrostatic interactions (the classic Derjaguin-Landau-Verwey-Overbeek (DLVO) theory), steric interactions (e.g., polymer adsorption), and hydrophobic interactions. Electrophoretic light scattering (ELS) is a commonly used technique for evaluating the potential of dispersions to maintain stability. ELS allows for the measurement of the zeta potential of the dispersion, which provides information about electrostatic interactions and, by extrapolation, its tendency to aggregate. Zeta potential is considered a reliable indicator of dispersion stability, but several parameters such as steric interactions, sedimentation, or hydrophobic interactions can also have a strong influence.

[0105] In some embodiments, the core graphene particles and associated biosensors described herein are chemically and physically stable in the buffer suspension, defined by a reduction in light absorption of the detectable portion in the buffer suspension by 20% or less, preferably 10%, and more preferably 1% or less, over 30 days.

[0106] Therefore, in some embodiments described herein, stable graphene core particles of this disclosure refer to graphene particles that, when contained in a suspension in an aqueous solution, produce a solution in which at least 95% of the particles remain in the suspension and the colloidal stability change at OD650 is -24% or less.

[0107] In particular, data have been obtained showing a change in reactive colloidal stability of 26% or less, with a subrange of 22% to 25% and an average sedimentation of 24%, reflecting the worst performance of four batches of nanobiosensors according to this disclosure. In one representative embodiment, the stability of graphene measured at OD650 at 60 minutes showed a 9% and 10% reduction in absolute colloidal stability for the NE and MMP15 nanobiosensors, respectively (see batch 4 in Example 40). Therefore, it is expected that the graphene biosensors of this disclosure according to some preferred embodiments will exhibit a reduction in colloidal stability of less than 10% in aqueous solutions.

[0108] In particular, in some preferred embodiments described herein, the stable graphene core particles of this disclosure comprise one or more organic and / or inorganic compounds that react to form a coating 1 to 20 nm thick having a packing density of at least 2.40 ± 0.05 × 10⁻⁵ mol / g, preferably up to 6.0 ± 0.5 × 10⁻⁴ mol / g.

[0109] Therefore, in the preferred stable graphene core particles of this disclosure, the minimum geometric extension of the attached coating is 0.45 nm and can reach up to 20 nm, and the coating exceeds 50%, preferably at least 85%, and more preferably at least 90%. Most preferably, a polymer monolayer is formed around the graphene, preferably completely encapsulating the graphene nanosheet therein. In some of the most preferred embodiments, the average size of the graphene particles, as determined by scanning electron microscopy and digital light scattering measurements, is from 10 nm to 5000 nm, preferably from 150 to 500 nm.

[0110] In those embodiments, as those skilled in the art will understand after reading this disclosure, the stability of the graphene core particles can be measured in months or even years, with some instances of graphene particles and associated biosensors being stable in aqueous solutions for more than 12 months.

[0111] The determination of surface coverage was performed by comparing BET surfaces. The Brunauer-Emmett-Teller (BET) theory aims to explain the physical adsorption of gas molecules on solid surfaces and serves as the basis for an important analytical technique for measuring the specific surface area of ​​materials. It measures the adsorption of N2 on graphene at 77 K. The result of this measurement is the size of the accessible surface (here: 150 ± 40 m² / g). The average density of surface groups was determined from titration experiments with polar head groups (acid or base). Dynamic light scattering was used to measure the size of the polymer to be bonded, as well as the size of graphene + layer + polymer. Depending on the chemical properties of the surface coating and the polymer, the zeta potential of the surface continuously changes until complete surface coverage is achieved.

[0112] In some embodiments described herein, the graphene core particles of this disclosure are combined with detectable components to provide the graphene biosensor described herein. In particular, the graphene biosensor described herein includes a detectable portion connected to the graphene core particles via peptide bonds, said peptide bonds containing recognition sequences specific to target biomarkers.

[0113] As used herein, the term "biomarker" refers to a biomolecule found in the biological environment that is associated with a biological event. In particular, biomarkers may be associated with the occurrence of physiological events (biological events associated with the normal function of a living organism and its parts) or with changes in physiological conditions, especially with signs of normal or abnormal processes or diseases.

[0114] As used herein, the term "symptom" in relation to a living organism refers to a physical condition of an individual living organism (as a whole or as one or more parts thereof) that does not conform to a standard physical condition relating to the individual's overall physical, mental and social condition. Symptoms as described herein include, but are not limited to, conditions and diseases, wherein the term "symptom" refers to a condition of a living individual relating to an abnormality in the function of the body or any part thereof, and the term "disease" refers to a condition of a living individual that impairs the normal function of the body or any part thereof and is typically manifested by distinguishing signs and symptoms.

[0115] Therefore, in some embodiments described herein, target biomarkers related to an individual’s condition may be selected for diagnostic purposes and / or to observe how the body responds to treatments for the condition.

[0116] The target biomarkers of this disclosure are also molecules configured to interact with peptide bonds to modify the relevant conformation. In some embodiments, the target biomarkers are proteins configured to specifically interact with peptide recognition sequences.

[0117] As used herein, the term “protein” refers to a polypeptide having secondary, tertiary, and possibly quaternary structures. The secondary, tertiary, and quaternary structures of proteins can occur on a variety of length scales (from tenths of an Å to nm) and time scales (from nanoseconds to seconds), making the secondary, tertiary, and possibly quaternary structures dynamic and not entirely rigid in many cases.

[0118] As used herein, the term "peptide" refers to a polymer composed of two or more amino acid monomers and / or their analogues, wherein the backbone of the polymer is formed by the portion formed by the α-carbon, amino group, and carboxyl group of the amino acid in the polymer. As used herein, the terms "amino acid," "amino acid monomer," or "amino acid residue" refer to any naturally occurring amino acid, any non-naturally occurring amino acid, and any artificial amino acid, including both D and L optical isomers of all amino acid subgroups. In particular, an amino acid is an organic compound consisting of an amine (-NH2) and a carboxylic acid (-COOH) and a side chain specifically linked to the α-carbon for each amino acid. Different amino acids have different side chains and different characteristics, such as charge, polarity, aromaticity, reduction potential, hydrophobicity, and pKa. Amino acids can be covalently linked by peptide bonds to form polymers through a reaction between the amino group of a first amino acid and the carboxylic acid group of a second amino acid.

[0119] The term "peptide" includes an amino acid polymer of any length containing a full-length protein, its analogues, and fragments. A polypeptide provides the primary structure of a protein, wherein the term "primary structure" of a protein refers to the sequence of amino acids covalently linked in the polypeptide chain to form the polypeptide polymer. The protein "sequence" represents the order in which the amino acids form the primary structure. The covalent bonds between amino acids within the primary structure may include peptide bonds or disulfide bonds. In the sense of this disclosure, polypeptides generally consist of a linear chain of amino acid residues covalently linked by peptide bonds. Based on the nature of the free radicals at each end, the ends of the linear polypeptide chain covering the terminal residues and adjacent segments are referred to as carboxyl-terminals (C-terminus) and amino-terminals (N-terminus).

[0120] Based on their size, peptides can be classified into oligopeptides (2 to 20 residues) and polypeptides (>20 residues). Proteins typically consist of polypeptide chains longer than 50 residues.

[25] The average length of a peptide can be estimated to be 0.35 to 0.40 nm per amino acid, depending on the peptide’s 2D structure (α-helix or amorphous).

[25] Note that peptides forming β-sheets are not suitable for the design of biosensors because their release kinetics upon enzyme cleavage are very slow. Peptides with 50 amino acids have a length of 17.5 to 20 nm, depending on their 2D structure. It should also be noted that non-natural amino acids can be used for these peptides to anchor them to graphene surfaces or graphene-linked organic or inorganic shells, or for linking fluorophores (organic dyes or nanostructures).

[0121] As used herein, the terms "amino acid," "amino acid monomer," or "amino acid residue" refer to an organic compound consisting of amine and carboxylic acid functional groups and a side chain specific to each amino acid. Specifically, α-amino acid refers to an organic compound consisting of an amine (-NH₂) and a carboxylic acid (-COOH) and a side chain specific to each amino acid linked to an α-carbon. Different amino acids have different side chains and different characteristics, such as charge, polarity, aromaticity, reduction potential, hydrophobicity, and pKa. Amino acids can be covalently linked via peptide bonds to form polymers through a reaction between the amino group of a first amino acid and the carboxylic acid group of a second amino acid. For the purposes of this disclosure, amino acid refers to any one of twenty naturally occurring amino acids, non-natural amino acids, and includes both D and L optical isomers.

[0122] In some preferred embodiments, the target biomarker is a protein with a length >50 amino acids, such as enzymes and chemokines / cytokines. Some of these enzymes have coenzymes and cofactors consisting of heterocyclic metal-binding molecules (e.g., all matrix metalloproteinases). As those skilled in the art will understand, some of these proteins are post-translational modified and linked to sugars or lipids.

[0123] In some other embodiments, the target biomarker may comprise a number of inorganic and organic molecules capable of altering or cheating the conformation of the peptide bond. As used herein, the term "conformation" in relation to peptides or proteins is defined as the spatial arrangement of its constituent atoms, which determines the protein structure of the molecule (the three-dimensional arrangement of atoms in a polypeptide) and the overall shape of the protein. As used herein, the term "cleavage" in relation to compounds and peptides refers to the breaking of a chemical bond and / or a chemical reaction involving the breaking of a chemical bond. As those skilled in the art will understand, peptide bond cleavage typically results in the enzymatic hydrolysis of the peptide bond within the bond and causes the bond to break into smaller peptide moieties.

[0124] A method for identifying organic and / or inorganic biomarkers configured to interact with peptide bonds to modify the conformation of peptides or cleave peptide bonds may be performed by: - ​​Computationally modeling the tertiary structure of the peptide bond to provide a computationally modeled peptide bond; - Computationally modeling the tertiary structure of a candidate organic or inorganic biomarker to provide a computationally modeled candidate organic or inorganic biomarker; - Determining the target site for the interaction between the computationally modeled peptide bond and the computationally modeled candidate organic or inorganic biomarker; - Determining the change in the computationally modeled peptide bond at the target site after the interaction with the candidate organic or inorganic biomarker; - Selecting the computationally modeled peptide bond and the organic or inorganic target biomarker when the determined change is a change in peptide bond conformation or cleavage.

[0125] Alternatively or additionally, the method for identifying an organic and / or inorganic biomarker configured to interact with a peptide bond may include - contacting a candidate organic and / or inorganic biomarker with a peptide bond for a period of time under conditions that allow interaction between the candidate organic and / or inorganic compound and the peptide bond; and - detecting changes in peptide bond conformation and / or peptide bond cleavage, performed after contact to provide a detection result; and - selecting the organic and / or inorganic biomarker when the detection result indicates that a change in peptide bond conformation or peptide bond cleavage has been detected, thereby providing an identified organic and / or inorganic biomarker.

[0126] The biosensor of this disclosure can then be configured to detect a target compound by linking peptide bonds to a detectable portion and to a graphene core particle of this disclosure, the graphene core particle being selected to have a coating of thickness having a detectable portion at a quenching distance of 20 nm or less relative to the linked graphene particle. In particular, as those skilled in the art will understand after reading this disclosure, if the target compound has a detected peptide bond configuration change that leads to an increase in peptide bond length, the coating is configured such that, after the increase in peptide bond length, the detectable portion in the biosensor provided after linking is at the emission distance. As those skilled in the art will understand, the functionality of the biosensor thus constructed can then be tested.

[0127] For example, the identification of protein biomarkers and corresponding supramolecular recognition sequences is performed according to the following procedure: 1) The primary structure of the target is retrieved from a database (e.g., UniProtKB or a Protein Data Bank). 2) The tertiary structure of the target is determined and subsequently refined using protein structure prediction software, such as the GalaxyWEB protein structure prediction cluster developed by Dr. Seok of the Computational Biology Lab at Seoul National University, South Korea. 3) The primary structure of a monoclonal antibody (MAB) fragment targeting the target is retrieved from a source (e.g., a protein database, SciFinder, or information provided by an antibody supplier). The tertiary structure is generated using Galaxy-WEB. 4) The docking procedure between the target and the antibody chain is simulated using the Mobyle platform developed by the Institute Pasteur Biology IT Center in Paris, France. This procedure displays the "true" epitopes of the target. 5) The final "site finder" program is then run on the Mobyle platform to dock the target epitope with the improved structure of the antibody (Ab) fragment. In an example of protein-protein interactions, this structure reveals the peptide sequence of the Ab fragment that is actually responsible for binding to the target. (F)

[0128] In addition to, or alternative to, an exemplary method for identifying protein biomarkers and corresponding supramolecular recognition sequences, a final "site finder" program is then performed on the Mobyle platform to dock the target protease epitope to an improved protease activity site structure. This structure reveals the peptide sequence that is actually responsible for the active site binding to the target.

[0129] In addition to, or alternative to, an exemplary method for identifying protein biomarkers and corresponding supramolecular recognition sequences, cleavage sites and sequences for protease targeting can be identified from available sites such as MEROPS (peptidase database).

[0130] In an exemplary method for identifying protein biomarkers and corresponding supramolecular recognition sequences, if the supramolecular recognition sequence is linear, it is synthesized and used as the recognition sequence for a nanosensor. If it is not linear, and if the LOD of the resulting nanosensor is not at least 10⁻¹⁴ M, structural information from another mAb is used to repeat the computer (in silico) program.

[0131] In some exemplary embodiments, many organic molecules can also interact with proteins, such as enzymes capable of interacting with peptides and altering their conformation. In this regard, all enzyme inhibitors can alter the conformation of the proteins they bind to. Some exemplary enzyme inhibitors include the non-peptide HIV-1 protease inhibitor tipranavir, diisopropylfluorophosphate (DFP), the anticancer drug methotrexate, and penicillin G, which is a streptomycin transpeptidase inhibitor.

[0132] In some exemplary embodiments, the interaction between the target biomarker and the peptide bond can lead to the cleavage of the peptide bond. In particular, in some embodiments, the target biomarker comprises a protease. Some exemplary proteases include serine proteases, cysteine ​​proteases, threonine proteases, aspartic proteases, glutamic proteases, metalloproteinases, and aspartic peptide lyases. Generally, as those skilled in the art will understand, all 553 known proteases of potential interest in the human genome (21 aspartic proteases, 143 cysteine ​​proteases, 186 metalloproteinases, 176 serine proteases, and 27 threonine proteases) and their analogues in rodent models can be detected by the biosensors of this disclosure

[27] .

[0133] In some embodiments, the target biomarker comprises an enzyme that performs post-translational modifications on peptides, altering their biophysical properties (kinetics, effective length, etc.). Some examples are argininases (which convert arginine to ornithine) and all enzymes that attach or move any chemical group to the side chain of a peptide. Some examples include enzymes capable of (de)phosphorylation (kinases, phosphatases), glycosylation enzymes (N-glycosylation) - glycosylation, glycosylphosphatidylinositylation, C-glycosylation and phosphorylated glycosylation), ubiquitination, S-nitrosylation, methylation, N-acetylation and esterification (C-terminal glycosylphosphatidylinositol (GPI)) anchoring, N-terminal myristylation, S-myristylation, S-isoprenelation.

[0134] In some embodiments, the target biomarker comprises a protein (e.g., a cytokine / chemokine) capable of binding a customized oligopeptide. The binding event alters the biophysical properties of the peptide (kinetics, effective length, etc.). As those skilled in the art will understand, exemplary detectable biomarkers include all cytokines of interest, chemokines, signaling peptides (e.g., pro-inflammatory factors, anti-inflammatory factors, growth factors), and hormones.

[0135] In some embodiments described herein, as those skilled in the art will understand, peptide bonds that are part of the detectable components of the graphene biosensor of this disclosure are selected to specifically bind to the target biomarker.

[0136] The term "specificity" and its variations, used herein, relating to the binding of the first molecule and the second molecule, refers to the recognition, contact, and formation of a stable complex between the first and second molecules, along with minimal or no recognition, contact, and formation of stable complexes between each of the first and second molecules and any other molecules that may be present. Some exemplary specific bindings are antibody-antigen interactions, cell receptor-ligand interactions, polynucleotide hybridization, enzyme-substrate interactions, and other interactions that can be identified by a technician. The term "specificity" and its variations, used herein, relating to computer-supported tools (e.g., software), refers to a tool sequence that, after alignment of the target sequence with a database sequence, is able to identify the target sequence (e.g., one of the biomarkers described herein) within a sequence set, such as in a database. Exemplary software configured to specifically detect target sequences includes Primer-3, PerlPrimer, and PrimerBlast. Multiple analytes can be measured simultaneously in a single experiment.

[0137] In some embodiments, the recognition sequence may be a supramolecular recognition sequence or a common sequence of the target protein, meaning that the bond recognition is specific to the target protein (i.e., contains a selective binding site). Therefore, it should be understood that the recognition sequence avoids non-specific binding motifs. Furthermore, the oligopeptide bond may be "cleavable" or "uncleavable." The recognition sequences described herein are preferably cleavable for rapid diagnostic purposes.

[0138] In some embodiments of the graphene nanobiosensors of this disclosure, the peptide bond may contain any number of recognition sequences or common sequences of the target biomarker, meaning that the bonded sequence is specific to the target biomarker (i.e., contains selective binding and / or cleavage sites for the target biomarker). Therefore, it should be understood that the bonded sequence avoids non-specific binding motifs. The peptide bond is typically a short, linear oligopeptide that positions the detectable portion within a suitable proximity to the graphene carrier particle for quenching. In some specific embodiments, the peptide bond length may be less than 100 amino acid residues, preferably less than 75 amino acid residues, more preferably 50 amino acid residues or less. In some most preferred embodiments, the peptide bond will be even shorter and less than 50 amino acid residues in length, more preferably less than 30 amino acid residues, and even more preferably 20 amino acid residues or less.

[0139] For example, in the graphene biosensor of this disclosure, the peptide bond may be 4 to 50 amino acid residues, more preferably 4 to 30 amino acid residues, and even more preferably 10 to 20 amino acid residues. The references to "specific interaction" or "specificity" in relation to recognition, as used herein, are intended to distinguish between non-specific binding or reaction between the recognition sequence and a molecule, and mean that the specific target biomarker set to which the oligopeptide sequence can interact is limited, and in some cases even exclusive, such that no other molecule besides the target biomarker binds or is cleaved at a considerable rate or frequency.

[0140] In particular, in some embodiments described herein, as those skilled in the art will understand, the specificity of the peptide bond to the target biomarker results in minimal interaction between the peptide bond and biomarkers that are different from the specific biomarker targeted by the peptide bond.

[0141] Biosensors can be configured to target a wide variety of biomarkers, depending on the selected recognition sequence. Some examples include a variety of enzymes, including proteases, kinases, arginases, cytokines / chemokines, dioxygenases (e.g., indoleamine 2,3-dioxygenase 1 and / or tryptophan 2,3-dioxygenase), esterases, etc., which can then be correlated with a specific health status in the sample being tested. For example, such biomarkers can be indicators of cancer or precancerous cell activity, bacterial activity, inflammation, etc., and are known in the literature. In one or more embodiments, the recognition sequence comprises, consists substantially of, or is even composed of a "common sequence," which refers to an enzyme receptor (peptide sequence) that undergoes enzymatic cleavage in the presence of a target analyte (e.g., protease, esterase, etc.). Such shared sequences are described, for example, in U.S. Patent Nos. 8,969,027 and 9,216,154, and U.S. Publication No. 2017 / 0219548, each of which is incorporated herein by reference without inconsistency with this disclosure. Relevant shared sequences are provided in tables within this disclosure. A guiding example is that shared sequences can be designed on a computer for typical proteases, such that the target protease cleaves the shared sequence faster than any other protease.

[0142] In one or more embodiments, the recognition sequence comprises, substantially comprises, or even comprises: a recognition sequence for detecting post-translational modifications of the enzyme, as described, for example, in WO 2016 / 149637, filed March 18, 2016, which is incorporated herein by reference without inconsistency with the present disclosure. In this embodiment, arginine in GR7G is converted to ornithine GO7G, thereby altering the peptide's kinetics and geometric elongation, enabling fluorescence detection in an optical Fe / Fe3O4 nanobiosensor. Other post-translational enzymes detected, as those skilled in the art will understand, are indoleamine-2,3-dioxygenase and tryptophan-2,3-dioxygenase.

[0143] In the presence of a target biomarker, such as a target enzyme, the enzyme modifies the recognition sequence (without cleavage), and more specifically modifies the primary structure (amino acid residues and metabolites) and secondary structure of the recognition sequence. As used herein, such “modification” or “post-translational modification” refers to the conversion or transformation of amino acid residues (and particularly side chains) in the recipient sequence into different forms and / or different residues. This subsequently extends, “unfolds,” or “unwinds” the secondary structure of the recognition sequence and / or increases and / or decreases the mobility of the recognition sequence. In one or more embodiments, the recognition sequence comprises, is substantially composed of, or is even composed of supramolecular recognition sequences to detect physical binding without chemical modification or enzyme cleavage. In particular, the target protein binds linearly to the supramolecular recognition sequence, such that the initially folded secondary structure, such as α-helices and / or β-sheets, is modified and linearly extended or unfolded. In each case, the interaction of the target analyte (e.g., protein, enzyme, etc.) with the recognition sequence results in a detectable change in the biosensor, indicating the presence of the target analyte.

[0144] In one or more embodiments, the recognition sequence comprises, substantially comprises, or even comprises: a recognition sequence for a biomarker of inflammation, infection, and / or bacterial activity, as described, for example, in WO2016 / 018798 filed July 27, 2015, which is incorporated herein by reference to the extent that it is not inconsistent with this disclosure. In some of these embodiments, the target biomarker may be an enzyme indicating inflammation, infection, and / or bacterial activity, such as MMP-8, MMP-9, MMP-11, MMP-14, and neutrophil elastase, compared to MMP-1, MMP-2, MMP-3, MMP-7, MMP-12, and MMP-13. Other enzyme-targeting biomarkers considered to partially indicate inflammation, infection, and / or bacterial activity, as will be understood by those skilled in the art, are cathepsins-B, -D, -E, -K, and -L, and urokinase plasminogen activator.

[0145] Generally, enzymes such as proteases and / or esterases are exemplary biomarkers indicating inflammation, infection, and / or bacterial activity. The recognition sequence is selected to be cleavable specifically by the protease or esterase (e.g., using a common sequence that can only be cleaved by the target protease) or generally (e.g., using a sequence that can be cleaved by any protease or esterase). Following bond cleavage, particles are released from each other, resulting in a detectable spectral change in the system.

[0146] The recognition sequence is typically provided as part of an oligopeptide bond between the detectable portion and the original graphene carrier particle. A suitable oligopeptide bond will comprise (essentially composed of or even composed of) the recognition sequence of the target biomarker, typically along with a terminal carboxylic acid group (C-terminus) and a terminal amino group (N-terminus). The oligopeptide may also contain a thiol group at the C-terminus and a carboxylic acid group at the N-terminus. In some embodiments, the oligopeptide linker comprises a hydrophilic region of at least 10 amino acids located at the N-terminus of the recognition sequence and a C-terminal linker region located at the C-terminal of the recognition sequence, wherein the C-terminal linker region contains a thiol reactive group at its end. In some embodiments, the C-terminus of the oligopeptide contains a cysteine ​​residue, lysine, or aspartic acid. In some embodiments, the N-terminal hydrophilic region of the oligopeptide has an excess of positive or negative charge in a ratio of about 1:1. The N-terminal hydrophilic region may also contain amino acid residues capable of forming hydrogen bonds with each other.

[0147] In some embodiments, the C-terminal and N-terminal connection regions may actually consist of any non-cleavable amino acid sequence (e.g., non-recognition sequence): (X)n-[recognition sequence]-(X)m, where X is any amino acid (which does not produce a cleavage sequence in the sequence), and n and m are each less than 5.

[0148] In some embodiments, the recognition sequence may include one or more spacer residues at the N-terminus and / or C-terminus. For example, one to ten amino acids (natural and non-natural, L- and D-, or combinations thereof) may be used as spacer regions at one or both ends to further extend the oligopeptide chain length, depending on the selected detectable portion. Some examples include N-terminal sequences such as GAG- and C-terminal sequences such as -AG.

[0149] Based on the target biomarker, the desired recognition sequence can be selected and synthesized. It should be understood that multiple sequences can also be obtained from the literature or determined through computer-aided research.

[0150] Some exemplary identification sequences, along with their associated biomarkers, are listed in the table below. In some cases, the proposed cut points are indicated by "-". Variants of the listed sequences are also considered.

[0151] Table 1. Common and recognition sequences a. Monocyte chemoattractant protein-1; b. Chemoattractant hormone (CC motif) ligand 20 (CCL20) or liver activation regulated chemokine (LARC) or macrophage inflammatory protein-3 (MIP3A); c. Granulocyte colony-stimulating factor; d. Interleukin-1 receptor-like type 1; e. Macrophage inflammatory protein; f. Osteoclast-stimulating factor-1; g. Inhibitor of metalloproteinases 1; h. Thymic stromal lymphopoietin.

[0152] In one or more embodiments, the nanosensor may be configured with a recognition sequence specific for cytokine detection. In those embodiments, the oligopeptide bond contains a linear supramolecular recognition sequence (i.e., a complementary site) of the receptor / binding site of the target cytokine. In the presence of a cytokine, the stretching of the chain during cytokine binding results in a detectable signal change (e.g., increased fluorescence) from the attached detectable particle. The nanosensor can measure cytokine concentration. Some exemplary supramolecular recognition sequences are listed in Table 2 below.

[0153] Table 2: Supramolecular recognition sequences used for detecting protein targets

[0154] a. Monocyte chemoattractant protein-1; b. Chemoattractant hormone (CC motif) ligand 20 (CCL20) or liver activation regulated chemokine (LARC) or macrophage inflammatory protein-3 (MIP3A); c. Granulocyte colony-stimulating factor; d. Interleukin-1 receptor-like type 1; e. Macrophage inflammatory protein; f. Osteoclast-stimulating factor-1; g. Inhibitor of metalloproteinases 1; h. Thymic stromal lymphopoietin; i. CD: differentiation cluster; j. ATP-dependent RNA helicase DDX3X (from human bronchial airway epithelial cells); k. Alkaline phosphatase, placental type; PPB1 phosphatase; l. Arginine-tRNA ligase, cytoplasmic; m. E3 ubiquitin ligase UBR4 (small airway epithelial cells); *Estimated limit of detection (LOD).

[0155] In some embodiments, the supramolecular recognition sequence may contain one or more spacer residues at the N-terminus and / or C-terminus. For example, one to ten amino acids (natural and non-natural, L- and D-, or combinations thereof) may be used as spacer regions at one or both ends. Some examples include N-terminal sequences such as GAG- and C-terminal sequences such as -AG.

[0156] In some embodiments, any other suitable linear peptide sequence (e.g., at least 10 amino acid residues) can be used as a supramolecular recognition sequence, in addition to sequences with a shared motif. If synthesized on a peptide synthesizer, peptide sequences of up to about 25 amino acid residues can be used, while sequences synthesized in organisms (e.g., *E. coli*) can be up to about 300 amino acid residues in length. Any designed sequence should be checked for the presence of cleavage sequences using a database (e.g., MEROPS).

[0157] In some embodiments, peptide aptamers for targeting CD9, CD63, and CD81 have also been developed, which are universally accepted cytosome surface markers. These aptamers are capable of binding to cytosomes and allow for their direct separation from collected biological samples using nanosensors without prior (ultra-fast) centrifugation. The combined use of CD9, CD63, and CD81 ensures that virtually all cytosomes from samples, such as those from airway cells, are captured. After treatment with a lysis buffer, the cargo of captured cytosomes can then be analyzed. Cytosome analysis presents another existing aspect of this technology. Cytosomes from small airway epithelial cells and bronchial airway epithelial cells show significant differences in their proteasomes (containing the content of four transmembrane proteins (CD9, CD63, CD81) in their outer membrane).

[0158] In some embodiments, the biosensors of this disclosure demonstrate flexibility in adapting to the basic nanosensor structure by modifying particles, oligopeptide bonds, etc., to suit available sensor technologies, and similarly by using multiple sensor technologies to detect additional targets. For example, as those skilled in the art will understand, nanosensors for detecting protease activity can be fabricated, wherein the graphene core particles of this disclosure are used in conjunction with peptide bonds and portions described in U.S. Patent No. 8,969,027.

[0159] In some embodiments, the enzyme common sequences used in nanosensors may also be combined with one or more of the above, including those in Table 3 below, wherein the cleavage point is indicated by "-".

[0160] Table 3

[0161] In some embodiments, the common sequence of the peptide bond may include an N-terminal spacer region (e.g., GAG, GAA, GAP) and a C-terminal spacer region (e.g., GA, AG, or EG).

[0162] In the graphene biosensor of this disclosure, peptide bonds are used to connect the detectable portion or label to the graphene core particle.

[0163] The term “connection” or variations thereof as used herein means a connection or binding by a bond, linkage, force or tie to hold two or more components together, encompassing direct or indirect connections, wherein, for example, a first molecule is directly bound to a second molecule or material, or one or more intermediate molecules are located between the first molecule and the second molecule or material.

[0164] As used herein, the term "detection" or variations thereof means determining the existence, presence, or fact of a target within a limited spacer region, including but not limited to samples, reaction mixtures, molecular complexes, and substrates. As used herein, "detection" or variations thereof may include determining the chemical and / or biological properties of a target, including but not limited to its ability to interact with and, in particular, bind to other compounds, its ability to initiate another compound, and other properties that a person skilled in the art can identify upon reading this disclosure. Detection can be quantitative or qualitative. When detection refers to, relates to, or involves identifying the quality or class of a target or signal in terms of its relative abundance against another unquantified target or signal, it is "qualitative." When detection refers to, relates to, or involves measuring the number or amount of a target or signal (also referred to as quantitative), detection is "quantitative," including but not limited to any analysis designed to determine the amount or proportion of a target or signal. As a person skilled in the art will understand, quantitative detection in the sense of this disclosure includes semi-quantitatively detecting above / below a certain amount of a target biomarker.

[0165] As used herein, the terms "detectable portion" or "label" refer to detectable molecules, including but not limited to radioisotopes, fluorophores, chemiluminescent dyes, chromophores, enzymes, enzyme acceptors, enzyme cofactors, enzyme inhibitors, dyes, metal ions, particles, metal sols, ligands (e.g., biotin, avidin, streptavidin, or haptens), etc. Therefore, as used herein, the term "labeled signal" indicates a signal emitted from a label that allows for detection, including but not limited to radioactivity, fluorescence, chemiluminescence, and the generation of compounds at the end of an enzymatic reaction.

[0166] In some embodiments described herein, the detectable portion is capable of emitting a fluorescent signal. The term fluorescent signal refers to a longer wavelength light signal emitted by a compound that can be excited by electrons from a shorter wavelength light source.

[0167] Some exemplary detectable markers of compounds capable of being excited by a light source and emitting fluorescent signals include detectable particles that generate detectable signals (e.g., optical or spectral), such as fluorescence or color changes, which can be visually perceived or measured with suitable instruments. Chromophores / luminescent bodies suitable for determination in this invention include any organic or inorganic dyes, fluoresces, phosphors, light-absorbing particles (e.g., Au, Ag, Pt, Pd), combinations thereof, or nonmagnetic metallized complexes thereof. Preferably, the selected chromophores / luminescent bodies are smaller than the graphene carrier particles and will typically have a particle size (maximum edge-to-edge dimension, i.e., diameter) of less than about 20 nm.

[0168] Suitable organic dyes are selected from coumarins, pyrene, anthocyanins, benzene, N-methylcarbazole, erythrosine B, N-acetylglucosamine, 2,5-diphenyloxazole, rubrene, and N-(3-sulfopropyl)acridine. Some specific examples of preferred coumarins include 7-aminocoumarin, 7-dialkylaminocoumarin, and coumarin 153. Some examples of preferred benzenes include 1,4-bis(5-phenyloxazol-2-yl)benzene and 1,4-diphenylbenzene. Some examples of preferred anthocyanins include oxazolidin, thioazolidin, indocyanine, merocyanine, and carbonylcyanine. Other exemplary cyanides include ECL Plus, ECF, C3-oxazine, C3-thiocyanine dye (EtOH), C3-thiocyanine dye (PrOH), C5-indocyanine, C5-oxazine, C5-thiocyanine, C7-indocyanine, C7-oxazine, CypHer5, Dye-33, cyanides (Cy7, Cy7.5, Cy5.0, Cy5.5, Cy3Cy5ET, Cy3B, Cy3.0, Cy3.5, Cy2), CBQCA, NIR1, NIR2, NIR3, NIR4, NIR820, SNIR1, SNIR2, SNIR4, cyanide 540, pinacyanol iodide, 1,1-diethyl-4,4-carbonyl iodide cyanide, and staining agents (Stains). All), dye-1041 or dye-304, and all BODIPY dyes.

[0169] Anthocyanin dyes can be linked in various conformations, ideally using peptide bonds shorter than the Förster radius of the anthocyanin dye (5 to 6 nm for Cy3.0 and Cy3.5, 6 to 7 nm for Cy5.0 and Cy5.5, and approximately 7 nm for Cy7 and Cy7.5). The maximum length of the tandem chain, consisting of any spacer region (approximately 2.84 nm) and no more than 12 amino acid residues in the cleavage sequence (up to 4 nm), indicates that shorter cleavage sequences (uPA and MMP) are suitable for use with Cy3.x and Cy5.x dyes, while cathepsins are preferably linked with Cy5.x and Cy7.x dyes to allow for optimal quenching of the anthocyanin dyes in the tandem chain. For all anthocyanins, their emission maxima are redshifted relative to the spontaneous fluorescence of human urine. Multiple anthocyanins can be linked to a single particle to create oligoplexing nanoplatforms for the simultaneous measurement of the activities of up to four enzymes. All four dyes in the ultraviolet A or blue region of the electromagnetic spectrum can be excited simultaneously, or each dye can be excited individually. All cyanide dyes have an excitation maxima that are blue-shifted by 20 to 25 nm (typical values ​​for fluorescent singlet states) relative to their emission maxima. An exemplary emission spectrum is shown for: NS-Cy3.0 (λex=538, λem=560), NS-Cy5.5 (λex=639, λem=660), NS-Cy7.0 (λex=740, λem=760), and NS-Cy7.5 (λex=808, λem=830).

[0170] Suitable inorganic dyes are selected from metallized and nonmetallized porphyrins, phthalocyanines, dihydroporphyrins (e.g., chlorophyll A and B), and metallized chromophores. Preferred porphyrins are selected from tetracarboxy-phenyl-porphyrin (TCPP) and Zn-TCPP. Preferred metallized chromophores are selected from ruthenium polypyridine complexes, osmium polypyridine complexes, rhodium polypyridine complexes, iridium(III) 3-(1-methylbenzimidazol-2-yl)-7-(diethylamino)-coumarin complexes, and iridium(III) 3-(benzothiazol-2-yl)-7-(diethylamino)-coumarin complexes.

[0171] Suitable fluorophores and phosphors are selected from phosphorescent dyes, luciferins, rhodamine (e.g., rhodamine B, rhodamine 6G) and anthracene (e.g., 9-cyanoanthracene, 9,10-diphenylanthracene, 1-chloro-9,10-di(phenyl-ethynyl)anthracene).

[0172] The broad UV / Vis absorption spectra of graphene and its derivatives allow for the linking of multiple detectable markers with their respective (different) recognition sequences to target more than one biomarker at a time. For this to work, the excitation and absorption spectra of the co-linked detectable markers should have no overlap or only minimal overlap, thereby minimizing FRET and / or interference between individual signals. However, in principle, any combination of fluorescent dyes is conceivable, but BODIPY dyes are particularly suitable because they are characterized by a small Stokes shift (the distance between the lowest excitation band and the maximum fluorescence band). An example of this strategy is the parallel use of BODIPY FL (505λex / 513λem), BODIPY 530550 (534λex / 554λem), BODIPY TR (589λex / 617λem), and BODIPY 630650 (646λex / 660λem) linked by their respective (different) sequences to independently monitor the activity of four biomarkers using a single biosensor. Some exemplary BODIPY dyes are shown and discussed in Example 7.

[0173] Alternatively, cyanine dyes 2, 3, 5 and 7 are another possible combination. However, cyanine dyes have a much larger Foster radius and can interfere with each other even when diluted.

[0174] Fluorescent quantum dots and fluorescent particles can also be used. Quantum dots are semiconductors composed of atoms from elements in groups II-VI or III-V of the periodic table (e.g., CdSe, CdTe, InP). The optical properties of quantum dots can be controlled by synthesizing (usually stable) shells. Such quantum dots are called core-shell quantum dots (e.g., CdSe / ZnS, InP / ZnS, InP / CdSe). Quantum dots of the same material but different sizes can emit different colors of light. Their brightness is attributed to the quantization of energy levels due to the confinement of electrons in all three spatial dimensions. In bulk semiconductors, electron-hole pairs are bound within the Bohr exciton radius, which is characteristic of various types of semiconductors. Quantum dots are smaller than the Bohr exciton radius, which leads to discrete energy levels. The band gap ΔE between the valence band and conduction band of a semiconductor is a function of the size and shape of the nanocrystal. Compared to conventional organic fluorophores, quantum dots have a slightly lower luminescence quantum yield, but they have a much larger absorption cross section and a very low photobleaching rate. The moiré extinction coefficient of quantum dots is about 105 to 106 M⁻¹ cm⁻¹, which is 10 to 100 times larger than that of organic dyes.

[0175] Core / shell quantum dots have a high-bandgap shell around their low-bandgap core, which emits light and is not absorbed by the shell. The shell passivates surface nonradiative emission from the core, thereby increasing the photoluminescence quantum yield and preventing natural degradation. Type I quantum dots (e.g., CdSe / ZnS) have a higher energy conduction band and a lower energy valence band compared to the core, resulting in the confinement of both electrons and holes in the core. Type II quantum dots (e.g., CdTe / CdSe, CdSe / ZnTe) have shells with either lower or higher energy conduction and valence bands compared to the core. Therefore, the movement of electrons and holes is confined to one dimension. Exciton radiative recombination at the core-shell interface produces type II emission. Type II quantum dots behave as indirect semiconductors near the band edge and therefore have absorption tails in the red and near-infrared. Alloy semiconductor quantum dots (CdSeTe) can also be used, but type I and type II are preferred. The variable alloy composition and internal structure allow for tuning of optical properties without changing the particle size.

[0176] Particularly preferred quantum dots are selected from CdSe / ZnS core / shell quantum dots, CdTe / CdSe core / shell quantum dots, CdSe / ZnTe core / shell quantum dots, and alloy semiconductor quantum dots (e.g., CdSeTe). More preferably, the diameter of the quantum dots is less than about 10 nm, even more preferably about 2 nm to about 5.5 nm, and most preferably about 1.5 nm to about 4.5 nm. If different color emission is required to create multiple sensors (multiple detection), this can be achieved by changing the size of the quantum dot core that produces different emission wavelengths. As discussed above regarding particles, quantum dots can be stable or unstable. Preferred ligands for stabilizing quantum dots are resorcinol aromatics.

[0177] In the graphene biosensor of this disclosure, advantageously, the graphene core particles act as quenchers for the signal of the detectable portion, eliminating the need to separately add co-connected quencher particles to the sensor (e.g., directly connected to the carrier particles). Therefore, unlike other sensors, the particles (detectable portions) connected to the graphene core particles interact with the graphene core particle carrier particles, but are typically selected and arranged (e.g., close to each other and the carrier particles) such that they do not interact with each other and interact only with the graphene core particles in terms of sensing functionality. (See, for example, the characteristics of peptides connected to graphene particles described and illustrated in the Examples section).

[0178] Therefore, in some embodiments described herein, the core particle and the biosensor each comprise a graphene core particle, wherein at least one detectable portion is connected to the original graphene nanosheet particle via a peptide bond. In this configuration, the detectable portion is at a quenching distance relative to the graphene, and the detectable signal of the detectable portion is quenched and weakened or undetectable due to its proximity to the central graphene nanosheet particle. The peptide bond contains a recognition sequence specific to the target biomarker, such that if the target biomarker is present in the test sample, it will interact with the peptide bond (and typically cleave or modify the configuration of the peptide bond), thereby placing the detectable portion at an emission distance in which quenching of the graphene particle does not occur (e.g., by releasing the detectable portion or by modifying the distance by changing the relevant configuration). Once released by a configurational change or the emission distance is reached, the detectable portion is no longer quenched by the central graphene nanosheet particle, and its signal (or a change in its signal) can be detected, thus indicating the presence and / or activity of the target biomarker in the sample.

[0179] In most embodiments described herein, in the graphene biosensor of this disclosure, the diameter of the connected polymer layer plus the co-connected common sequence does not exceed 20 nm to allow the detectable portion to be placed at a quenching distance relative to the graphene particle, wherein the graphene quenches the signal of the detectable portion (see the characteristics of the polymer described and illustrated in the Examples section).

[0180] Therefore, in some embodiments, the thickness of the coating material in the graphene core particles and / or biosensors of this disclosure is equal to or less than 18 nm to allow a minimum of 2 nm for the connected peptide sequences characterized as targeting capabilities.

[0181] In some embodiments, as a person skilled in the art will understand after reading this disclosure, the thickness of the coating material in the graphene core particles and / or biosensors of this disclosure is 8 to 17 nm, 2 to 18 nm, or 5 to 15 nm, depending on the size of the detectable component connected to or potentially connected to the coating material.

[0182] In some embodiments described herein, the permissible ratio of peptide length (A) and polymer layer thickness (B) in the graphene biosensor for protease activity detection can be detected using the procedure illustrated in Example 31.

[0183] In some embodiments, the target biomarker is post-translational modified, with a layer thickness approaching 20 nm before binding to its target or post-translational modification. Both events elongate the peptide sequence, allowing the detectable portion to be located at an emission distance from which the graphene particles do not quench the detectable portion, thereby resulting in enhanced fluorescence from the chain luciferase.

[0184] It has been unexpectedly found that the biosensors of this disclosure not only possess high stability in the detection of relevant signals but also high reproducibility in the detection of relevant signals, particularly in some embodiments in which the graphene layer comprises 2 to 100 sheets of pristine graphene, more preferably fractal pristine graphene, and is coated with a coating material covering at least 90%, preferably at least 99%, and more preferably up to 100% of the graphene particles. In those embodiments, the target biomarker can be detected with high reproducibility, and particularly with a coefficient of variation of the signal detected between different preparation groups or batches of less than 10%, wherein the signal is the amount of fluorescence in the background of each batch of the sensor.

[0185] In some embodiments described herein, the reproducibility of the nanobiosensor is determined by its ability to reproduce the measurement conditions of a defined biological sample, which can be expressed in terms of the coefficient of variation.

[0186] Therefore, the high reproducibility of the biosensors of this disclosure allows those skilled in the art to formulate core particles modified to be chemically stable in aqueous solutions and connected to a modified sensor portion according to this disclosure in separate components to generate the same amount of signal from a single biological sample. For this to be valuable for measuring biological processes, the amount of signal released by the defined biological sample must be reproducible.

[0187] In some embodiments of the graphene biosensors described herein, the graphene layer comprises 2 to 100 sheets of pristine graphene, the graphene layer being coated with a coating material at least 90%, and it is desired that differences between different preparation groups or batches of sensors will show signals with a coefficient of variation of less than 10% for each build.

[0188] In particular, for the purpose of reproducibility testing, the amount of sensor added to the reaction is preferably determined using absorbance rather than quality-dependent measurement. Specifically, the particle concentration can be determined by absorbance measurement using a standard curve with R² > 0.99. Under these conditions, the particle concentration can be adjusted to CV% < 4%.

[0189] In some embodiments described herein, wherein the graphene layer coated with a coating material comprising at least 90% of a particle suspension having an average particle size of 10 nm to 5000 nm, preferably 150 to 500 nm, as determined using scanning electron microscopy and digital light scattering measurements, reproducibility can also be detected based on the stability of the aqueous suspension containing a sensor, which is assessed by evaluating the colloidal stability at OD650 or OD270 over a set period of time. In some embodiments where the particle size is 10 nm to 5000 nm, and preferably 150 to 500 nm, the average reduction at OD650 between measurements is 24% or less, and in some preferred embodiments, it is typically 10% or less.

[0190] In those embodiments, the sensor is constructed to allow formulations in which the sensor remains in a homogeneous suspension in aqueous solution. This feature allows for uniform particle distribution and subsequent interaction with rare, specific biomarkers present in biological samples at concentrations as low as femtoseconds. Further support for stability is achieved by modifying several layers of graphene with surface modifications that mask the non-polar properties of the graphene surface. These modifications introduce charges that allow the particles to remain suspended in an aqueous environment. Polar modifications are required to support the preparation of the sensor in a buffer solution of at least 1 mg / mL. With a stable solution of at least 1 mg / mL, all sensors can be mixed at functional concentrations in samples ranging from 5 to 500 µg / mL. Preferably, the sensor concentration is maintained at 25, or 30, or 50, or 100, or 200, or 300 μg / mL. The optimal functional concentration for each sensor can be determined based on specific components and associated characteristics. The combined effect of introducing size and charge modifications is to produce a solution that maintains a stable suspension of the particles, such that the absorbance of the graphene nanobiosensors remains unchanged over a 1-hour period at room temperature. The stability of the solution was assessed by evaluating the colloidal stability at OD650 or OD270 at 60-minute intervals at room temperature. The graphene sensor showed a change of -20% to -26% at OD650. The absorbance of the graphene sensor at OD270 showed a change of -17% to -21%. Therefore, as those skilled in the art will understand, the colloidal suspension of the graphene sensor remains suspended more effectively than other particles known in the art.

[0191] In some preferred embodiments, the graphene particles are 900 nm or smaller, preferably 350 nm or smaller, more preferably 250 nm or smaller, and can be used to detect target biomarkers present at concentrations as low as femtoliters.

[0192] It was also unexpectedly discovered that the biosensor of this disclosure not only has high stability and high reproducibility of signal detection, but also has a high signal-to-noise ratio, especially in some embodiments in which the graphene layer comprises 2 to 100 sheets of pristine graphene, more preferably fractal pristine graphene, at least 90%, preferably 100%, of the surface of the graphene particles is coated with a coating material to minimize interference to fluorescence readings caused by the adsorption and quenching of previously cut detectable portions (e.g., fluorescent dyes) on the uncoated graphene surface.

[0193] Specifically, the biosensor having the above-described features provides a signal-to-noise ratio (S:N) performance of at least 25 or higher in aqueous solution. In experiments conducted using exemplary biosensors having the above-described features, the relationship between the biosensor and the sample cultured with the sensor at time 0 was examined, and the relative fluorescence index (RFI) or sample RFU / measured control RFU averaged 4.3 + / - 1.9. Furthermore, biosensors maintained in aqueous solution for up to 90 minutes showed no significant change in background signal, and when measuring different batches of biosensors, the average integrated S:N of the multiple sensors increased from 25 to 46, to 159, and to 312. Therefore, evaluating the integrated S:N of the signal-to-noise ratio at both t=0 and t=60 indicates a 3-fold to 13-fold increase in sensitivity to defined stimuli when the size of the graphene biosensor and solvent contamination are stable (see Representative Example 44 and corresponding Figures 59 and 60). As technicians will understand, the S:N performance depends on the sensor.

[0194] In particular, in those experiments conducted using the exemplary nanobiosensors described herein, as those skilled in the art will understand, an average improvement of 25-fold was measured for four different sensors, ranging from 8-fold to 50-fold (see Figures 20 and 59 in Example 17, Batch 1 formulation). The same four sensors were improved to an average SN of 312 (see Batch 4 of Examples 40 and 43), with an S:N range of 109-fold to 623-fold. In contrast, for the same four sensors measured simultaneously using the same buffer and sample, the average SN of the iron particle sensor was 20 (range 6 to 42) (see Example 44).

[0195] In some embodiments, the biosensor may have different sensor lengths and configurations in the above-described configuration. For example, in some embodiments, the length of the peptide chain may exceed 50 amino acids. This is possible in all cases, for example, when the reverse bending of the peptide chain is envisioned to place the detectable portion at the emission distance.

[0196] In some embodiments, peptide bonds may be indirectly connected to the coating or graphene surface and / or detectable portion of the graphene core particles of this disclosure via portions also referred to herein as spacer regions or linkers. For example, exemplary peptide bonds in Table 1 may include a spacer region GAG preceding the N-terminus of each sequence, and a spacer region AG for the C-terminus. As those skilled in the art will understand, the selection of appropriate spacer regions in the graphene biosensors of this disclosure may be based on the target biomarker to be detected and the quenching and emission distances of the specific biosensor (see Example 9).

[0197] In some embodiments, the peptide bond is an oligopeptide with a length of 5 ± 0.5 nm containing a 3-amino acid spacer region (GAG), a 4+4 amino acid common sequence, and a 2 to 3-amino acid spacer region (G)AG. In some embodiments, the layer size is 2 nm (functionalized graphene) + 12 nm (coating material, e.g., PEI) = 14 nm. 14 nm + 5 nm containing the spacer region peptide bond = 19 nm < 20 nm.

[0198] In some embodiments, graphene-based biosensors are configured for detection methods based on surface plasmaonic resonance and FRET between graphene carrier particles and the detectable portion. FRET describes the energy transfer between two particles. Surface plasmaonic resonance is used to excite the particles. The donor particle, initially in its excited state, transfers this energy to the adjacent acceptor particle via nonradiative dipole-dipole coupling. In short, when the detectable particle is bound to an oligopeptide in its initial state, the first emission is observed upon excitation of the donor particle. Once the target biomarker binds and / or cleaves its recognition sequence, a change in FRET is observed, and the emission spectrum changes. Generally, once the distance between the two particles is altered by the biomarker (by cleaving or elongating peptide bonds), light is emitted from the detectable particle, and a significant blue shift in absorption and emission is observed. This is because the distance between the two particles increases. In some embodiments, the signal intensity from the detectable particle increases with the increase in the distance between the two particles.

[0199] In the biosensors described herein, given the photophysical properties of graphene, as those skilled in the art will understand, plasma quenching is dominantly opposite to that of other nanomaterials characterized by plasma excitation and quenching superposition.

[0200] Generally, the excitation of a biosensor is directed towards particles with higher energy states (e.g., donor particles). Since the absorption spectrum of graphene (and carboxygraphene) varies only slightly between the ultraviolet and near-infrared, such biosensors can operate over a wide optical region. Excitation of the detectable particle can advantageously be performed at about 300 nm to about 1500 nm. Excitation is typically performed using an energy source of appropriate wavelength selected from tungsten lamps, LEDs, lasers, and / or bioluminescence (e.g., luciferase, kidney, green fluorescent protein). Changes in particle absorption and / or emission upon peptide binding, cleavage, or chemical modification of oligopeptide bonds will be observed over a period of about 1 minute to about 120 minutes, preferably about 1 minute to about 60 minutes, and in some cases from about 1 minute to about 30 minutes, depending on the activity of the biomarker. In practice, the assay can first be performed by calibrating a specific biosensor using control samples with and without known concentrations of the target biomarker.

[0201] Specifically, the original graphene nanosheet is located within the core particles described herein, and is connected by linking the detectable portion to the chemically transformed surface of the core particles described herein. In some embodiments in which the core particles comprise a layer of organic and / or inorganic molecules, the chemically transformed surface is coated with a layer of organic and / or inorganic molecules, and is connected to the layer of organic and / or inorganic molecules via peptide bonds.

[0202] In some preferred embodiments, the original graphene nanosheets are dispersed prior to bonding (e.g., by ultrasonic oscillation) and bonding is performed on the dispersed original graphene nanosheets.

[0203] In some preferred embodiments, dispersion is performed using graphene particles containing fractal graphene. In those embodiments, dispersion may be desirable to reduce the characterization of larger fractals entering the prepared graphene swarm. In some embodiments where the target biomarker is detected at femtomolar concentrations, the swarm size of the particles is typically 10 to 5000 nm, preferably 50 to 500 nm, to support homogeneity and stability of the aqueous solution of the particles. The use of ultrasonic oscillation treatment introduces energy into the system to break the electrostatic bonds and van der Waals forces that hold the fractals together. Once broken, the coating reaction (e.g., polymerization) captures the swarm as shown. Ultrasonic oscillation treatment represents a mechanism for introducing energy into the system, some of which will be used to break larger fractals. This will continue to reach equilibrium depending on the power of the ultrasonic oscillation treatment. Introducing 2 to 40 kJ or 10 to 20 kJ or more preferably 12 to 15 kJ using a microprobe ultrasonic oscillator in 200 mL of a solution of several layers of graphene in DMF produces a suitable swarm. Alternatively, the size of the fractal population can be modified by dispersion using a high-shear homogenizer. Alternatively, energy could be provided by microwaves to disrupt larger fractals, or by extrusion under high pressure. These methods use mechanical methods to disperse large fractals to reduce the overall population size. Alternatively, size selection techniques such as filtration or size grading, or sedimentation under centrifugation, are expected to be used to select populations representing 50 to 500 nm. Some examples include reference probe ultrasonic oscillation treatment (see Example 22 and related Figures 27 and 29). The effective endpoint is determined by measuring the final population size after dispersion treatment by any method such as TEM or light scattering. When the dispersion step is included, absorbance efficiencies >1.0, >2.0, and >3.0 were observed using absorbance efficiency measured by OD270 / mg / ml. (See Example 40 and Table Figure 55 for relevant information representing efficiencies of some preferred embodiments).

[0204] In some embodiments described herein, the graphene particles of this disclosure are functionalized to provide a functional group FGg, which is selected to allow the connection of a corresponding functional group FGcg of the coating material. The connection of the coating material by the combination of the groups FGg and FGcg is preferably carried out after the functionalized graphene particles are dispersed, resulting in the core graphene particles providing a functional group FGcp, which is configured to bind a corresponding functional group FGp provided in the peptide bond.

[0205] In some embodiments, based on appropriate coupling chemistry illustrated in schemes (I) to (IX), the following schemes for synthesizing biosensors are described. Wherein A is a reagent configured to functionalize graphene, CM is a coating material in the sense of this disclosure, P is a peptide bond in the sense of this disclosure, and DM is a detectable portion.

[0206] As shown in Scheme X, graphene of formula (Xa) reacts with reagent FG1-A-FGg, wherein FG1 is a reactive functional group that reacts with or causes to react with graphene, and FGg is a functional group capable of chemically coupling with a coating material, and A is a chemical moiety of a C2 to C4 or C5 to C12 substituted or unsubstituted straight-chain or branched alkyl group, wherein one to four carbon atoms may be replaced by one or more heteroatoms selected from O, S, or NH. Exemplary FG1 includes, but is not limited to, -N3 (azide) that decomposes into a nitride under heating conditions, and maleimide that is a dienophile. Exemplary FGg includes -COOH or NH2.

[0207] The functionalized graphene of formula (Xb) reacts with the functionalized coating material FGcg-CM-FGcp, wherein FGcg is a functional group capable of reacting with FGg on the functionalized graphene. Therefore, when FGg is -COOH, an exemplary FGcg can be NH2 to form an amide bond with FGg.

[0208] The coated graphene of formula (Xc) comprises the functional group FGcp, wherein FGcp is a functional group capable of reacting with FGpc on the functionalized peptide FGpc-P-FGpd. Similarly, exemplary FGcp comprises -COOH or NH2. Thus, when FGcp is -COOH, the exemplary corresponding Fpc may be NH2 to form an amide bond with FGcp.

[0209] The peptide-derived graphene of formula (Xd) comprises the functional group FGpd, wherein FGpd is a functional group capable of reacting with FGdp on the functionalized detection portion FGdp-DM. Similarly, exemplary FGdp comprises -COOH or NH2. Thus, when FGdp is -COOH, the exemplary corresponding Fpd could be NH2 to form an amide bond with FGdp.

[0210] It should be understood that any pair of coupled functional groups described herein may be implemented together with any other coupled functional group, as illustrated in schemes (I) to (IX) described herein. Thus, when one functional group is an amine-NH2, the other functional group may be NHS (Scheme V) or ethylene oxide (Scheme IX), in addition to a carboxylic acid.

[0211] In some embodiments described herein, the assembly of a preferably dispersed coating on particles can be performed by first attaching the coating to the surface of the graphene particles to provide coated particles with coating functional groups FGcp, such as amides, amines, esters, ethers, thioesters, or thioester bonds, and then linking peptide bonds to the functional groups FGcp via the corresponding functional groups FGp of the peptide linker. In some embodiments, the linker is first attached to the polymer and then to the graphene. As those skilled in the art will understand, the linking can be performed directly or indirectly and can be carried out by covalent or non-covalent bonding, depending on the corresponding functional groups.

[0212] In some embodiments, the peptide bonds of this disclosure can be linked by spacer regions configured to directly connect to graphene, such as

[28] (targeting the edge of the graphene) and

[28] (adsorbed onto a flat graphene surface) to anchor any peptide sequence to the original graphene.

[0213] In some embodiments, the peptide bonds of this disclosure may be linked to terminal cysteine, which is oxidized to a sulfur radical linked to graphene.

[0214] In some embodiments, non-natural amino acids may be chained to the N-terminus of the peptide sequence for linkage (all chemical reactions are as described above).

[0215] In some embodiments, the coating material may comprise oligopolyvinyl groups <20 nm in diameter, star-shaped dendritic molecules <20 nm in diameter, and block copolymers. In some embodiments, when the coating material may comprise the block copolymer being used, a hydrophobic polymer (e.g., polystyrene, polyisobutylene, polyvinyl chloride, polyethylene, polypropylene, and other polymers identifiable by a person skilled in the art) may be a component of the block copolymer. When a negatively charged polymer (e.g., polystyrene-sulfonic acid) is used in the polystyrene block copolymer, hydrophobicity and electrostatic attraction are combined. In those embodiments, the hydrophobic-hydrophilic block copolymer may be dissolved in a solvent (e.g., dimethylformamide (DMF)) that also disperses graphene well. Under continuous acoustic treatment, H2O is added to the mixture, forcing the hydrophobic ends of the block copolymer to bind to the graphene. This supramolecular bonding occurs spontaneously in the dispersion due to the increased hydrophobicity resulting from the continuous addition of H2O. Therefore, this bonding principle is expected to work reliably as long as the sensor is used in an aqueous solvent.

[0216] Based on the characteristics of the graphene biosensor in aqueous solution in the sense of this disclosure, it was also found that the graphene biosensor is unexpectedly stable and can provide highly reproducible detection of target biomarkers in aqueous samples.

[0217] As used herein, the term “sample” means a limited quantity of something, indicating a larger quantity of that something, such as a fluid from a sample, such as a biological environment, culture, tissue, commercial recombinant protein, synthetic compound or a portion thereof.

[0218] As used herein, the term "sample" refers to a portion of a substance from the environment used for testing, examination, or research. The environment may include individuals. When used as a noun in the context of detection, diagnosis, and treatment, the term "individual" means a single biological organism, including but not limited to animals, and particularly higher animals, and particularly vertebrates, such as mammals and particularly humans. In these cases, a sample may contain a portion of a tissue, organ, or other biological substance from a organism, such as the urethra, urine, cervix, vagina, rectum, oropharynx, conjunctiva, or any bodily fluid.

[0219] In particular, the biological sample may contain one or more cell lines of any biological lineage as a representative of the total population of similar cells in the sampled individual. Exemplary biological samples include the following: whole venous and arterial blood, plasma, serum, dried blood spots, cerebrospinal fluid, lumbar puncture, nasal secretions, sinus cleaning fluid, tears, corneal scrapings, saliva, sputum or expectorate, bronchoscopic secretions, tracheal aspirates, endotracheal aspirates, bronchoalveolar lavage fluid, vomitus, endoscopic biopsy, colonoscopy biopsy, bile, vaginal fluid and secretions, endometrial fluid and secretions, urethral fluid and secretions, mucosal secretions, synovial fluid, ascites, peritoneal lavage fluid, tympanic membrane aspirate, urine, clean-retained midstream urine, catheterized urine, suprapubic aspirate, kidney stones, prostatic secretions, feces, mucus, pus, wound drainage, skin scrapings, skin scissors and skin biopsies, hair, nail clippings, buccal tissue, bone marrow biopsy, solid organ biopsy, surgical samples, solid organ tissue, cadaver or tumor cells, and others that can be identified by a technician. Biological samples can be obtained using aseptic or non-aseptic techniques, depending on the sample type and as can be identified by those skilled in the art. Some biological samples can be obtained by contacting a swab with a human body surface and removing some substance from said surface; some examples include pharyngeal swabs, nasal swabs, nasopharyngeal swabs, oropharyngeal swabs, buccal or oral swabs, urethral swabs, vaginal swabs, cervical swabs, genital swabs, anal swabs, rectal swabs, conjunctival swabs, skin swabs, and any wound swabs. Depending on the type of biological sample and the intended analysis, biological samples can be used fresh for sample preparation and analysis, or they can be fixed using a fixative.

[0220] In the methods and systems described herein, samples may be divided into two or more portions and / or processed to enrich target biomarkers and / or remove selected substances to improve detection quality. Therefore, the term "sample" as used herein includes samples that have been chemically treated or stored, preserved, or further analyzed.

[0221] The sample used in conjunction with the graphene core particles and the associated graphene biosensor composition method and system is an aqueous sample.

[0222] In the sense of this disclosure, an "aqueous sample" is a sample in which water is a solvent or a major component. In particular, aqueous solutions typically contain a buffer component at a concentration that is able to maintain a defined pH during the experiment in the presence of a sensor and a sample.

[0223] In the embodiments described herein, it has been found that graphene biosensors having 2 to 100 original graphene sheets, preferably fractal graphene, coated with at least 90% of the coating material, are unexpectedly stable in aqueous samples and are able to provide detection characterized by high reproducibility and signal-to-noise ratio, particularly when having a size of 10 nm to 5000 nm and preferably 150 to 500 nm.

[0224] In particular, in some embodiments, sensors incorporating these characteristics of the present disclosure exhibit absorbance reproducing the sensor concentration (independent batches of biosensor solutions with the same characteristics) with a coefficient of variation (CV) < 3%. This CV is achieved by having a solution containing sensors that remain uniformly distributed in a defined reaction. The nanosensors are prepared using a method including a dispersion step (e.g., using ultrasonic oscillation treatment) and solvent removal, resulting in a sensor solution that maintains colloidal stability with less than 15% sedimentation over a 60-minute interval. In particular, in some embodiments where the particles are provided by a method including a particle dispersion step and a solvent removal step, sedimentation of 9% to 10% is observed.

[0225] In some embodiments, the reproducibility of the sensor of this disclosure can also be detected by measuring the reproducibility estimates of both the control and the sample, since these two values ​​are divided to estimate S:N. The sensor of this disclosure shows a batch-to-batch CV% < 20%. In this regard, the biosensor of this disclosure having the above-described combination of characteristics can provide 3% to 10% batch-to-batch CV%. Ideally, the batch-to-batch CV% will be < 10%. This is evaluated by preparing independent batches of the sensor and comparing the SN (SM / AC) under matched conditions with a standard stimulus. (See Example 22 and associated Figure 27, and Example 44 and associated Figure 59).

[0226] In some embodiments, the S:N of the graphene biosensor of this disclosure can be determined by maintaining a low, stable measurement control reading. For example, for biosensors evaluated on VarioSkan Lux, AC is 0.01 to 0.1. The signal depends on the sensor and the stimulus. The sample signal of any given sensor using the same stimulus can be increased by 15-fold by solidifying the sensor with a dispersed solvent. Furthermore, incremental addition of the dispersion (average SN increase of 6.8-fold) and removal of the dispersion and solvent (average SN increase of 15-fold) indicate the combined use of the core particles (see Example 44 and related Figure 59), (1) dispersion to an absorbance efficiency of >3.0 OD270 / mg / ml using acoustic treatment or equivalent (see Example 40 and related Figure 55), and (2) reduction of the amount of solvent so that the total weight loss associated with this S:N performance as measured by TGA is less than 5% (wt%), possibly less than 2% (see Example 21 and related Figure 25).

[0227] In some embodiments, reproducibility and signal-to-noise ratio can be improved by making appropriate buffer selection. Buffer selection can be made based on a Good list reflecting the most suitable pH target for biomarker detection. As those skilled in the art will understand after reading this disclosure, the addition of a monobasic salt improves S:N performance to the extent that the concentration of the added salt matters. Monobasic salts reduce assay controls and produce an improved S:N ratio relative to the sample. The inclusion of a monobasic salt reduces assay controls on VarioSkan Lux by an average of 0.01 to 0.1 RFU. Overall, the effect of buffers on all tested sensors results in an average S:N improvement of 4-fold (ranging from 2.5-fold to 6.7-fold) (see Example 42 and the associated Figure 57). Some proteases require buffer cofactors, such as divalent cations. In some preferred embodiments of protease detection, the buffer contains 10 μM of divalent calcium, magnesium, and zinc ions. The test formulation is concentrated at pH 7.2, which is the physiologically relevant pH used in all sensors. However, the tumor environment and intracellular compartments typically reflect a pH below 7.0. Therefore, pH is not necessarily a condition, except when the reaction chamber is shared, as in multiple reactions. Some enzymes require acidic conditions, and pH 7.0 and pH 6.0 and 6.4 and 6.8 are expected to support enzymes such as cathepsins or enzymes derived from inflammatory environments. As those skilled in the art will understand, receptor-ligand interactions are preferably measured under physiological conditions; therefore, 155 mM and pH 7.2 are widely supported conditions for many sensor formulations.

[0228] In some embodiments, the graphene biosensor of this disclosure includes multiple detectable components, each comprising a detectable portion and a recognition sequence specific to the target biomarker.

[0229] In some of those embodiments, the recognition sequences of the detectable components in a variety of detectable components are configured to specifically recognize different target biomarkers.

[0230] Therefore, in those embodiments, the graphene biosensor of this disclosure is configured for multiple detection and to simultaneously measure multiple analytes in a single experiment.

[0231] In particular, the terminology used herein refers to several embodiments in which two or more target biomarkers can be detected in the same reaction using the same reagent mixture.

[0232] Among the multiple target-specific detectable components of a nanobiosensor, the excitation spectrum of each detectable portion has minimal overlap with the emission spectrum of another detectable portion of the multiple target-specific detectable components. Therefore, in those embodiments, each detectable portion is assigned to a specific biomarker that has minimal or no overlap between the excitation and emission spectra of the detectable portions of the multiple detectable components, so as to minimize the FRET effect that distorts the readings between the detectable portions.

[0233] In a graphene sensor configured for multiple detection, given the characteristics and features of the graphene sensor, the multiplexing capability of a sensor having 2 to 100 graphene sheets and a coating covering at least 90% of the graphene surface is 5 or more peptide sequences + detectable portions, possibly up to 10 in the ultraviolet / visible spectrum and up to 50 in the entire electromagnetic spectrum.

[0234] In some embodiments of the graphene biosensors described herein, a detectable component comprising peptides and detectable portions (e.g., luciferases) may be included in a multiplex biosensor at concentrations up to 1.0 ± 0.25 × 10⁻³ mol / g. In some embodiments where the fastest signal enhancement is desired in the femto-micromolar concentration range of the target biomarker (e.g., protease), as will be understood by those skilled in the art upon reading this disclosure, the concentration of the detectable component may be 1.6 ± 0.2 × 10⁻⁵ mol / g graphene-nano biosensors.

[0235] Therefore, in some of the embodiments in which the biosensor is considered to be a multiplex biosensor, the concentration of the detectable component (peptide sequence + detectable portion) of 1.0 ± 0.25 × 10⁻³ mol / g nanometer biosensor is divided by the number of peptide sequences + detectable portions used for multiple purposes. As those skilled in the art will understand, if the concentrations of each detectable component used are not equal, the sum of the concentrations of all portions of each peptide sequence + detectable portion must be equal to 1.0 ± 0.25 × 10⁻³ mol / g nanometer biosensor.

[0236] Additionally, in some embodiments where the biosensor is a multiplex biosensor, given that the preferred concentration of the chain peptide sequence + detectable portion is 1.6 ± 0.2 × 10⁻⁵ mol / g nanometer biosensor to obtain the fastest signal enhancement in the femto-micromol concentration range of the target biomarker (e.g., protease), this concentration is divided by the number of detectable components for multiple purposes. As those skilled in the art will understand after reading this disclosure, if unequal concentrations are used, the sum of the concentrations of all portions of each peptide sequence + fluorophore must equal 1.6 ± 0.2 × 10⁻⁵ mol / g nanometer biosensor.

[0237] Therefore, in some preferred embodiments of the multiple nanobiosensors described herein, as those skilled in the art will understand upon reading this disclosure, the concentration of the sum of all peptides and luciferases used may be from 1.0 ± 0.25 × 10⁻³ mol / g to 1.0 ± 0.25 × 10⁻⁷ mol / g, preferably from 1.0 ± 0.25 × 10⁻⁴ mol / g to 1.0 ± 0.25 × 10⁻⁶ mol / g, and even more preferably from 1.0 ± 0.25 × 10⁻⁵ mol / g to 2.0 ± 0.25 × 10⁻⁵ mol / g.

[0238] Similar multiple capabilities can be achieved by using compositions comprising multiple biosensors of this disclosure, each biosensor providing a detectable component specific to a different target biomarker. In those embodiments, different assemblies of sensors can be mixed such that, as part of the reaction, each sensor can still produce a signal greater than the background signal limited to that of a single sensor. In this context, a biosensor specific to the target biomarker can be mixed with 1, or 2, or 3, or 4, or 5, or 6, or 7, or 10 or 20 different sensors and still produce a signal greater than the background. When expressed as a 1:20 ratio, the multiple sensors can be detected at 5% to 7% of the expected value. Thus, the multiple sensors can be detected in one of 20, 10, 4, or 2 multiple sensors.

[0239] In some embodiments, multiplex detection of the target biomarker can be combined with the detection of conditions associated with multiple target biomarkers. For example, the sensor of this disclosure can be used for multiplex detection of MMP-9, CTS-K, MMP-2, and ARG to determine the presence or absence of lung cancer. In another example, the sensor of this disclosure can be used for multiplex detection of arginase IL-13 to detect anti-inflammatory events and conditions, as well as IL-6, GCSF, MIP-1, and MCP-1 to detect and diagnose pro-inflammatory events and conditions in an individual. Exemplary process for preparing multiplicity.

[0240] In some embodiments, one or more graphene core particles or graphene biosensors described herein are included in the composition along with suitable additives. The term "additive" as used herein refers to any of a variety of media commonly used as solvents, carriers, binders, or diluents in the graphene biosensors described herein, which are included as active ingredients in the composition. In particular, compositions containing one or more oximes may be used in one of the methods or systems described herein.

[0241] In particular, in some embodiments, for storage and / or use, the biosensor is typically dispersed into a pharmaceutically acceptable solvent system, which can be any type of suitable diluent, excipient, carrier, etc. The term "pharmaceutically acceptable" as used herein means that it is not biologically or otherwise undesirable because it can be applied to the article without excessive toxicity, irritation, or allergic response, and does not cause unacceptable biological effects or interact harmfully with any other component of the composition comprising it.

[0242] In some embodiments, a pharmaceutically acceptable carrier may be selected to minimize any degradation of the biosensor components, as is known to those skilled in the art, and to minimize any adverse side effects of the article (for in vivo administration), as is known to those skilled in the art. Some exemplary carriers include non-aqueous solutions such as ether, DMF, DMSO, etc. The biosensor is stable at room temperature and does not deteriorate even at high temperatures up to 100°C, and can be stored and transported without special handling requirements (e.g., without freezing).

[0243] Pharmaceutically acceptable ingredients include those suitable for veterinary use as well as for human use, and will depend on the route of administration. For example, compositions suitable for administration by injection are typically solutions in sterile isotropic aqueous buffers. Some exemplary carriers include aqueous solutions such as normal (n.) saline (about 0.9% NaCl), phosphate buffered saline (PBS), sterile water / distilled water (DAW), or other acceptable carriers as will be understood by a person skilled in the art.

[0244] Based on the information disclosed herein, graphene carrier particles are stable over an indeterminate amount of time. Peptide bonds are also stable over long periods (years). It should be understood that specific compounds selected for the detectable portion may reduce the overall storage stability of the biosensor, and specific sensors may be stored under more careful conditions (e.g., under argon) to preserve the fragile detectable portion for a longer period, if desired.

[0245] It should also be understood that biosensors can be fabricated in a sequential stage, where graphene carrier particles can be stored until use, at which point peptide bonds and detectable portions are added (e.g., added directly before shipment to the end user or added by the end user). Similarly, graphene carrier particles can be pre-conjugated with selected peptide bonds and stored without the need for connected detectable portions, which can be added later, closer to the time of use. Notably, graphene carrier particles have a higher tolerance to mislabeling, where detectable portions accidentally connected directly to the graphene particle itself (and not through bonds) should not interfere with other correctly connected detectable portions. In other words, the signal from a misconnected carrier particle will simply be permanently quenched due to its proximity to the carrier particle. Therefore, biosensors have a higher tolerance threshold during fabrication, which further contributes to their advantages.

[0246] In some embodiments, as those skilled in the art will understand, the compositions described herein may contain components in suitable concentrations other than the graphene core particles and / or biosensors of this disclosure. In particular, compositions in the sense of this disclosure may generally contain salts and buffers (substances capable of maintaining the pH of the solution in which they are dissolved), said buffers being selected to maintain the pH of the solution in which they are dissolved at a controlled pH. In an exemplary preferred embodiment, the composition may contain 5 mM MES having 10 μM divalent cations and 155 mM NaCl, and other combinations of salts and buffers that can be identified by those skilled in the art upon reading this disclosure.

[0247] In one or more embodiments, the biosensor can be pre-dispensed into the wells of a microplate and stored / dispensed for use. Black chimney well microplates are particularly suitable for this purpose. Opaque microplates are preferred for assays to reduce ambient light interference and noise. Methods for the non-invasive detection and quantification of a variety of biomarkers are also described herein. These methods utilize the biosensors described herein and can be performed as part of point-of-care testing resources in healthcare settings, at home, or on-site to evaluate biomarkers indicating a variety of conditions and diseases. Such biomarker-based diagnostics can have numerous applications in precision medicine, particularly for airway diseases, such as in differentiating lung cancer from other lung conditions.

[0248] In some preferred embodiments, the composition may be in lyophilized form. Supporting agents for lyophilization may include trehalose, dextrose, sucrose, glycine, magnesium stearate, microcrystalline cellulose, starch (corn), silicon dioxide / titanium dioxide, stearic acid, sodium glycolate starch, gelatin, talc, sucrose, calcium stearate, povidone, pregelatinized starch, HPMC, OPA products, cross-linked carboxymethyl cellulose, hydroxypropyl cellulose, ethyl cellulose, calcium phosphate, or cross-linked povidone.

[0249] In some embodiments, the graphene biosensor described herein can be used in a method for detecting the activity of a biomarker in an aqueous sample, comprising contacting a biological sample with any biosensor according to the present disclosure to generate a reaction solution; and detecting changes in the reaction solution.

[0250] In some preferred embodiments, the aqueous sample is a biological sample from an individual. In those embodiments, the biological sample can be provided for analysis by collecting and processing the sample from the individual. In those embodiments, the processing of the sample typically produces a processed aqueous sample configured for use in combination with a detection reagent. In particular, a biosensor in the reaction solution can be contacted with the processed biological sample for a period of time sufficient to allow a target biomarker (if present in the sample) to interact with a recognition sequence in the biosensor. Biological samples include, but are not limited to, blood, urine, saliva, tears, sputum, bronchoalveolar lavage fluid, nasal swabs, respiratory condensate, milk, feces, rectal fluid, vaginal fluid, etc. In one aspect, a biological sample is collected from an object and prepared for analysis. Samples can be collected and prepared manually, including, for example, manual pipetting or mixing / diluting of samples, manual protein spot cutting, manual biopsy collection, manual blood drawing, and manual microwell loading. Alternatively, automated processes can be used, including, for example, automated liquid processors, automated protein spot cutters, automated biopsy collection, automated blood collection, and automated microwell loading. It should be understood that biological samples may contain target biomarkers, including various enzymes described herein, which can be used as biomarkers indicating the health status of the object. Pharmaceutically acceptable buffers may be added before, during, or after adding the sample to the reaction solution to adjust the pH to an optimal level based on the target biomarker and the specific detectable fraction used. For example, a weakly basic buffer such as HEPES may be used to adjust the pH of the reaction mixture to a desired range. Furthermore, the pH can be adjusted to fine-tune the reaction. For example, some enzymes (e.g., MMPs and cathepsins) are more active at weakly acidic pH (lower limit about 6.6) when extracted from cancer samples compared to non-cancer samples. Therefore, pH is a parameter that can be optimized within the scope of this disclosure to further enhance the assay output. Cofactors, such as Mg(II), Ca(II), or Zn(II), may also be added to the reaction solution to further enhance the enzyme activity of the assay.

[0251] When the target biomarker is present in a sample, the detectable portion generates a detectable signal (e.g., optical or spectral), such as fluorescence or color change, which can be visually perceived or measured with suitable instruments. A disc reader can be used to detect the measurement results. As explained when bound, the central graphene particle quenches the emission of the detectable portion. In the presence of a protease or other target biomarker, it cleaves the bonds separating the detectable portion from the central particle, or modifies the bonds extending the detectable portion to the adjacent quenched exterior. The detectable particle generates a detectable signal that varies according to its proximity to the carrier particle, which can be detected and correlated with the activity of the target protein. For example, the maximum absorption of TCPP is close to 420 nm, therefore an inexpensive 420 nm LED or any other type of laser diode can be used for excitation of biosensors using TCPP as the detectable portion. It should be understood that the biosensor is not limited to the TCPP-graphene FRET pair illustrated in the examples. Any FRET sensing pair can be used. If other FRET pairs are used, the excitation wavelength and detection device can then be modified accordingly.

[0252] In some implementations, FRET can be performed over the entire electromagnetic spectrum.

[0253] In some implementations, photoacoustic detection (photoacoustic response of multiple fluorophores) can be distinguished.

[0254] In some embodiments, the radioactive isotope can be released from the graphene core and subsequently filtered out.

[0255] In some embodiments, the MRI probe may be released from graphene coated with a ferromagnetic material (iron oxide) or a high-spin d- and f-block metal composite.

[0256] In some embodiments, the EPR probe can be released from graphene coated with a ferromagnetic material (iron oxide) or a high-spin d- and f-block metal composite.

[0257] Assays using biosensors can detect biomarkers down to the sub-femtomer level (<10-15 moles / L), which is at least two orders of magnitude more sensitive to competing technologies (e.g., immunoassays). In fact, as demonstrated in working examples using non-optimized biosensors, limits of detection (LODs) of 10-16 for proteases are possible. A unique advantage of this technique is the ability to measure different biomarkers (e.g., several proteases, cytokines, and kinases) in a single liquid biopsy. In other words, this technique allows for the simultaneous detection of different classes of biomarkers on a single platform. Therefore, in some multiplex embodiments, it is preferable to use multiple different types of detectable fractions, enabling assays to analyze multiple different types of target biomarkers in a single biological sample. Focusing on multiple biomarkers allows for the differentiation of related diseases (e.g., lung cancer vs. other diseases) and the detection of diseases at very early stages. For example, the latter is important for lung cancer detection because cancer survival is significantly increased when detected at stage 0 or 1 compared to stage 3 or 4.

[0258] In particular, in the multiple biosensors described herein, the detectable component is selected to minimize the overlap of the excitation and emission spectra of the co-connected detectable portions (e.g., luciferases). More particularly, in some multiple embodiments of the biosensors described herein, the detectable component is selected to minimize the occurrence of FRET between detectable portions, such as luciferases. In this regard, as those skilled in the art will understand, for example, the selection of a detectable portion such as a BODIPY dye shows particular suitability because it exhibits narrow excitation and emission spectra.

[0259] In some preferred embodiments, the aqueous sample may be heat-treated before and / or during contact with one or more graphene biosensors described herein.

[0260] Particularly in some embodiments, in the absence of a biosensor as described herein, the sample may be heat-treated at a temperature of 40°C to 75°C for 0.5 to 3 hours. Preferably, the sample or serum may be heat-treated at a temperature of 50°C to 60°C for 0.5 to 2 hours. In some embodiments, the sample or serum may be heat-treated at a temperature of 55°C for 1 hour.

[0261] In some embodiments of the method for detecting a target biomarker, detection is performed after heating the test solution, in the presence of a biosensor as described herein. In the presence of a biosensor as described herein, the sample or serum to be tested may be heat-treated at a temperature of 40°C to 75°C for 0.5 to 3 hours. Preferably, in the presence of a biosensor, the sample or serum to be tested may be heat-treated at a temperature of 50°C to 60°C for 0.5 to 2 hours. In some embodiments, in the presence of a biosensor, the sample or serum to be tested may be heat-treated at a temperature of 55°C for 1 hour.

[0262] In some embodiments, the heat treatment may be performed according to the sample heat treatment protocol illustrated in Example 21. The heat treatment may be applied to the sample before, during, or both of the measurement. The effect of the heat treatment is particularly to improve the sample reading, while having no effect on the control. Therefore, the SN is increased due to the enhancement of the specific signal. Temperatures between room temperature and 75°C are effective. Preferably, the sample may be pretreated at 65°C for 5 to 12 minutes, or the measurement may be performed during incubation at 41°C or 45°C.

[0263] In some preferred embodiments, the solution containing the monobasic salt is heated and / or cultured in a buffer solution maintained at pH 7.2. In some preferred embodiments, the culture may be carried out at 45°C.

[0264] In some embodiments, as those skilled in the art will understand, biosensors and / or related combinations may therefore be configured to detect target biomarkers associated with a target condition in an individual.

[0265] For example, in one or more embodiments, the nanosensor can be configured for rapid diagnosis of lung diseases based on protease activity profiles of two or more proteases (e.g., in a group). The common sequence is cleaved by its respective protease through proteolytic cleavage or by a chemical compositional change in a post-translational modification sequence. For example, arginase I+II converts arginine to ornithine without the need for proteolytic cleavage of the oligopeptide. Therefore, as those skilled in the art will understand, the methods described herein are also applicable to use with other nanosensors, such as the nanosensors for detecting post-translational modifications of enzymes described in WO 2016 / 149637. Nanobiosensors and methods can be used to detect targeted biomarkers and thereby diagnose a variety of lung diseases, including lung diseases, cancer, and infections.

[0266] In one aspect, nanobiosensors targeting chronic inflammatory lung diseases (asthma, COPD, fibrosis) target the corresponding shared sequences of proteases and peptide bonds reported in Table 4: Table 4: Common sequences of target protease and peptide bonds Host proteases Shared sequence* SEQ ID NO: Protease 3 (PR3) 95 cathepsin G (CTG) 82 Unless otherwise stated, the sequence also contains N-terminal GAG and C-terminal AG spacers.

[0267] In some embodiments, the nanobiosensor for pulmonary arterial hypertension (PAH) can be configured to target the proteases reported in Table 5 and the corresponding common sequences of peptide bonds: Table 5: Common sequences of target protease and peptide bonds Host proteases Shared sequence* SEQ ID NO: MMP-2 (matrix metalloproteinase-2) 51 MMP-9 55 MMP-14 93 Unless otherwise stated, the sequence also contains N-terminal GAG and C-terminal AG spacers.

[0268] PAH can lead to secondary complications such as COPD (chronic obstructive pulmonary disease), asthma, and chronic bronchitis. PAH involves enhanced activity of three MMPs: MMP-2, MMP-9, and MMP-14 (MT1-MMP-1). MMP-2 and MMP-9 can cleave basement membrane-associated collagen IV, which accelerates pulmonary vascular remodeling. Although MMP-14 is membrane-bound, it can be detected in liquid biopsies (serum, BALF, and potential EBC) in disease states.

[0269] In some embodiments, the nanobiosensor may be designed to detect influenza virus infection, targeting the proteases reported in Table 6 and the corresponding common sequences of peptide bonds. Table 6: Common sequences of target protease and peptide bonds Host proteases Shared sequence* SEQ ID NO: TMPRSS2 (Epitheliasin, transmembrane serine protease 2) 86 Human-like airway trypsin peptidase (HAT) 85 Granulase B 49 Unless otherwise stated, the sequence also contains N-terminal GAG and C-terminal AG spacers.

[0270] In some embodiments, the biosensor of this disclosure can be configured to detect other proteases in the lungs, such as TMPRSS2 (epitheliasin) and human airway trypsin peptidase (HAT), which, under these conditions, promote influenza virus infection of H1N1 (A / Memphis / 14 / 96), H2N9 (A / Mallard / Alberta / 205 / 98), and H3N2 (A / Texas / 6 / 96) after cleavage of hemagglutinin (HA). The viral pathogens are capable of stimulating the expression of proteases in the "influenza-cytokine-protease cycle." The enzymes are expressed as zymogens and subsequently activated by the protease network in the lungs. Granzymes are also known to activate H1N1. Therefore, granzyme B is included in this group. As those skilled in the art will understand, the latter has the advantage of being detectable in the EBC.

[0271] In some embodiments, the nanobiosensor can be configured to detect viral infection targeting the activity of kallikrein in the lungs. Kallikrein (KLKs) is a family of proteases with trypsin-like or chymotrypsin-like specificity, consisting of 15 closely related secretory serine proteases. KLK-2, KLK-3, KLK-4, KLK-5, KLK-6, KLK-12, and KLK-15 are poorly expressed in the lungs of healthy individuals. This expression pattern changes in response to viral infection. In the case of influenza infection, the expression of KLK-1, KLK-2, and KLK-5 is increased, while the expression of KLK-13 and KLK-14 is decreased. KLK-13 is increased in almost all coronavirus (SARS and MERS types) infections. Exemplary common sequences of exemplary kallikrein and associated peptide bonds are reported in Table 7. Table 7: Common sequences of target protease and peptide bonds Host proteases Shared sequence* SEQ ID NO: KLK-1 98 KLK-2 99 KLK-5 100 KLK-12 101 KLK-13 102 KLK-14 103 KLK-15 104 *Unless otherwise stated, the sequence also includes N-terminal GAG and C-terminal AG spacers. **Contains N-terminal GAG and C-terminal GA spacers***Contains N-terminal GAA and C-terminal AG spacers.

[0272] In some embodiments, the biosensor may be configured to detect Streptococcus pneumococcus infection, which targets bacterial proteases, and may: exemplary proteases and corresponding exemplary common sequences of the target biomarkers of Streptococcus pneumococcus are reported in Table 8. Table 8: Common sequences of target protease and peptide bonds bacterial protease consensus sequence SEQ ID NO: Streptococcal IgA1 protease 105 **Includes N-terminal GAG spacer region and C-terminal GAG spacer region

[0273] As those skilled in the art will understand, Streptococcus pneumoniae is the most common bacterial infection of the lungs, and, if not all streptococci, the vast majority express the Streptococcus pneumoniae IgA1 protease. Therefore, this protease is one of the most active known proteases and is present only in cases of streptococcal infection. This will allow for rapid testing (<10 minutes) for bacterial lung infections.

[0274] In one aspect, a nanobiosensor for detecting Mycobacterium tuberculosis infection targets the following proteases. Exemplary proteases and corresponding exemplary shared sequences of associated peptide bonds that are target biomarkers for Mycobacterium tuberculosis are reported in Table 9. Table 9: Common sequences of target protease and peptide bonds bacterial protease consensus sequence SEQ ID NO: Mycoplasma membrane protease MycP1 106 Rv2869c (Mycobacterium tuberculosis) peptidase (RIP-1) 107 **Includes N-terminal GAG spacer region and C-terminal GAG spacer region

[0275] As will be understood by those skilled in the art, other secretory Mycobacterium tuberculosis proteases that can be used as target biomarkers are MycP1 and Rv2869c (Mycobacterium tuberculosis) peptidase (RIP-1), both of which are specific for MtB.

[0276] In some embodiments, the nanobiosensors of this disclosure can be configured to detect chronic obstructive pulmonary disease (COPD). Exemplary common sequences of the target biomarkers for COPD and the associated peptide bonds are reported in Table 10. Table 10: Common sequences of target protease and peptide bonds Host proteases consensus sequence SEQ ID NO: Neutrophil elastase (NE) 87 MMP-7 (Matrix Solubility Factor) 53 MMP-8 89 MMP-12 92 cathepsin G 82 Protease-3 (myeloblastic protease) 96 Unless otherwise stated, the sequence also contains N-terminal GAG and C-terminal AG spacers. ****Contains N-terminal GAP and C-terminal AG spacers. *****Contains N-terminal GAA and C-terminal EG spacers.

[0277] Additional target biomarkers for COPD can be identified and selected based on the fact that endogenous serine protease inhibitors are responsible for lung tissue protection against serine protease in healthy subjects. The lack of hereditary inhibitors is considered the most important cause of COPD, followed by smoking. In the absence of effective inhibitors, uncontrolled proteolysis and subsequent lung damage are observed. The most important proteases used for COPD detection are neutrophil elastase, neutrophil-derived MMP-8, macrophage-derived MMP-12, cathepsin G, and protease 3. These proteases can damage ECM components such as collagen, laminin, fibrils, and elastin. In particular, the cleavage of pulmonary elastin leads to emphysema, which drives the pathophysiology of COPD. MMP-7 degrades the extracellular proteoglycan gyneglignan, which leads to the subsequent release of gyneglignan-bound transforming growth factor-β (TGF-β). Therefore, the co-initiation of all six proteases (NE, MMP-7, -8, -12, CTSG, and protease 3) allows for the detection of COPD.

[0278] In some embodiments, the nanobiosensor may be configured to detect lung cancer using a proteome and to distinguish between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) targets: exemplary proteases and corresponding exemplary common sequences of associated peptide bonds for target biomarkers of SCLC and NSCLC are reported in Table 11. Table 11: Common sequences of target protease and peptide bonds Host proteases consensus sequence SEQ ID NO: MMP-1 88 MMP-2 51 MMP-10 90 MMP-12 91 MMP-15 94 cathepsin B 43 cathepsin H 83 cathepsin L 84 Arginase II 97 Neutrophil elastase 87 Unless otherwise stated, the sequence also contains N-terminal GAG and C-terminal AG spacers. ****** Contains N-terminal GAA and C-terminal AG spacers.

[0279] In some embodiments, as those skilled in the art will understand, the relevant datasets used to select the protease for detecting SCLC and NSCLC vs. non-cancer health conditions, and for distinguishing between SCLC and NSCLC, are available from the NCBI GEO database (see www.ncbi.nlm.nih.gov / geo / at the date of submission of this disclosure). The datasets included in the analysis are from human cancers, and the datasets contain both primary tumor samples and healthy human tissues. Exemplary calculated p-values ​​for SCLC and NSCLC are reported in Table 12 below.

[0280] Table 12: Calculated p-values ​​from NCBI GEO ID ID p-value logFC Gene symbol Gene description Two, SCLC and NSCLC vs. tumor controls NM_002421_at 2.04E-04 0.960344 MMP1 Matrix metalloproteinase 1 (interstitial collagenase) NM_002425_at 1.14E-03 0.921643 MMP10 Matrix metalloproteinase 10 (macrolysin 2) NM_002426_at 3.64E-21 3.321265 MMP12 Matrix metalloproteinase 12 (macrophage elastase) NM_002428_at 5.58E-22 -0.75712 MMP15 Matrix metalloproteinase 15 (membrane-intercalating) NM_004530_at 1.41E-20 -1.9972 MMP2 Matrix metalloproteinase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV collagenase) NM_147783_at 1.33E-06 -0.75068 CTSB cathepsin B NM_004390_at 2.96E-27 -2.71018 CTSH cathepsin H NM_001333_at 1.59E-12 1.221857 CTSL2 cathepsin L2 NM_001172_at 7.34E-18 1.582521 ARG2 Arginase, type II NM_001972_at 2.26E-11 -0.41571 ELA2 elastase 2, neutrophils NSCLC vs. SCLC 204475_at 9.96E-01 -3.84E-03 MMP1 Matrix metalloproteinase 1 205680_at 7.62E-04 2.16 MMP10 Matrix metalloproteinase 10 204580_at 4.87E-02 1.04 MMP12 Matrix metalloproteinase 12 203365_s_at 2.19E-01 -1.58E-01 MMP15 Matrix metalloproteinase 15 201069_at 7.13E-02 -5.65E-01 MMP2 Matrix metalloproteinase 2 200839_s_at 1.27E-02 -6.27E-01 CTSB cathepsin B 202295_s_at 4.80E-05 -1.19 CTSH cathepsin H 202087_s_at 7.48E-02 -4.50E-01 CTSL cathepsin L 203945_at 9.67E-01 -5.25E-03 ARG2 Arginase 2 206871_at 2.71E-01 -7.17E-02 ELANE elastase 2, neutrophils

[0281] Based on these gene expression data, the selected enzyme group should be able to distinguish between SCLC and NSCLC.

[0282] The distinction between SCLC and NSCLC is important because the treatment methods differ for each type of cancer. The main subtypes of NSCLC are adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. These subtypes, which originate from different types of lung cells, are classified as NSCLC because their treatment and prognosis (prospects) are generally similar. A smaller subtype of all lung cancers is SCLC. This subtype is more sensitive to chemotherapy and radiation therapy, but has a higher overall mortality rate.

[0283] Furthermore, similar to pancreatic cancer, NSCLC can be detected in stages 0 and 1 via liquid biopsy because protease characteristics change significantly during the transition from non-cancerous to cancerous. In principle, the same protease / arginase selection can distinguish SCLC from healthy tissue; based on differences in gene expression, MMP10 and cathepsin H can be selected as suitable proteases to differentiate SCLC from NSCLC.

[0284] Similar gene expression analyses can be used to identify proteases overexpressed in other solid tumors such as pancreatic cancer, including the use of databases such as NCBI GEO, Entrez Gene ID, Unigene ID, and Gene Symbol. This strategy enables the selection of candidate enzymes from the human genome that have a high probability of becoming proximal biomarkers for pancreatic cancer.

[0285] These target sequences and detection platforms can be configured into a variety of sensors of this disclosure, including solution-based assays, microfluidic assays, micropore assays, high-throughput assays, etc., including those described in U.S. Patent Nos. 8,969,027, 9,682,155, 9,731,034, and 9,216,154, which are incorporated herein by reference, and those described in co-pending WO 2017 / 165800 (US 2020 / 0300849), filed March 24, 2017.

[0286] In another preferred embodiment, the inflammatory biomarker can be detected in conjunction with the detection of a specific viral, bacterial, or fungal infection. For example, the nanosensor can be prepared using a peptide aptamer targeting capsid B, a recognition sequence for detecting Haemophilus influenzae B, or a recognition sequence targeting Haemophilus influenzae NT or Moraxella catarrhalis. Similarly, a nanosensor for viral infection can be prepared using a protease concordance sequence (or) for HIV detection.

[0287] It should be understood that each of the aforementioned sensors can be designed in alternative configurations, wherein the graphene core particles are linked by modifiable recognition sequences, protease-shared sequences, or post-translational modifiable sequence nt bonds that interact with the target biomarker. In such embodiments, as described above, while the detectable particles remain relatively stationary and attached to the carrier particles, the quencher particles are alternatively cleaved or moved away from the detectable particles and the carrier particles to achieve a change in the distance between the particles. As described, the increase in distance between the graphene core particles and the detectable particles (due to cleavage or elongation of the quencher particle chain) results in a detectable change in the signal from the nanosensor.

[0288] In one or more embodiments, the sensor according to this disclosure can be used to diagnose lower respiratory tract infections by detecting markers associated with infection of infected lower airway epithelial cells. These markers include CCL20, TSLP, and CCL3-L1. As described herein, cytosome contents can be used to determine whether cytosomes originate from the upper or lower airway and also indicate the location within the object where infection or other conditions may originate. The sensor can also be used to assess environmental risks in patients with chronic lung disease. Exposure to airborne pollutants, ozone, particulates, acetaldehyde, acrolein, formaldehyde, tobacco smoke, and other compounds can trigger respiratory inflammation. Detection of markers such as cytokines, proteases, and / or kinases can be used to identify stages of lower respiratory tract inflammation, allowing for personalized environmental assessments. The nanosensor can also be used to measure lower respiratory tract inflammation in the immediate management of inflammation in asthma. Details regarding biomarkers associated with severe asthma and the understanding of protein expression patterns are described in U.S. Patent No. 8,053,199, which is incorporated herein by reference. Currently, anti-inflammatory drugs (corticosteroids, IL-13 antibodies, etc.) are administered and monitored based on symptoms and exacerbations. By testing lower airway markers, such as cytokines, proteases, and / or kinases, patients and caregivers can track inflammation in real time and adjust treatment accordingly. Similarly, nanosensors can be used to measure lower airway remodeling in severe asthma and chronic obstructive pulmonary disease (COPD). Remodeling refers to the process of airway fibrosis, which is associated with a progressive decline in lung function in a subset of patients with severe asthma and COPD. Currently, there are no available treatments or diagnoses. New therapies targeting epigenetic remodeling are currently being developed. This approach will enable the development and approval of remodeling inhibitors for clinical use by detecting and measuring the presence of fibronectin, IL6, or vimentin, which are indicators of progressive airway remodeling.

[0289] In some embodiments, the sensors of this disclosure can be used in conjunction with the detection of opportunistic infections. Opportunistic infections are also important complications in patients undergoing cancer treatment, patients experiencing immunosuppression due to treatment for rheumatoid arthritis or inflammatory bowel disease, or patients with HIV. Pneumocystis pneumonia (PCP) is the most common infection in HIV patients and is currently only diagnosed through invasive bronchoscopy. Invasive aspergillosis is also an opportunistic lung infection that occurs in patients undergoing chemotherapy for leukemia. When fungi invade the airways of patients who are immunosuppressed due to their leukemia treatment, nanosensors can be used to indicate the presence of different host response proteins.

[0290] In some embodiments, the sensors of this disclosure can also be used to monitor transplant rejection or host resistance to graft disease in lung transplantation. In cases where a lung transplant patient rejects the organ, the patient is treated with intensive immunosuppressive therapy, and their physician manages the boundary between excessive immunosuppression (resulting in opportunistic infections) or insufficient immunosuppression. If the transplant patient's immune profile can be monitored in real time, transplant management will be easier. It will also be understood that nanosensors can be used to monitor the response to lung cancer treatment. Mobile biosensing will detect cancer signatures in the airways or bloodstream, such as cytokines, proteases, and / or kinases (e.g., MMPs, EMTs, etc.). This will include free proteins and proteins contained in microparticles (cytosomes).

[0291] In some embodiments, the sensors of this disclosure can be used in conjunction with protein sample collection, processing, spectral analysis, and detection. The embodiments described herein can also be used in conjunction with conventional PCR / RT-PCR, real-time quantitative PCR / RT-PCR, and isothermal PCR / RT-PCR methods to assess bacterial, viral, and fungal infections associated with increased or decreased protein expression levels. Therefore, the assays according to the invention can be used as a standalone technique and / or in conjunction with conventional methods, particularly in risk groups pre-identified as being at risk through genetic testing.

[0292] Generally, detection using the sensors of this disclosure involves contacting a prepared sample with a nanosensor (and typically multiple nanosensors) to generate a reaction mixture. The nanosensor can then be detected or excited using an appropriate energy source. The wavelength used will depend on the particles used in the nanosensor. Changes in the absorption and / or emission of the particles are then detected over a period of time as the target biomarker interacts with the nanosensor (e.g., through binding and extension of a recognition sequence).

[0293] The embodiments described herein also rely on nanosensors integrated with a microfluidic and smart device platform, which can then be read by a simple but robust fluorescent or optical reader. "Microfluidics" refers to the techniques for manipulating, controlling, and / or analyzing small-volume (fluid) samples, typically in the range of microliters (10⁻⁶) to picoliters (10⁻¹²). In one aspect, microfluidic technology is used to introduce rapid, high-throughput sample collection, sample processing, sample spectral analysis, and target detection. In another preferred embodiment, the microfluidic device will be connected to a mobile computing device, such as, but not limited to, a smartphone or tablet. Mobile computing devices include tablets, smartphones, handheld computers, laptops, etc., capable of optically scanning samples and transmitting information to other computing devices. This integrated platform will revolutionize current "conventional" approaches that utilize multiple instruments with cumbersome manual sample handling procedures. In another preferred embodiment, a standalone, battery-powered diagnostic platform with rapid analysis time and multiple capabilities will achieve statistical significance of the measurements, wherein the use of a handheld smartphone-based system that can be used as a diagnostic tool for patients and medical professionals consumes minimal human fluid (<5 μl per sample).

[0294] In some embodiments, assays performed using the biosensors of this disclosure may use a combination of two or more of the aforementioned sensors. Importantly, the nanosensors used according to this disclosure may be designed to include supramolecular recognition sequences, protease-shared sequences, or post-translationally modifiable sequences that induce specific interactions with biomarkers. The term "specific interaction" as used herein is intended to distinguish between interactions between recognition sequences of the sensor and non-specific binding or reactions between molecules, and means that the specific set of target analytes to which the oligopeptide sequences can interact is limited, and in some cases even exclusive, such that none bind to or cleaves any other molecule at a considerable rate.

[0295] The methods of this disclosure can be carried out using appropriate systems comprising a variety of combinations of: layered graphene, organic and / or inorganic molecules, detectable portions, peptide bonds, detectable components, biosensors, reagents for carrying out the chemical reactions described herein, and other components that can be identified by a person skilled in the art.

[0296] An exemplary optical detection technique has been described. However, it should be understood that other analytical methods may be used. Generally, these methods involve exposing a reaction solution (e.g., in a micropore) to an appropriate energy source. The wavelength used will depend on the detectable label used in the biosensor. Both the location / presence and concentration / intensity of the signal from the detectable portion can then be sensed or detected using an appropriate sensing or detection instrument for both quantitative and qualitative evaluation of the detectable portion.

[0297] The systems disclosed herein can be provided as kits of part. In kits of part for performing any of the methods described herein to prepare biosensors, graphene particles may be included alone in the kit, or in the presence of one or more buffers and reagents, for the chemical transformation of the graphene particles. In kits for detecting biomarkers, one or more biosensors described herein may be included alone or in the presence of one or more buffers and reagents for the detection of one or more biomarkers. Additional components that can be identified by a person skilled in the art may also be included.

[0298] In a complete kit, graphene particles, biosensors, and reagents are contained independently in the kit, or may be contained in the composition along with suitable carriers or adjuvants. For example, one or more biosensors may be contained in one or more compositions along with reagents that are also used for detection in one or more suitable compositions.

[0299] Additional components may include markings, reference standards and other components that can be identified by a person skilled in the art after reading this disclosure.

[0300] In some embodiments described herein, the kit components, along with suitable instructions and other necessary reagents, may be provided for carrying out the methods disclosed herein. The kit typically contains the composition in a separate container. Instructions, such as written or audio instructions on paper or electronic support such as magnetic tape, CD-ROM, flash memory drive, or via a Uniform Resource Locator (URL) containing a PDF copy of the instructions for performing the assay, will typically be included in the kit. Depending on the specific method used, the kit may also contain other packaged reagents and materials (i.e., wash buffers, etc.).

[0301] Those skilled in the art, upon reading this disclosure, can identify suitable adjuvants for the composition and further details regarding the general manufacture and packaging of the kit. Examples

[0302] The following embodiments illustrate methods according to this disclosure. However, it should be understood that these embodiments are provided by way of illustration and nothing therein should be considered as a limitation on the overall scope of this disclosure. Example 1: Photochemical Properties of Graphene and Metals

[0303] Graphene contains at least 98% carbon and exhibits minimal reactivity with water or aqueous buffers, as well as with thiol functional groups (such as those provided on proteins and glutathione) in aerated aqueous buffers. These properties are the opposite of those of metal / metal oxide materials.

[0304] Graphene absorbs more UV / V light than graphite in the ultraviolet (UV), visible, and near-infrared (IR) regions.

[28] In particular, stacked graphene exhibits light absorption characteristics across the entire wavelength range from far ultraviolet to mid-infrared (100 to 1500 nm). [6]

[0305] Although direct excitation occurs in the deep / far ultraviolet, surface plasmas of graphene have very strong intensity in the visible light range and cannot effectively excite attached organic fluorescent dyes or nanoparticles. [6]

[0306] In contrast, many nanoparticles (especially Au, Ag, and Fe in Fe / Fe3O4) exhibit strong plasma excitation in the visible light range of the electromagnetic spectrum, which facilitates both excitation and quenching of luciferases. [29, 30] In graphene, plasma excitation in the visible light range of the spectrum can occur via energy transfer from nearby connected fluorescent organic dyes, and is also expected to originate from metallic nanostructures and quantum dots.

[31]

[0307] Due to the photophysical properties of graphene, it is not necessary to integrate the FRET acceptor of each luciferase into the design. This allows for multiplexing and avoids overcrowding on the surface of nanobiosensors, which can significantly slow down nanobiosensors kinetically.

[0308] Due to the photophysical properties of graphene, when graphene is used in FRET pairs, there is no need to integrate FRET acceptors into the design to complement FRET donors (e.g., fluorophores). As those skilled in the art will understand, these properties of graphene allow users to minimize overcrowding on the surface of nanobiosensors, which kinetically slows down nanobiosensors.

[0309] In addition to the above, some forms of graphene, such as exploded graphene and some fractions from flash graphene, have hyperaggregated fractal morphologies, which can be determined by light scattering methods such as SEM (scanning electron microscopy) and light scattering. [18, 19]

[0310] An exemplary SEM (scanning electron microscopy) image of a 7-layer fractal graphene is reported in Figure 1. [8]

[0311] As can be seen from the description in Figure 1, the stacked graphene layers are characterized by fractal sizes ranging from Df = 2.5 ± 0.3 (upper limit) to Df = 1.8 ± 0.4 (lower limit). Generally speaking, any fractal size from Df = 2.9 ± 0.09 to Df = 2.1 ± 0.09 can be achieved, depending on the size and surface modification of the exploded graphene.

[0312] The fractal geometry of exploded graphene leads to enhanced light absorption because light is "trapped" within the structure. In contrast, in flat graphene sheets, light passes through the graphene only once, and reflection and diffraction of light from within the fractal morphology result in multiple absorption events, as those skilled in the art will understand. Example 2: Overview of Biosensors and Fabrication Methods

[0313] Figure 2A provides a schematic diagram of the main components of the biosensor described herein, which relates to some exemplary embodiments in which the graphene is provided by detonation graphene and the coating of the core particles is provided by a polymer coating.

[0314] In the exemplary configuration schematically shown in Figure 2A, nanosheets of detonated graphene aggregates are used to provide the graphene component of the core particles coated with a monolayer polymer coating. A detection component, formed by peptide bonds containing a shared sequence and a detectable portion, is further linked to the core particles via a connection to the polymer coating.

[0315] A biosensor having the structure schematically shown in Figure 2A can be provided using the exemplary process schematically illustrated in Figure 2B. In the schematic diagram of Figure 2B, the graphene surface is functionalized with, for example, multiple carboxylic acid groups and / or coated with a thin polymer monolayer. In the exemplary illustration of Figure 2B, multiple detectable portions can be linked to the same carrier particle via respective oligopeptide bonds. For example, as schematically shown in Figure 2B, two or more different detectable portions can be linked to the same carrier particle via respective oligopeptide bonds, which are specific to their respective target biomarkers.

[0316] Specifically, Figure 2B schematically illustrates the design of the novel biosensor: A: Synthesis of carboxylated graphene; B: Chemical linkage of polyethyleneimine via amide bonds; C: Linkage of the concordant sequence (oligopeptide) to the fluorescent dye (TCPP). TCPP is linked to the N-terminus of the oligopeptide on the resin. After cleavage from the resin and HPLC purification (if desired), the concordant sequence + TCPP is linked to polyethyleneimine via its C-terminus. Due to kinetics, the concordant sequence + TCPP is linked via the C-terminus of the oligopeptide. In the presence of the correct protease, the concordant sequence is cleaved by proteolytic hydrolysis, and the half of the concordant sequence covalently linked to the fluorescent dye TCPP is released (photoconversion).

[0317] Figure 2C shows a further schematic diagram of thin monolayer graphene with overlapping edges that are entangled with each other, or nanosheets consisting of two to three layers or composed of two to three layers that are stacked more orderly.

[0318] Figure 2D shows a schematic diagram of high-density carboxylic acid groups added to the surface of graphene nanoparticles.

[0319] Further details regarding the process involving graphene carboxylation and the bonding of detectable components via a PEI polymer coating are reported in Examples 3 to 9. Example 3: Synthesis of Carboxylated Graphene - General Process

[0320] In order to synthesize the graphene derivative referred to herein as "carboxylated graphene", detonation graphene (0.2 to 0.5) nanoparticles (nanoparticle sheets) were dispersed in anhydrous dimethylformamide (DMF) at room temperature.

[0321] After dispersion, a carboxylic acid with a primary or secondary halogen is added, followed by anhydrous sodium azide (NaN3). The temperature is then slowly increased (1°C / min) to 80°C. A nucleophilic substitution (SN) reaction occurs between 20°C and 40°C, in which an organic azide and a sodium halide soluble in DMF are formed. The organic azide releases dinitrogen (N2) and forms a nitride intermediate at temperatures >50°C. The nitride is characterized by having 6 electrons instead of 8 in the outer shell of nitrogen. This reactive intermediate undergoes cycloaddition with a π-π double bond on the graphene surface to form a stable azircyclic propane anchor.

[0322] The cycloaddition of aziridine provides the opportunity to add a customized amount of carboxylic acid to the graphene surface without impairing the optical and electrical properties of graphene.

[0323] An exemplary reaction scheme and method are outlined in Example 4. Example 4: Graphene Derivatization (Carboxygraphene (CG))

[0324] Figure 3A shows an exemplary reaction scheme for the synthesis of carboxylated graphene from detonation graphene in a one-pot reaction.

[0325] This scheme begins with detonation synthesis of graphene (0.40) nanosheets with an elemental composition of 98.9% C, 0.08% H, and 1.02% O. The material is then reacted with bromovalerate and sodium azide to produce carboxylated graphene (CG), as follows.

[0326] 1.0 g of graphene nanosheet (GN) particles were suspended in 20 mL of DMF in a 250 mL three-necked round-bottom flask at 20 °C. Next, 0.50 g of 5-bromopentanoic acid (0.0028 mol) and 0.18 g of NaN3 crystals were added to the GN suspension at 40 °C. After the NaN3 dissolved, the suspension was slowly heated to 80 °C (approximately 1 °C / min) and stirred for 1 hour. N2 release was observed. Finally, after 1 hour, the suspension was cooled to room temperature, and the derivatized GN was collected by centrifugation (5 min @ 7,000 rpm), washed 5 times with DMF, and then washed 3 times with anhydrous diethyl ether. The resulting GN was dried at 50 °C for 1 hour for characterization and then stored under argon. Yield: 82% (by weight, assuming total reaction). The elemental analysis of carboxylated graphene showed 94.22% C, 1.79% H, 1.17% N, and 2.82% O. The inclusion of N indicates that the addition reaction was successful.

[0327] Figure 3B shows a schematic structure of carboxylated graphene in which only a single graphene nanosheet is shown, while Figure 3C shows a side view of a top layer of carboxylated graphene with a high density distribution of carboxylic acid groups throughout the surface.

[0328] As shown in Figure 3D, differential thermogravimetric analysis (DTA) indicates a weight loss of 4.5 ± 0.5% by weight when heated above 100°C. The material is nearly stable at temperatures above 100°C, as expected from graphene. The initial weight loss is due to partial loss of valeric acid units linked in the carboxylated graphene shell. Example 5: Titration of Carboxylated Graphene (CG)

[0329] 100 mg of carboxygraphene (CG) was suspended in 20 mL of 0.100 M NaOH. After stirring the suspension at 300 K for 5 minutes, 0.100 M HCl solution was added gradually. At each step, after ensuring that equilibration had been reached (1 to 5 minutes), the pH of the solution was recorded using a pH meter before adding the next amount of HCl. The same procedure was used with the same volume of NaOH, but without adding carboxygraphene.

[0330] As shown in Figure 4, the volume difference of HCl in two titration curves for the same pH value of approximately 7.00 gives the concentration of ionized groups (carboxyl groups) per weight increment of carboxylated graphene. The titration curve shows an acidic group (-COOH) density of 6.1 × 10⁻⁴ mol / g (i.e., a surface density of 4 -COOH groups / nm²). For comparison, this is 3 times higher than that of Fenton-oxidized graphene oxide, which has an acidic group density of 1.7 × 10⁻⁴ mol / g (i.e., a surface density of 1 -COOH group / nm²). This density of -COOH groups on the surface of the carboxylated graphene corresponds to an average space of 0.27 nm² for each -COOH group in the synthesized carboxylated graphene. This corresponds to a very high labeling density of surface -COOH groups, which contributes to the much higher water dispersibility of these carboxylated graphene nanosheets.

[0331] It should be understood that the synthesis of carboxylated graphene is even simpler and has a faster reaction time compared to Fenton-oxidized graphene oxide. The process also does not involve the addition of iron salts that slightly interfere with the photophysical properties of the fluorophores linked in the biosensors described herein. Example 6: Carboxylated graphene-polyethyleneimine (G-PEI)

[0332] The following describes an exemplary synthetic procedure for chaining polyethyleneimine to carboxylated graphene to produce polyethyleneimine-derived carboxylated graphene.

[0333] At room temperature, 100 mg of the synthesized carboxygraphene was suspended in 20 mL of anhydrous DMF in a 50 mL round-bottom flask. Next, 50 mg of branched polyethyleneimine (PEI) with a molecular weight of 10,000, 25 mg of 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC), and 25 mg of 4-dimethylaminopyridine (DMAP) were added to the carboxygraphene solution. The reaction was stirred overnight at room temperature. Subsequently, the carboxygraphene-polyethyleneimine (G-PEI) was collected by centrifugation (5 min @ 7000 rpm) and washed twice with DMF and three times with diethyl ether. The material was collected by centrifugation after each washing step. Yield: 65% (by weight, assuming total reaction).

[0334] A schematic diagram of the obtained carboxylated graphene-polyethyleneimine structure is shown in Figure 5A, where only a single sheet is shown for reference. Elemental analysis of the obtained carboxylated graphene-PEI: 82.58% C, 4.65% H, 10.65% N, and 2.12% O. The significant increase in N content indicates that the addition reaction of PEI was successful. Based on the N content, it is estimated that the obtained carboxylated graphene-PEI contains 27 ± 2% PEI.

[0335] The differential thermogravimetric analysis (DTA) shown in Figure 5B indicates a weight loss of 4.5 ± 0.5% by weight, starting at 150 °C and extending across the entire temperature range. This behavior is significantly different from that of carboxylated graphene and is consistent with graphene nanosheets stabilized by bonding via polyethyleneimine. Example 7: Synthesis of a PEI-coated biosensor based on exploded graphene

[0336] A detectable component formed from the peptide cleavage sequence of tetra(4-carboxyphenyl)porphyrin (TCPP) and MMP-1 is connected to a graphene-based biosensor coated with PEI according to the following scheme.

[0337] 50 mg of the synthesized carboxygraphene-PEI was suspended in approximately 8.1 mL of DMF in a small glass vial. Next, 5 mg of DMAP, 5 mg of EDC, and approximately 3.5 mg of TCPP pre-conjugated with the peptide cleavage sequence of MMP-1 (GAGVPMS-MRGGAG) were added to the vial. The sample was sonicated for 5 minutes to ensure that all substances were suspended in DMF. The reaction was then stirred overnight at room temperature.

[0338] The biosensor was collected by centrifugation (5 min @ 7000 rpm) and washed with DMF and then three times with ether. Figure 6A shows the structure of the carboxylated graphene-polyethyleneimine-based biosensor; only a single piece is shown for reference.

[0339] The synthesized materials were analyzed by spectroscopic methods. As shown in Figure 6B, the UV / Vis spectra of MMP-1-containing carboxylated graphene, carboxylated graphene-PEI, and carboxylated graphene biosensors in water were analyzed. The TCPP-detectable labeled Soret band was clearly distinguishable at approximately 420 nm. TCPP is a peptide chain containing the shared sequence of MMP-1 and a polyethyleneimine layer system. Based on an estimated absorption coefficient of 135,000 M⁻¹cm⁻¹, the TCPP concentration was 1.63 × 10⁻⁵ mol / g carboxylated graphene.

[0340] Figure 6C reports additional exemplary detectable portions that can be added using the process schematically shown above, showing BODIPY dyes with little or no overlap between the excitation and absorption spectra, including BODIPY FL, BODIPY TR, BODIPY 530550, and BODIPY 630650. Example 8: Further Functionalization of Graphene Surfaces

[0341] In the foregoing embodiments, the graphene surface was functionalized to chain carboxylic acid groups to the graphene surface. Subsequently, polyethyleneimine (or mainly other polymers) was bonded to the carboxylic acid functional groups via stable amide bonds.

[0342] Additional functional groups can be provided to the surface of graphene, for example, through modification of carboxyl groups. Those skilled in the art will be able to use the entire toolbox of organic chemistry

[33] to perform such functionalization for use in carboxylic acid reactions suitable for chaining polymers or monomers to the surface of graphene. For example, refer to the following exemplary reactions: - converting a carboxylic acid to an alcohol and forming a stable ether - converting a carboxylic acid to an ester - converting a carboxylic acid to an aldehyde and subsequently to a double bond (Wittig-type reaction) - converting a carboxylic acid to a carbon center with an effective leaving group, followed by a nucleosubstitution reaction.

[0343] As those skilled in the art will understand, additional reactions can be used on graphene, with or without prior functionalization, to provide functional groups configured to bind to corresponding functional groups on organic or inorganic compounds, or directly to corresponding functional groups on peptide bonds. These reactions include: - Click reactions [34,35] (especially Diels-Alder reactions [36,37]). These can be carried out directly on the surface of graphene, or with peptide sequences and chain links already connected to the surface. - Addition of radicals (e.g., radicals with carbon, nitrogen, oxygen, and sulfur centers) to the graphene surface [38,39] - Addition of electrophilic agents (e.g., electrophilic agents with carbon, nitrogen, oxygen, and sulfur centers) to the graphene surface

[39]

[0344] In some embodiments where carboxylated graphene is functionalized based on titration experiments and molecular modeling to provide carboxylated graphene, the highest possible packing density of linear molecules attached to the graphene surface is 6.0 ± 0.5 × 10⁻⁴ mol / g. The lower limit of observable water dispersibility is 2.40 ± 0.05 × 10⁻⁵ mol / g.

[0345] As those skilled in the art will understand, additional maximum and minimum packing densities can be identified for different functionalizations. In the case of synthesizing asymmetric nanobiosensors characterized by unsubstituted (pristine) graphene surface regions, this region is no greater than 10 relative percentages to minimize interference with particle fluorescence readings caused by the adsorption and quenching of previously cleaved fluorescent dyes on the graphene surface. Example 9: Direct and Indirect Linkages of Peptides with Carboxylated Graphene

[0346] In the alternative linking process of detectable components, instead of using polyethyleneimine or other water-soluble polymers, peptide sequences linked to fluorescent dyes can also be directly linked to the carboxyl groups of carboxylated graphene.

[0347] In some cases, in addition to or instead of direct connection to the graphene surface, peptide bonds can be indirectly connected by providing spacer regions or linkers for binding to the carboxyl group. For this purpose, lysine, ornithine, or any non-natural amino acid with spacer regions and accessible additional primary amines can be used to facilitate the connection, as illustrated in the schematic diagram of Figure 7.

[0348] The spacer region can be combined with the desired peptide bond by a technician, while controlling the particle size to obtain the desired distance between the particle and the detectable part.

[0349] The “connector” (e.g., a shared sequence of linked luciferases) between a specific class of nanostructures based on central graphene and the detectable components of the biosensors described herein is a supramolecular system. A large number of supramolecular host-guest systems have been published in the literature.

[40]

[41] The following host-guest systems are noteworthy: a) Biotin / streptavidin

[42] b) Cyclodextrines and multiple hosts

[43] c) Stoddart-cyclophanes and hydrophilic hosts

[44] d) Cucurbiturils and custom hosts

[45] e) (structurally modified) DNA assemblies

[46] f) Nucleic acid / peptide hybrids

[47] g) Supramolecular binding of designed peptides

[48] Example 10: Chemical amination and peptide bond linkage on graphene surfaces

[0350] Figure 8 schematically illustrates exemplary additional chemical amination of graphene surfaces, such as aromatic substitution of nitrobenes for chemical amination of graphene surfaces

[20] .

[0351] According to an exemplary protocol, 1.0 g of graphene nanosheets (GN) were suspended in 20 mL of DMF in a 250 mL three-necked round-bottom flask at 20 °C. Next, 0.50 g of 1-amino-4-bromo-butane (0.0033 mol) and 0.20 g of NaN3 crystals were added to the GN suspension at 40 °C. After the NaN3 dissolved, the suspension was slowly heated to 80 °C (approximately 1 °C / min) and stirred for 1 hour. N2 release was observed. Finally, after 1 hour, the suspension was cooled to room temperature, and the derivatized GN was collected by centrifugation (7,000 rpm for 5 minutes), washed 5 times with DMF, and then washed 3 times with anhydrous diethyl ether. The resulting graphene amine (GA) was dried at 50 °C for 1 hour for characterization and then stored under argon. Yield: 80% (by weight, assuming total reaction). The elemental analysis of carboxylated graphene showed 95.48% C, 1.86% H, 2.38% N, and 0.28% O. The inclusion of N indicates that the addition reaction was successful.

[0352] To add a detectable component, 1.00 g of GA was dispersed in 30 mL of DMF in a flask with a stir bar. 0.54 g of EDC and 0.53 g of DMAP were added and stirred at room temperature until dissolved. 0.20 g of GA was added and the reaction was stirred for 21 hours. The CGP particles were washed four times with DMF and three times with diethyl ether by centrifugation as described above. The GA concordant sequence of the MMP 3-TCPP-labeled particles was dried by rinsing with argon and keeping loosely covered at room temperature for 48 hours. This reaction can be carried out with each peptide designed and configured as a peptide bond (concordant sequence, post-translational modification, and supramolecular binding) in the sense of this disclosure. Example 11: Reaction of carbocations embedded in graphene with secondary amine groups of PEI

[0353] Another PEI linkage reaction can be carried out by combining secondary amine groups with carbocations in explosive graphene.

[0354] In the exploded graphene 0.3, multiple “pores” (carbocations) are embedded in the structure, resulting in a zeta potential of +60 mV. [8] The pores near the surface can react with polyethyleneimine, as shown above. Notably, the exploded graphene can undergo nucleophilic addition reactions with almost all alcohols, thiols, amines, phosphines, and (possibly) carboxylic acids (polymeric, monomeric, and small molecule).

[0355] In conventional multilayer graphene, hydroxyl and ethylene oxide functional groups can react with PEI. The hydroxyl group, after elimination, forms a carbocation, which then reacts according to the scheme in Figure 9. The ethylene oxide (oxygen-containing three-membered ring) reacts with an amine under nucleophilic addition. These reactions covalently link PEI to the graphene layers. If defects (carbocations) are present in the graphene, another possibility arises: directly linking PEI to the graphene. This allows for direct nucleophilic bonding of PEI to the graphene surface. Example 12: Electrostatic attraction on the graphene surface

[0356] The strong positive charge of the exploding graphene allows anionic polymers containing sulfonic acid, aminosulfonic acid, and organic sulfuric acid to be adsorbed onto the graphene surface. The driving force is electrostatic attraction.

[0357] Note that when block copolymers are used, hydrophobic polymers, such as polystyrene, polyisobutylene, polyvinyl chloride, polyethylene, polypropylene, etc., can be components of the block copolymer. They will be adsorbed onto the surface of the original graphene, while their hydrophilic components will regulate the water dispersibility. Note that the surface charge of the exploded graphene is positive (ζ potential = +60 mV). [8] When negatively charged polymers (e.g., polystyrene-sulfonic acid) are used in block copolymers of polystyrene, this allows for a combination of hydrophobicity and charge attraction. Example 13: "Click Chemistry" of Graphene

[0358] Additional reactions that allow for the connection of molecules on the surface of the graphene nanosheets containing the core particles described herein can be carried out via click chemistry.

[0359] "Click chemistry" can be carried out on the surface of exploded (and multi-layered) graphene. The typical reaction is the Diels-Alder reaction because it does not require a copper catalyst. Figure 10 shows a scheme for the Diels-Alder reaction between maleimine derivatives and graphene. However, in principle any click reaction, including the addition of thiols to the surface, as well as the addition of free radicals (e.g., carbon, nitrogen, oxygen and sulfur centers) to the graphene surface [37,38], the addition of electrophiles (e.g., carbon, nitrogen, oxygen and sulfur centers) to the graphene surface

[38] , and other methods that can be identified by a person skilled in the art (see also Example 8), can be carried out as described, for example, in Li et al. 2016

[48] , Namvari et al. 2014

[49] and Farivar et al. 2021

[50] . Example 14: Use of biosensors and related functional principles

[0360] Figure 11 schematically illustrates the functional principle of the biosensor described herein for related purposes.

[0361] In the schematic diagram of Figure 11, it is shown that when interacting with a biological sample containing a target biomarker, changes in peptide bonds (e.g., cleavage or elongation) result in characteristic changes in the detectable signal of the detectable portion of the sensor, such as changes in fluorescence spectra. Observable changes in fluorescence (increase, decrease, shift, etc., depending on the detectable portion) are a function of the presence and / or activity of the biomarker.

[0362] In an exemplary embodiment schematically illustrated in Figure 11, during proteolytic cleavage of the concordant sequence (oligopeptide), the linked fluorophores are released and escape quenching by the graphene core of the biosensor. Fluorescence enhancement and intensity were observed to be a function of protease activity. Instead of particle-based biosensors, biosensors can be constructed using similar components, except that graphene is first immobilized on a solid support (e.g., a flat substrate), with other elements such as peptide bonds and subsequently detectable portions attached to the solid support to create a flat sensor, such as a lateral flow-type sensor or a fixed micropore sensor. As those skilled in the art will understand after reading this disclosure, the sensor will otherwise operate in a manner in which a sample is added to the solid support, and a detectable change is produced in a assay on the solid support by binding or cleavage of a target biomarker in the sample. Example 15: Multiple Biosensors

[0363] Although classic biosensor designs allow each particle to use only one protease-cleavable fluorescent dye or quantum dot, the broad UV / Vis absorption spectrum of graphene and its derivatives allows for the interconnection of a wide variety of biosensors targeting different proteases.

[0364] Figure 12 shows a working example of a detonation-graphene-based biosensor for matrix metalloproteinase-1. The working principle of a graphene-based biosensor for protease activity is illustrated herein.

[0365] As shown in Figure 12, the protease (if present in the test sample) cleaves its common (cleavage) sequence and releases a luciferase. The luciferase escapes the quenching of the graphene, resulting in a measurable increase in fluorescence ("luciferase activation"). The prototype biosensor for MMP1 was tested in samples with increased concentrations of the enzyme.

[0366] It can be seen that the intensity of the detection signal is proportional to the enzyme concentration, as shown in Figures 13A and 13B. Even under suboptimal experimental design, the estimated limit of detection (LOD) of this prototype biosensor still shows detectability of femtoprotein activity. When TCPP binds, it shows almost no fluorescence due to strong carboxyl graphene quenching. It is subsequently released over a 60-minute period, and its fluorescence is detectable. Example 16: Buffering and Standard Reaction

[0367] Proteases are protein-digesting enzymes that function under physiological conditions. The matrix metalloproteinase family involves complex divalent cations, while cathepsins are primarily activated in an acidic lysosomal environment. For example, the initially designed Fe-NBS protocol was prepared in a 25 μM HEPES buffer containing 10 μM divalent cations.

[0368] The system was modified to achieve a more robust signal through improved pH control by incorporating isotropic conditions to support protease activity while also supporting the need for divalent cations. It was also attempted to demonstrate that buffers compatible with multiple different gNBS sensors can be constructed in a single step, allowing for future use in multiple applications.

[0369] To this end, a series of buffers containing MgCl2, CaCl2 and ZnCl2 at a final concentration of 10 μM were designed. HEPES buffer, as well as MES, TRIS and phosphate-buffered saline were evaluated (table in Figure 14).

[0370] Solutions of uPA gNBS and CTSD gNBS were prepared in water at a working concentration of 300 μg / ml (10×). They were then subjected to acoustic treatment in a water bath for 10 minutes. Figure 14 reports the dilution of gNBS at a ratio of 1:10 in each different buffer formulation.

[0371] 125 μl of gNBS solution was repeatedly dispensed into black 96-well plates. 5 µL of serum was added, and the plates were incubated at 37 °C for 60 min. The emitted fluorescence was read on a VarioSkan Lux disc reader at 421 nm excitation and 650 nm emission. Assays included a control in which 5 μl of appropriate buffer was added instead of serum. gNBS was incubated with two different normal healthy donor serum samples or pooled normal serum (PNS). Buffer 1 was the initial formulation. Protease activity was observed under all buffer conditions. The results are reported in Figure 15, Figure A (CTDS gNBS) and Figure 15, Figure B (uPA gNBSS).

[0372] Much more stable pH was observed in PBS and MES buffer formulations, where the pH remained stable for several days. Only DMSO significantly increased the RFU observed under stimulation conditions. However, it is noteworthy that buffers containing NaCl significantly reduced both background and serum signal. Lowering AC contributes to improving the signal-to-noise ratio performance of gNBS. Buffers 2, 3, 6, 8, and 9, all containing NaCl, exhibited significantly reduced AC performance compared to the initial buffer.

[0373] Buffer 9 was selected as the buffer with the best signal-to-noise ratio performance. This performance supports the conclusion that the effect of physiological ion concentration stabilizes the zeta potential and enables the natural conformation of the peptide-TCPP construct. Therefore, the zeta potential stability that contributes to the stability of the particle solution also supports optimal enzyme conditions. The effect of NaCl on the zeta potential can be observed in Figure 16. To confirm these observations, the experiment was repeated on additional gNBS using buffers 1, 2, and 9. Evaluation of gNBS against uPA, CTSD, MMP10, and CTSH all showed reduced AC signal and the highest signal-to-noise ratio with buffers containing isotonic salt concentrations, with buffer 9 being superior to buffer 2 (Figure 17).

[0374] In addition to the features of the biosensors disclosed herein, these experiments have demonstrated the role of ions in the effective function of gNBS for detecting protease activity.

[0375] In particular, the functionality of the sensors disclosed herein relies on the ability to be repeatedly constructed to the same specifications and to allow measurement of activated proteases or other suitable biomarkers in normal serum samples, making it possible to prepare different batches and groups of sensors to produce the same signal.

[0376] Additionally, buffers and preparation conditions can be selected to produce the optimal signal-to-noise ratio. In this experiment, it was observed that the addition of monobasic salt ions significantly reduced noise, as reflected by the reduction in the buffer signal alone (see AC values ​​in Figures 15 and 17).

[0377] It was also noted that the effect of monobasic salt ions stabilized the zeta potential of the particles within a pH range of 5.0 to 8.0. This range reflects the natural working environment of certain protease family members, which are typically expressed in lysosomes or other acidic environments. Protease activity can occur under a range of pH conditions reflecting the natural biological environment from pH 2.0 to pH 10.0. Depending on the pH requirements, the MES buffer can be replaced with a buffer whose pKa is more suitable for the pH target, such as PIPES, MOPS, HEPES, TRIS, phosphate buffers, Bikini, CHES, or other buffers commonly found on the Good list.

[0378] The above observations support the conclusion that the presence of salt reduces the AC activity of the sensor. An effective salt concentration (shown in Figure 18) with a ratio less than 1.0 was observed compared to the same sensor in the same buffer but without any monobasic salt ions. Monobasic salt ions of 15.5 mM to 155 mM reduced the AC ratio under salt / no salt conditions. Hypertonic salt conditions can similarly improve signal-to-noise ratio performance by reducing AC. Salt concentrations can be as high as 600 mM, or even as high as 1 M, under physiological conditions reflecting a high-salinity environment. Example 17: Fabrication and performance of a biosensor prepared using an ultrasonic oscillation process.

[0379] In the presence of a limited proportion of oxygen, several layers of graphene can be produced in a detonation reaction using a hydrocarbon acceptor in a gas-phase detonation. The resulting graphene consists of branched fractal aggregates, which have an average size of 50 to 500 nm when detonated with a molar oxygen ratio of 0.3.

[0380] Electron microscopy of the obtained particles shows that the graphene aggregates are composed of chains, sheets, aggregates and individual lamellar crystals, as shown in the exemplary image of Figure 19A.

[0381] The size and shape spectra are clearly visible from the transmission electron microscopy analysis of gNBS (Fig. 19B). The particles imaged in Fig. 19B were prepared as described in the KSU publication dated December 30, 2020 (batch 120120). They show many irregularly shaped particles of different sizes, and the number of layers at higher magnification reflects the expected 5 to 25 layers of graphene.

[0382] As fractals, there is no defined size or shape for individual particles. They consist of aggregates formed during detonation, and therefore their size and shape are random across the spectrum.

[0383] An exemplary SEM (scanning electron microscopy) of the exploded graphene is shown in Figure 1. [8] To prepare the gNBS solution for determination, the gNBS was mass-out and resuspended in a buffer to a defined concentration by mass. The solution was then dispersed in an ultrasonically oscillating water bath. The time in the water bath was not defined in the initial disclosure and could result in different levels of particle destruction. The effect of this difference is seen in Figure 20. When the working solution of gNBS was generated, the KSU batch 120120 formulation caused some particles to precipitate from the solution. This was due to the ineffective dispersion of graphene during the initial preparation process, resulting in instability of the final product. In contrast, when the graphene was prepared by probe ultrasonic oscillating during preparation and before gNBS assembly, the particles maintained more polydispersity.

[0384] To further evaluate the effect of acoustic treatment on the distribution of the resulting gNBS group, the dispersion characteristics of the particles were analyzed by running the v7.02 analysis software on Malvern Instruments ZetaSizer. The detonated graphene was either directly dissolved in DMF (without acoustic treatment) or actively dispersed by acoustic treatment in DMF with a Fisherbrand FB705 Sonic Dismemberator for 1 hour.

[0385] Using a standard protocol, the resulting graphene solution was first reacted with bromopentanoic acid and sodium azide to assemble gNBS to produce carboxylated graphene (CG) (Figure 21: Figure A). It was further modified by chaining polyethyleneimine with carboxylated graphene using EDC and DMAP to produce polyethyleneimine-derived carboxylated graphene (Figure 21, Figure B). Finally, peptide-TCPP was added using EDC chemistry with a DMAP catalyst to produce a sensor; in this case, a peptide recognized by the protease neutrophil elastase (NE) was used. Solutions of each intermediate, CG, and polyethyleneimine-derived CG (CGP) were prepared and treated in an ultrasonic oscillation bath for 10 minutes to disperse the solution for effective mixing and redistribution of the particles, and to ensure effective surface modification and water solubility. PDI measurements were performed immediately after acoustic treatment and subsequently again after 30 minutes of settling.

[0386] The results of the polydispersity index (PDI) measurement are shown in Figure 21. PDI is a measure of particle uniformity in solution. The data show that after ultrasonically oscillating the starting graphene material for 1 hour before assembling gNBS, the particle size uniformity was greater (the number of PDIs was smaller). This uniformity was evident in the CG and CGP intermediates as well as the NE gNBS final product.

[0387] In addition, a solution of 1.0 g graphene at 0.3 (OC ratio) was dissolved in 20 mL of DMF without further treatment. The absorbance at 270 nm was measured on a NanoDrop 1000C spectrophotometer. The solution was then dispersed for up to 60 minutes using a probe ultrasonic oscillator with 20-second on and 10-second off pulses at an intensity level of 35. Samples were collected at intervals, and ABS270 nm was measured for up to 1 hour. As the probe ultrasonic treatment continued, the graphene slurry became so thick that it had to be diluted to obtain the ABS270 nm reading, and the reading was corrected for the dilution. Figure 22 shows the increase in absorbance with increasing treatment time. The increase in absorbance is because the graphene fractal is broken down into smaller particles when more energy is applied.

[0388] When the energy applied during acoustic treatment increases the number of particles, the decrease in overall particle size leads to Mie or Rayleigh scattering of light, which is observed as a decrease in transmission over the UV-Vis spectrum

[51] . Thus, when graphene is acoustically treated, the quality of the input process decreases, but the same or more linked peptide-TCPP particles can be maintained due to the overall increase in particle surface area.

[0389] Proteases are highly sensitive to environmental changes, such as pH and chemicals in buffers. Upon examination, no chemicals used during the cleaning and preparation of the biosensors remained on the sensors, contributing to the observed batch-to-batch variations. Thermogravimetric analysis (TGA) was performed on four CG formulations. The temperature was increased from room temperature (20°C) to 1000°C at a rate of 20°C / min in a nitrogen environment using a TGA5500 instrument.

[0390] In many fabricated nanosensors where efforts were made to completely dry the particles, a decrease in the overall percentage of quality loss was observed (table in Figure 23).

[0391] In particular, the TGA of the initial KSU batch (120110) showed that most of the quality was lost in the temperature range up to 100°C, which matches the boiling point of the solvent used in the preparation and indicates residual solvent contamination. When the process was modified to include the use of temperature and desiccant to make an effort to dry, the percentage of quality loss was reduced (Figures 24 and 25).

[0392] It was observed that sensors without solvent removal required a curing period before fully functioning. For the initial batch of nanosensors prepared by KSU, this curing period was determined to be 2 weeks, where the signal-to-noise ratio was insufficient to produce a significant signal, mainly due to increased AC. In contrast, nanosensors prepared with efforts to remove residual solvent (including overnight drying and incubation at 37°C after manufacturing) produced robust signals within 5 days of preparation (Figure 26). Drying of the final sensor product was considered important for removing residual solvent and exhibiting optimal activity.

[0393] Residual solvent affects the performance of gNBS by inhibiting optimal sensor startup (manifested as a decrease in signal-to-noise ratio performance). Therefore, the removal of the preparation solvent was characterized using TGA. TGA provides a measurement of the amount of solvent released as a percentage of the total quality. Specifically, solvent removal was performed at 20°C to 200°C or 20°C to 400°C. As a contribution to the total quality of the sensor, the preparation of gNBS should result in a contribution of less than 30% to the quality of the thermal transition. Ideally, the quality loss measured by TGA should be less than 10% or 5%. Ideally, the quality loss measured by TGA should be less than 1%. Example 18: Effects of Batch-to-Batch Variation and Ultrasonic Oscillation Treatment

[0394] The preparation method, as defined by the initial disclosure, is a three-step process.

[0395] First, graphene was modified to produce carboxylated graphene (CG). This was achieved by dispersing 2.0 g of detonation-synthesized graphene at 0.3 (OC ratio) in 40 mL of DMF in a 250 mL Erlenmeyer flask, which was suspended in a silicone oil bath at room temperature with a magnetic stir bar. After dispersion, the absorbance of the solution was measured on a NanoDrop 2000C spectrophotometer. The reaction temperature was raised to 40 °C, and 1.0 g of 5-bromopentanoic acid was added to the graphene suspension, followed by the slow addition of 0.36 g of sodium azide crystals. Once all reagents were dissolved, the temperature was slowly raised to 80 °C and incubated at 80 °C for 1 hour, and then allowed to cool to room temperature. The suspension was washed 5 times with DMF and 3 times with diethyl ether by centrifugation at 10,000 × g for 5 minutes, and then dried.

[0396] Next, to prepare the polyethyleneimine-derived carboxylated graphene (CGP) intermediate, 1 g of CG was dispersed in 30 mL of DMF in a flask with a stir bar. 0.54 g of EDC and 0.53 g of DMAP were added and stirred at room temperature until dissolved. 1.2 g of polyethyleneimine was added and the reaction was stirred for 21 hours. The CGP particles were centrifuged four times with DMF as described above and washed three times with diethyl ether. The CGP particles were dried by rinsing with argon and keeping loosely covered at room temperature for 48 hours.

[0397] The final step was the conjugation of the sensor peptide-TCPP. 103 mg of CGP was dispersed in 16 mL of DMF for 1 minute using an ultrasonically oscillating water bath. 10.5 mg of EDC and 11 mg of DMAP were added to the CGP with stirring at room temperature. Subsequently, 7 mg of the TCPP-labeled peptide was added, and the reaction was stirred at room temperature for 20 hours. The resulting gNBS was washed three times with DMF and three times with diethyl ether, followed by argon rinsing. The final gNBS product was stored at -20°C. This protocol reflects the initial KSU protocol.

[0398] Large performance variations were observed between batches of gNBS formulations prepared with graphene using the initial protocol, even among batches prepared on the same day (Figure 27 compares batches 120120 and NE100 with NE101 and NE102). It is thought that the use of ultrasonic oscillation to treat the water bath dispersion sensor during working solution preparation may produce different population sizes and particle numbers with each ultrasonic oscillation treatment. Therefore, unless particle size is optimized prior to preparation, repeated ultrasonic oscillation treatments of the gNBS working solution will periodically alter the analytical results.

[0399] To determine the effect of ultrasonic treatment on the starting material, modifications were made to the preparation as outlined in the table in Figure 28. As described in the initial protocol, the starting graphene material was dispersed in DMF using only a stir bar without ultrasonic treatment, or dispersed for 10 or 20 minutes using a probe ultrasonic oscillator (table in Figure 28). After dispersion, the amount of graphene input decreased due to the increase in particle number and surface area, but the amount of the peptide TCPP input for the conjugation reaction remained unchanged. One batch of NE gNBS was prepared using the KSU protocol, and two batches were prepared using probe ultrasonic treatment of the starting graphene for 10 and 20 minutes.

[0400] All three batches were compared with those prepared in KSU (Figure 26). KSU batch 120120 and HEB batch NE100 were prepared using the same protocol and dispersion procedure. HEB batches NE101 and NE102 were modified by ultrasonic oscillation treatment involving probes. gNBS were evaluated using normal human serum under standard conditions compared with the assay control (AC) containing sensors and buffers but without serum. For batches 120120 and HEB batch NE100, an average difference of 244% was observed between batches 1 and 2. In contrast, the batches treated with ultrasonic oscillation (NE101 and NE102) showed an average difference of 132% between batches (Figure 27). The conclusion is that dispersion by ultrasonic oscillation treatment prior to preparation resulted in more consistent particle size, smaller and more acceptable PDI (<0.5) in the graphene starting material (Figure 21), and more reproducible activity in the final gNBS sensor.

[0401] To determine the effect on particle size in the final formulation, the effect of ultrasonic oscillation treatment in a water bath on the final particles was evaluated. A significant increase in ABS was observed in NE-gNBS that were not subjected to acoustic treatment during preparation, with an average increase of 259% in ABS at the ABS wavelengths of graphene (265 nm) and TCPP (425 nm).

[0402] In contrast, for batches prepared including acoustic treatment, the change in ABS over time showed an average change of 121% at the same wavelength (Figure 29). The conclusion is that particle preparation including a probe ultrasonic oscillation treatment step produces a more uniform suspension, unaffected by the water bath ultrasonic oscillation treatment step used to prepare the working solution. Example 19: Fabrication and Performance of a Biosensor

[0403] Preferred methods for assembling particles include using ultrasonic oscillation or physical crushing to adjust the particle size distribution. Graphene is derivatized by adding carboxyl moieties to the surface using a aziridine anchor. The surface is then modified by adding a polymer such as polyethyleneimine (PEI) followed by a peptide dye complex.

[0404] For the set of experiments reported in this embodiment, attempts were made to discover the boundaries of these steps and components by replacing other components in order to evaluate modifications that could be considered for alternative preparation schemes.

[0405] Specifically, the experiment was repeated to determine the amount of carboxyl labeling that occurred during the first step of the synthesis. Solutions of 0.5 M NaOH and HCl were prepared using high-purity HPLC-grade water. The 0.5 M NaOH solution was degassed with argon for 2 hours, followed by dispersion of CG. After degassed, 250 mg of CG was dispersed in 50 mL of 0.05 M NaOH. The sample was ultrasonically shaken in a water bath for 10 minutes and then stirred at room temperature for 24 hours. After 24 hours, the CG suspension was filtered to remove CH<, and the solution (0.05 M NaOH, 0.05 M NaOH, and 0.05 M HCl filtered with CG) was degassed overnight. Next, 20 mL of 0.05 M HCl was added to a 10 mL aliquot of the CG-treated and filtered NaOH solution. After each addition of NaOH, the pH was recorded once it stabilized and before the next addition. For the blank, a 10 mL solution of 0.05 M NaOH was prepared in the same manner by adding 0.05 M HCl and back-titrifying with 0.05 M NaOH. Degassed during titration was maintained. Using this protocol, the CG formulation required more NaOH for neutralization than the blank, indicating the presence of acidic functional groups. Repeat experiments yielded concentrations of 1.765 × 10⁻⁴ and 1.8 × 10⁻⁴ mol / g acidic groups. While lower than the initial estimate, this supports the addition of acidic groups to graphene (Figures 30A and 30B).

[0406] To support this observation, elemental analysis of unmodified graphene and carboxylated graphene showed undetectable levels of oxygen and hydrogen in the starting graphene materials (table in Figure 31). After carboxylation, detectable levels of H and O were observed from 0.5% to 3.5%. A decrease in oxygen content was noted compared to the initial KSU formulations (KSU and CG1.0), thus concluding that some of the oxygen content originated from the solvent. To support this, formulations CG1.2, CG1.3, and CG1.4 (refer to the table in Figure 28) were prepared using an ultrasonic oscillation treatment and an included drying scheme, and showed even lower percentages of H and O obtained. In comparison, the CG product derived with PEI (CGP102) produced significantly higher contributions of H, O, and N. CGP102 was assembled using CG1.0 (table in Figure 31).

[0407] Based on this, it was concluded that the initial pH titration overestimated the number of carboxylic acid groups added to the graphene, and a more accurate estimate was obtained using argon degassing and back titration. This was supported by elemental analysis, which showed that the estimated oxygen content in the CG formulation in which residual solvent was actively eliminated was lower than the estimated oxygen content in the product produced using the initial scheme. Despite the differences in elemental analysis, all schemes described were still able to annotate graphene with H and O and produce functional biosensors.

[0408] To determine the steps used for biosensor assembly, the addition of PEI to graphene was tested in the absence of a prior carboxylation step. In the first case, the control uPA gNBS was assembled using a standard protocol via the carboxylation of graphene followed by PEI polymerization. In the second example, 0.3% graphene was replaced with CG with the same addition of 500 mg / g PEI, using 243 mg / g EDC and 247 mg / g DMAP, as outlined in the table for CGP104 in Figure 28 (uPA - without GC).

[0409] The reaction was incubated for 90 minutes with stirring at room temperature. In a third case, 0.3 g of graphene was dispersed in DMF containing 550 mg / g PEI but without EDC or DMAP catalyst. Instead, the reaction was heated to 80 °C for 1 hour (uPA-80 °C). After the addition of PEI, all three formulations were washed at least five times with 200 mg / g standard ethanol and resuspended in DMF. Subsequently, all three formulations were conjugated with the peptide TCPP using the uPA-TCPP sequence. All three constructs were treated with 105 mg / g EDC, 111 mg / g DMAP, and 65 mg / g uPA-TCPP for 90 minutes with stirring at room temperature. The resulting particles were washed with ethanol and dried overnight in a 37 °C oven. The sensor was tested by preparing solutions of 200 µg / mL in 10 mM MES, 10 µM CaCl2, MgCl2, ZnCl2, and IS:155. Incubate 70 µl of each working solution with 10 µl of pooled normal serum at 45 °C for 90 min.

[0410] Fluorescence was measured using 425 nm excitation and 653 nm emission on a VarioSkan Lux disc reader. All three formulations produced assay control values ​​less than 0.05 and had RFUs of 19 to 31 from pooled normal serum (in Figure 32). All three sensors were insensitive to the reaction at 10 mM KOH, which indicates non-covalent binding of TCPP (data not shown).

[0411] Therefore, it is concluded that, since the oxygen detection limit of elemental analysis of graphene is 0.5%, the target with added PEI becomes detectable when the amount of oxygen is increased to a detectable level in the case of CG generation. However, considering the functional sensors produced by thermal condensation at 80°C or by directly adding PEI with a catalyst to graphene, evidence has been obtained that 0.3% graphene has some ability to connect the small amount of PEI molecules required to assemble the sensor, which implies the presence of oxygen in detonated graphene.

[0412] Other methods for coupling PEI to graphene that require fewer steps or are more efficient were also evaluated. Alternatives for the bromovalerate linker step have been identified, such as modified tetraethylene glycol (TEG), NHS azidoacetate, or azidoacetic acid. These molecules provide azido and carboxyl or amino groups in place of bromovalerate + azido compound.

[0413] An experiment was conducted in which 125 mg of graphene probe was ultrasonically oscillated into 22.5 mL of DMF and heated to 60 °C. PEI was prepared by adding 60 mg of 10 k MW PEI to 3 mL of DMF followed by the addition of 270 mg of NHS azidoacetate. The mixture was heated to 60 °C and incubated for 30 min. The activated PEI solution was then added to the graphene, and the temperature was increased from 60 °C to 80 °C over 10 min. The reaction was continued for 1 h and purified by washing and drying with ethanol. A second formulation was prepared in the same manner, but with 10 k MW PEI instead of 1.2 k MW PEI. Both constructs were then treated with 105 mg / g EDC, 111 mg / g DMAP, and 65 mg / g uPA-TCPP for 90 min at room temperature with stirring. The resulting particles were washed with ethanol and dried overnight in an oven at 37 °C. The sensor was tested by preparing solutions at 200 µg / mL in 10 mM MES, 10 µM CaCl2, MgCl2, ZnCl2, and IS:155. 70 µl of each working solution was incubated for 90 min at 45 °C in black 384-well plates with 10 µl of buffer, 50 mg / mL acetylated BSA, or pooled normal serum.

[0414] Fluorescence was measured using 425 nm excitation and 653 nm emission on a VarioSkan Lux disk reader (Fig. 33). Similar activation was observed in uPA sensors prepared using either PEI formulation. This observation suggests that the distance between peptides and associated dyes and the graphene backbone can be controlled by selecting polymers of different molecular weights, thereby reducing the distance between the graphene quencher and fluorine. This may become necessary when switching to more efficient fluorescent dyes with shorter Foster radii or more efficient luciferase groups. The experiment also determined the ability to use anchors other than bromovalerate.

[0415] Currently, the assembly of graphene-based biosensors consists of a series of surface modification reactions using the following modifications: graphene to carboxylated graphene to carboxylated graphene - PEI to graphene biosensor. To reduce this complex synthetic process, single surface modifications of graphene were investigated prior to assembling the biosensor, as described below.

[0416] A small-scale reaction was initiated by dispersing 1 g of 0.3 g of detonated graphene in 50 mL of distilled and degassed N,N-dimethylformamide (DMF) (Fisher Scientific catalog number: D119-20) in a round-bottom flask at room temperature. The dispersion was then heated to 40 °C, and 556 µL (0.0028 mol) of azide-TEG3-amine (BroadPharm catalog number: BP-20580) was slowly added. The temperature was then increased to 80 °C, and the reaction solution was stirred for 1 hour. After 1 hour, the reaction was cooled to room temperature, and the sample was collected by centrifugation (5 min at 8000 rpm). Finally, the G-TEG3 amine product was washed 5 times with DMF and 3 times with anhydrous diethyl ether. The resulting G-TEG3 amine was further dried with argon and stored at -20 °C.

[0417] The next step was to assemble the MMP1 biosensor using G-TEG3 amine, following the same optimized conditions established for the G-PEI biosensor. For this purpose, two solutions were prepared: 1) 3.75 mg of TCPP-MMP1 peptide, 5 mg of EDC, and 5 mg of DMAP dissolved in 2 mL of distilled and degassed DMF; and 2) 50 mg of G-TEG3 amine dispersed in 2 mL of distilled and degassed DMF. Both solutions were sonicated (3 min) in an ultrasonically oscillating water bath and vortexed until completely dissolved. Solutions 1) and 2) were then mixed and further ultrasonically oscillated (3 min) to ensure all substances remained suspended in DMF. The solutions were then stirred overnight at room temperature. After 24 hours, the biosensor was collected by centrifugation (8000 rpm for 8 min), washed three times with DMF, and washed three times with ether. Finally, the biosensor was dried with argon and stored at -20°C.

[0418] Once the biosensor was assembled, dynamic light scattering (DLS) and zeta potential measurements were performed using a NanoBrook 90Plus PALS. For this purpose, a 0.03 mg / mL MMP1 solution was prepared using high-purity water (HPLC water, Fisher catalog number: W7-4). The mean size (DLS), polydispersity index, and zeta potential surface charge are shown in the table in Figure 34. The results showed that the biosensor had a micro-positive charge of 4.36 mV, a mean diameter of 713.30 nm, and a PDI of 0.147.

[0419] The following experiment is a side-by-side comparison of the biosensors G-PEIMMP1 and G-TEG3 amine MMP1. For this experiment, a 0.03 mg / mL solution of each MMP1 biosensor was prepared using HEPES buffer (25 µmol / L HEPES buffer rich in 10 µmol / L Ca2+, Mg2+, Zn2+, pH 7.20). After preparing the biosensor solutions, the pH of G-TEG3 amine increased to 7.40 and the pH of G-PEI increased to 7.52; therefore, the pH of both solutions was subsequently adjusted to 7.20 using 0.1 M HCl solution.

[0420] Three solutions (described below) were prepared and plated in duplicate in 96-well plates (Greiner Bio-one, 96-well microplate, PS, F-bottom, black non-bound; item #: 655900). - Solution 1: Sample control (125 µL of HEPES buffer + 5 µL of collected control serum - S, P, or pooled) - Solution 2: Assay control (125 µL of biosensor solution in HEPES buffer + 5 µL of HEPES buffer) - Solution 3: Assay (125 μL of biosensor solution in HEPES buffer + 5 μL of collected control serum - S, P, or pooled).

[0421] After the solution was plated, the 96-well plates were incubated at 37°C for 1 hour, and fluorescence intensity was measured every 15 minutes during the 1-hour incubation period. Fluorescence intensity was measured using a BioTek Synergy H1 disc reader (exc = 425 ± 10 nm, em: 650 ± 20 nm). Figure 35 shows the fluorescence intensity measured every 15 minutes for each control serum (S, P, and pooled) of the MMP1 G-PEI and MMP1 G-TEG3 amine biosensors. For all three tested control sera, the highest intensity measured after 1 hour of incubation with G-TEG3 amine was higher than that of the G-PEI biosensor.

[0422] Moreover, for both biosensors tested with all three control serum samples, the intensity increased with time in an almost linear trend. Figure 36(A) shows the intensity measured after 1 hour of incubation for the two biosensors tested with each control serum. Here, the intensity difference between G-PEI and G-TEG3 amine can be noted, where the intensity of G-TEG3 amine was almost twice that of G-PEI for control sera S and P. It should also be mentioned that the intensity of G-TEG3 amine was lower than that of G-PEI for the assay controls, which is summarized in Figure 36(B). Overall, the G-TEG3 amine-based biosensors produced improved signal / noise measurements with lower assay controls and produced a larger serum-specific signal.

[0423] Based on the above experiments, it is concluded that since the graphene synthesized by explosion is hydrophobic and is not actually dispersed in water or an aqueous buffer (and many surfactants interfere with the fluorescence quantum yield of organic dyes), the addition of a hydrophilic coating is necessary to obtain the desired stability.

[0424] This can be achieved by attaching carboxylic acid groups to the surface of the graphene stack via azibene addition

[53] . Alternatively, amine-terminated oligoethylene glycols can be attached to the surface of the exploded graphene via azibene addition.

[0425] Polyethyleneimine is linked to the carboxylic acid. Alternatively, the amino group may be linked to the surface of the exploded graphene and subsequently to a polymer exhibiting the carboxyl group.

[0426] Alternatively, for the addition of graphene with azide groups, the precursor can be combined with the polymer and then the polymer can be anchored to the graphene surface.

[0427] The bonded organic coating (polymer, oligoethylene glycol) has the following functions:

[0428] Without a hydrophilic coating, graphene nanobiosensors will not disperse in an aqueous buffer. The achievable concentration of the biosensor depends strongly on the chemical properties of the coating and the mass ratio of the coating to graphene.

[0429] When dispersed in an aqueous buffer, the coating is responsible for the stability of the biosensor, and the graphene core of the biosensor is very stable.

[0430] The size and structure of the PEI are responsible for the fluorescence enhancement observed during proteolytic cleavage of the shared sequence. Generally, Förster Resonance Energy Transfer (FRET) occurs between the graphene (acceptor) and the attached organic fluorophore. The effectiveness of FRET depends on the distance. Therefore, the structure of the PEI (branched vs. linear) is important. However, graphene (and especially graphene stacks) effectively absorbs light from the ultraviolet region to the near-infrared region. Theoretical studies by Bian et al. (2016) suggest that the quenching range around the graphene monolayer extends to about 20 nm

[54] . A strong quenching that greatly reduces TCPP fluorescence occurs within this "Förster volume". After escaping from the Förster volume, TCPP begins to emit. Example 20: Multi-graphene biosensor

[0431] The possibility of operating multiple different sensors in the same reaction well was considered. Graphene has broad-spectrum light absorption (Figure 37). Therefore, multiple different peptide-dye sequences can be assembled on a single graphene particle, or multiple sensors, each labeled with a different single peptide-dye, can be mixed together.

[0432] The multiple capabilities of the biosensors of this disclosure were evaluated using the following system – mixing two separately labeled particles. Peptides of neutrophil elastase (NE) and argininase (ARG) were labeled with FAM or TAMRA. Peptide-FAM or peptide-TAMRA was directly substituted into the preparation of peptide-TCPP, where the only modification was conjugation at half density due to the increased fluorescence efficiency of these dyes. Therefore, peptides were added at 35 mg / g CGP (refer to the table in Figure 28). gNBS working solutions were prepared by assembling NE-FAM, NE-TAMRA, or uPA-TCPP gNBS into an assay buffer consisting of 10 mM MES, 10 µM CaCl2, MgCl2, ZnCl2, IS:155. 125 μl of working solution containing an increased amount of each gNBS was dispensed into black 96-well plates, and each well was stimulated with buffer (for assay control (AC)) or in triplicate with 5 μl of pooled normal serum. The plates were incubated in VarioSkan Lux at 45 °C (Figs. 38 and 39). Fluorescence of each reaction was observed over 90 min using Ex496 / Em526 (for FAM) and Ex555 / Em586. Alternatively, MMP7 and MMP9 peptides were conjugated with CGP at 70 mg / g with Rhodamine-B using the KSU preparation protocol described above. The gNBS working solution was prepared to a final concentration of 30 µg / mL gNBS in 10 mM MES, 10 µM CaCl2, MgCl2, ZnCl2, IS:155. Triplets of 125 µL of the reaction mixture were prepared in black 96-well plates and incubated at 37°C for 60 min with 5 µL of serum or buffer-only (AC). Fluorescence was measured using an Ex535 / Em570 on a BioTek Synergy H1 FL800 disc reader (Figure 39).

[0433] These observations support the model that graphene can serve as an effective quencher for a variety of different dyes. It has been shown that multiple different sensors can be constructed using different dyes, and that these dyes can be successfully quenched by the graphene framework in individual reactions, attempting to demonstrate that multiple dyes can be observed in the same reaction.

[0434] For the gNBS preparation protocol described above, Arg-FAM and NE-TAMRA gNBS were prepared again by halving the input peptide dye contribution to 35 mg / g CGP. The gNBS working solution was prepared by assembling 400 µg / ml Arg-FAM or NE-TAMRA gNBS into an assay buffer consisting of 10 mM MES, 10 µM CaCl2, MgCl2, ZnCl2, IS:155. 80 µl of reactants were set in a 384w black plate, with 35 µl of a single sensor and 35 µl of buffer, or 35 µl of each sensor mixed, to bring the final concentration of any sensor to 200 µg / ml. 10 µl of pooled normal serum or buffer was added to each sensor solution.

[0435] The sensors were stimulated in triplicate and incubated at 45°C for 90 minutes, with measurements taken at 10-minute intervals. Each well was measured at 496 / 526 nm (FAM) and 555 / 586 nm (TAMRA) (Fig. 40).

[0436] Notably, both single and multiple sensors were detectable, and no signal was detected from one sensor to another channel; however, there was some evidence of quenching when both sensors were present (comparing singlet and multiplet fluorescence in either channel). Arg-FAM was also observed in both singlet and multiplet reactions against an elevated background. This is interpreted as indicating that the amount of conjugated peptides can exceed the quenching capacity of the graphene backbone when using highly efficient dyes.

[0437] This noise can be mitigated by titrating to reduce the labeling level. Alternatively, a shorter linker chemistry between the FAM dye and graphene will bring the FAM closer to the FRET pair and improve quenching. In this context, replacing the 10 kMW PEI with the 1.2 kMW PEI described in Figure 32 is considered as a feasible solution.

[0438] The use of PEI polymerization or a combination of direct conjugation, such as by TEG or NHS azidoacetate or equivalents, is considered to conjugate dye-labeled peptides to a graphene backbone, thereby enabling the graphene backbone to effectively quench the attached dye.

[0439] As exemplified above, such chemical changes are expected to be effective as long as the peptide-dye conjugate remains within 20 nm of the graphene

[54]

[55] . Therefore, signal-to-noise ratio performance can be improved by modifying the distance between the dye conjugate and the graphene backbone through optimized polymerization or conjugation chemistry to optimize effective dye quenching based on the quantum efficiency of the selected dye. This conjugate selection must also contribute sufficiently charged groups, such as carboxylic acids, amino groups, or alcohol side chains, to allow for secondary conjugation of the peptide-dye complex and support the dissolution of the graphene particles.

[0440] Finally, the NE-FAM gNBS reaction was set up in duplicate, with one assay performed in a 96w black plate and analyzed on a VarioSkan Lux platform, and the other assay using NE-FAM gNBS in a 96-well PCR plate and analyzed on a StepOnePlus PCR platform (Figure 31). The absence of background signal and the linear relationship between incubation time and fluorescence were evident on both platforms, and this increases the possibility that these types of gNBS assays can be performed on laboratory equipment with even wider distributions than fluorometers, such as quantitative PCR platforms. Example 21: Heat Treatment of Serum

[0441] Experiments were designed to demonstrate the specificity of these reactions by heat-inactivating the protease. For this purpose, samples of two different control sera were heat-treated at 55°C for 1 hour. The heat-treated and untreated sera were incubated with MMP10 and CTSH gNBS under standard conditions. A modest increase in RFU was unexpectedly observed in both sera and both sensors (Fig. 42, Fig. A). The experiment was repeated with treatment of both control sera at 65°C for 10 minutes (any longer interval resulted in serum precipitation). In addition, the sera were treated with EDTA to achieve a final EDTA concentration of 5 mM in the sensor reaction. MMP2 was also substituted for MMP10 gNBS to understand the generalizability of this observation (Fig. 42, Fig. B). Consistent with other reports, treatment of sera with EDTA produced a modest decrease in activity

[56] . In contrast, heat treatment of sera at 65°C resulted in a significant increase in activity in both sera and both sensors. The addition of EDTA moderately reduced the elevated activity, as it did with untreated serum, and this effect was more pronounced for the MMP2 sensor. Therefore, unexpectedly, treatment of serum at elevated temperatures showed an increase in the activity of an additional protease associated with gNBS signaling. To determine whether this change was a result of an additional initiation event occurring when the zymogen was activated, CTSH gNBS was stimulated with pooled normal serum or a duplicate sample (heat-treated at 65°C for 10 min). The reaction was measured at 30-minute intervals after the first hour within 240 min. The activities of both untreated and heat-treated serum remained linear over the duration (Figure 43). If an additional zymogen were activated, an increase in kinetics over time would be expected. However, no evidence of kinetic change was found.

[0442] To support this conclusion, the effect of heat treatment on the activity of pooled normal serum sensors was evaluated in 18 protease biosensors (Figure 44). When serum heat-treated at 65°C was used, each sensor whose signal was significantly higher than that of the assay control (RFI > 0.05) showed an average 3.2-fold increase in activity.

[0443] Finally, MMP12 and CTSD gNBS were prepared in 125 μl working solutions of 5 mM MES, 10 μM cations, and IS:155. They were stimulated with 5 μl buffer (for background measurement) or in triplicate with 5 μl PNS. Replica plates were assembled, and each replicate was incubated in VarioSkan Lux at 37°C, 41°C, or 45°C. Fluorescence was measured at 10-minute intervals during incubation. Sample background measurements are shown from wells incubated with 125 µL buffer (sensorless) and supplemented with 5 µL serum. In this experimental group, the serum was not heat-treated prior to measurement. Instead, the reaction was carried out at progressively increasing temperatures.

[0444] Each temperature increment showed additional activity; therefore, for both MMP12 and CTSD gNBS, incubation at 41°C produced more fluorescence than at 37°C, and at 45°C more than at 41°C (Fig. 45). For MMP12 and CTSD, in previously untreated serum, increasing the assay temperature to 41°C nearly doubled the RFU (1.7× and 1.9×, respectively). Increasing the assay temperature to 45°C doubled the RFU (3.1×). Under these conditions, the sensor background signal or sample background was not increased. For comparison, serum was also pretreated at 65°C for 5 minutes, and the effect on the signal-to-noise ratio was compared between pretreatment at 65°C and the application of the increased assay temperature (compare the open and closed symbols in Fig. 45).

[0445] The signal of serum pretreated at 65°C was 2.3-fold higher than that of heat-treated serum. Subsequent assays run at 41°C and 45°C also showed 2.3× and 2.0× signals, respectively. Therefore, pretreatment of serum and running the protease assay at elevated temperatures improved the signal-to-noise ratio using both individual and additive mechanisms.

[0446] Regarding the thermostability of proteases, active MMP2 enzymes have been reported to have two irreversible development temperatures of 70 °C and 78 °C; these are substantially higher than the temperatures used herein

[57] . The possibility that a family of inhibitors designed to modulate protease activity exhibits higher temperature sensitivity and that operating these reactions at higher temperatures reduces their inhibitory effect was considered. The effect of increasing temperature concluded to alter several aspects of the assay, which together contribute to a significant improvement in the signal-to-noise ratio.

[0447] It is anticipated that the stability of the protease and the relative instability of the inhibitory protein to increased temperature will alter the ratio of activity to the inhibited enzyme. Increased temperature will lead to enhanced enzyme kinetics, providing an improved detection of protease activity at elevated temperatures. Furthermore, the increased temperature modification is expected to produce protein corona observed around the particle and described herein (Figure 46).

[0448] The expected temperature-dependent gradient balance of protease activity with temperature will denature other proteins in serum before precipitation occurs. Culturing serum before or during assays effectively improves the signal-to-noise ratio. 41°C, or 45°C, or 50°C, or 55°C, or 60°C, or 65°C, or 70°C, or 75°C, or the intervals between these targets are considered effective in achieving the desired effect. Example 22: Negative Control

[0449] Well-described literature describes the corona that forms when proteins aggregate around particles

[58] . It is desirable to test whether a protein solution lacking any activity can produce and promote a background signal. It is worth recalling that most of these sensors require protease-specific cleavage.

[0450] Arginase is an exception as an enzyme targeting post-translational modifications. In the gNBS system, arginase converts arginine to ornithine and urea. This modification alters the structure and causes the peptide to localize the terminal TCPP dye outside the Foster radius of graphene. Therefore, understanding the contribution of general protein corona to background performance is important. To understand the signal-to-noise ratio using proteins as negative controls, 175 human serum samples were evaluated on 18 gNBSs. Each gNBS was aggregated in MES assay buffer (10 mM MES, 10 µM cations, 155 mM NaCl, pH 7.2). 125 µL of each gNBS working solution was added to three wells. 5 µL of serum was added to duplicate wells, and 5 µL of buffer was added to a third well.

[0451] Also included was a sample-only control, in which 5 μL of serum was added to 125 μL of buffer without a sensor. Additionally, each sensor was stimulated with 5 μL of 20 mg / mL acetylated BSA solution. The plates were loosely covered and incubated at 45°C for 90 min, followed by fluorescence measurement at Ex / Em 425 / 653 nm for 500 ms on a VarioSkan Lux disc reader (Figure 24). In one exception (Arg gNBS), the average fluorescence obtained by all gNBS was significantly greater than the BSA response. Only one sensor had a serum sample with a signal equal to or less than the BSA control. The BSA control was significantly greater than the assay control used to measure the amount of fluorescence provided by the sensor in buffer-only. This negative control was interpreted as a much more valuable reflection of the non-specific performance of gNBS. The effect of temperature on Arg gNBS activity demonstrates that the optimal temperature for this sensor is below 45°C. Example 23: Lyophilization

[0452] Maintaining reproducible and repeatable performance is crucial for maintaining an effective signal-to-noise ratio. Formulations of uPA, CTSE, CTSK, and NE gNBS in the MES working buffer were provided, and lyophilized plates were prepared using three different excipient formulations. The plates were tested on different dates using the same batch of normal serum and compared with wet chemistry batches added to the same plate.

[0453] When compared with wet reagents, improvements in inter- and intra-assay accuracy were observed, as measured by reduced coefficients of variation in both settings (Figure 47). Therefore, maintaining higher accuracy allows for more reproducible signals in different measurements of protease activity. This accuracy is necessary to maintain constant signal-to-noise ratio performance in the assay environment. With consistent, reproducible, and reproducible measurements of protease activity, the relationships between protease activities of protease target groups can be combined to distinguish serum from that of normal patients and patients with a range of different inflammatory conditions. Example 24: Clinical Performance of Graphene Biosensors

[0454] Performance of gNBS on clinical samples. In the first cohort, 256 subjects were accumulated from a European website and classified as either lung cancer (n=89) or non-lung cancer (n=167). Serum samples were heat-treated in a thermal looper at 65°C for 12 min and then cooled to 15°C. Working solutions were prepared using MES assay buffer (5 mM MES, 10 μM cation, pH 7.20, 154 mM NaCl), with each gNBS adjusted to a final ABS425nm value of 0.1 to 0.15 depending on the sensor. The preparation was ultrasonically agitated in a water bath for 10 min and 125 µL was dispensed into repeat wells. 5 μL of heat-treated serum was added to the repeat wells and a single serum control. Each plate contained a sensor-only assay control.

[0455] The plates were loosely covered with foil and placed in an incubator at 37°C for 90 minutes. Fluorescence was read out at Ex / Em 425 / 653 nm on a VarioSkan Lux. The mean fluorescence observed for LC and non-LC subjects is reported (table in Figure 48). A general decrease in protease activity was recorded in lung cancer patients vs. non-lung cancer patients. Patient stage and disease subtype did not affect this observation. Similarly, the non-LC subjects included 9 patients with breast cancer, colorectal cancer, and hematologic malignancies (which did not confound the non-LC analysis), although these subjects were not included in this analysis. 175 subjects in the second cohort were recruited as part of the "US SST-18" cohort.

[0456] In this experiment, all non-LC controls were age-matched donors with a smoking habit of >20 pack-years, serving as case controls for the LC cohort. In the LC cohort, most LC samples were from untreated Phase I subjects. In this experiment, a second batch of sensors, different VarioSkan Lux fluorometers, and samples from the US (but not the EU) were tested. gNBS were aggregated and the solution adjusted to a final ABS425 nm concentration of 0.1 to 0.15 according to the sensor in the MES assay buffer (10 mM MES, 10 μM cation, pH 7.20, 155 mM NaCl). 125 μl of assay buffer or an assay buffer containing each gNBS was loaded into a 96W black plate.

[0457] The plates were loosely covered with foil and incubated at 45°C for 90 min. Fluorescence was read once at a single endpoint (500 ms) on VarioSkan Lux at Ex / Em 425 / 653 nm. A similarly significant decrease in overall protease activity was observed in the LC samples (table in Figure 48). Student's two-tailed heteroscedasticity t-test was calculated to compare RFUs between lung cancer and different non-lung cancer cohorts. The current calculations yielded p-values ​​ranging from 2.6e-6 to 2.6e-27 in both cohorts and for all sensors. The implications of this observation are the possibility that these sensors can be configured in many different arrangements to realize diagnostic potential with a very strong ability to detect the presence of disease.

[0458] Figure 49 shows a table of p-values ​​for Student's two-tailed heteroscedasticity t-test comparing RFU of lung cancer and non-lung cancer samples. Example 25: Exemplary methods and systems for fabricating biosensors

[0459] Some exemplary methods for assembling particles may include the following steps: 1) dispersing a large amount of multilayer graphene in dimethylformamide (DMF) to obtain a solution; 2) treating the solution with a probe ultrasonic oscillator using 10 to 15 kJ / g of graphene for up to 1 hour; 3) functionalizing the graphene surface to derive a carboxylated surface to obtain carboxylated graphene (CG) by means of a nucleophilic substitution reaction with sodium azide and bromovalerate, as described herein; 4) washing the CG with DMF or ethanol to remove reactants; 5) for core particles assembled with PEI, resuspending the CG in DMF to obtain a CG / DMF solution; 6) distributing the CG / DMF using ultrasonic oscillation treatment; 7) in the case of exemplary polyethyleneimine (PEI), conjugating the PEI at room temperature for 1 to 2 hours using 1-ethyl-3-(-3-dimethylaminopropyl)carbodiimine hydrochloride (EDC) and 4-dimethylaminopyridine (DMAP) in a defined ratio. 8) Thoroughly wash the product with ethanol to obtain core particles. 9) Dry the core particles at ambient temperature for 30 minutes, and then further dry them at 37°C for 24 to 48 hours.

[0460] The corresponding system may include: a) layered graphene; b) reagents for modifying graphene, connecting polymers, and subsequently, sensors; c) apparatus for ultrasonic oscillation processing, probe ultrasonic oscillation processing, physical dispersion using a high-shear mixer, or dispersion (e.g., microwave); d) tools for measuring absorbance to normalize the graphene concentration used in assembly (e.g., a Nanodrop spectrophotometer); and / or e) sensor preparation apparatus for drying sensors (e.g., an oven supplemented by a vacuum or drying chamber in some cases). Example 26: Exemplary System for Detecting Biomarkers Using Biosensors of the Present Disclosure

[0461] The method for detecting one or more target biomarkers according to this disclosure can be performed using a system comprising the following components: 1) a nanosensor. This can be a single sensor or multiple sensors. The sensor can be provided as a solution and adjusted to a working concentration using absorbance defined for a working solution for each sensor. Alternatively, the sensor can be provided pre-lyophilized in an opaque 96 or 384-well plate. 2) a buffer: Sensor activity is supported by an optimal buffer. This buffer can be provided as a solution or as a lyophilized cake in a multi-well plate. The buffer contains components that stabilize the solution at a defined pH. In the current configuration, this pH is 7.2. The optimal pH can vary depending on the presence of the sensor and the optimal pH required to support enzyme health and activity. Preferably, the buffer will also contain a monovalent ion. The monovalent solution supports enzyme health and provides ion buffering for the particles to support dispersion. Furthermore, the peptide configuration will be affected by the concentration of the monovalent salt. Generally, the buffer will contain ions necessary for enzyme activity. In the case of proteases, MMPs are enzymes that require divalent cations, so the buffer is supplemented with a selected divalent cation.

[0462] Additional components include, for example, the following: 3) Standard curves consisting of a series of concentrations of the included dyes. These standard curves will be used to convert relative fluorescence units (which depend on the fluorometer used) into dye concentrations. The standard curves may contain a variety of different dyes for multiplex reactions. Therefore, the standard curves allow the kit to be used on many different platforms. Alternatively, relative fluorescence may be used.

[0463] Alternatively or additionally, the system may also include: 4) controls indicating the presence of a functional sensor. These controls may comprise serum samples having known and proven activity. They may be purified enzymes with known activity. They may be chemicals designed to hydrolyze peptides to non-enzymatically release dyes.

[0464] The system may also include: 5) an incubator capable of maintaining the multiwell plate at a constant temperature during the experiment. Some fluorometers include this incubation capability. The plate needs to be maintained at 25°C, 37°C, 41°C, 45°C, 50°C, 55°C, 60°C, or 65°C for up to 5 minutes during the experiment. 6) a fluorometer for determining the amount of fluorescence released during the reaction. This fluorescence is typically measured as an endpoint, but can also be measured dynamically, allowing the rate of change to be measured at intervals of 1 minute, 5 minutes, 15 minutes, 30 minutes, or 60 minutes. 7) an application-specific algorithm. The integration of sensor activity describing diagnostic status will require algorithms to interpret defined activities and classify samples. This algorithm will be application- and sensor-specific.

[0465] In particular, components 1 to 4 and 7 may be included in the kit, as will be understood by the technician.

[0466] For multiplex detection, another consideration is that dye selection and the combination of sensors with multiple dyes are calibrated to ensure no FRET between dyes. Additionally, depending on whether the sensor is connected to a single particle or to a mixture of individual particles, the final particle concentration must be optimized to ensure sufficient particle interaction with the reactants to drive the reaction, but not so much graphene to quench the released signal. Furthermore, for mixed sensors, a buffer should be available that provides optimal activity for both enzyme targets. Signal reproducibility depends on reproducing the environment of each reaction. Therefore, a buffer providing a stable pH and defined salt concentration, as well as a spectrophotometrically defined sensor concentration, will replicate the environment and support batch-to-batch stability (see Figure 27). Example 27: Testing the stability, reproducibility signal-to-noise ratio, and multiplex capability of a biosensor in solution.

[0467] During the fabrication of the gNBS sensor, several layers of graphene were covalently bonded to the sensor reagent. The covalently assembled graphene particles did not decompose in aqueous solution. As a result, the particles were observed not to cause pH drift in aqueous solution and were dispersible in aqueous solution as described herein.

[0468] A second aspect of the structural advantages of graphene-based sensors is reflected in the signal-to-noise ratio performance of each nanosensor family in aqueous solutions containing serum. At time point 0, no significant release of any fluorescent signal is expected because at t=0, the enzyme has no time to cleave and release any fluorescence. Furthermore, the signal enhancement after the addition of serum depends on both the system's sensitivity and consistently low noise, making the signal distinguishable. The relationship between FeNBS and gNBS at t=0 and the sensor and serum-cultured samples was evaluated, and two differences were found.

[0469] The control, i.e., the sensor in the presence of only buffer, showed a slight increase in signal over a period of 60 minutes, depending on the sensor for FeNBS. This increase was approximately 2-fold. In contrast, gNBS maintained in aqueous solution for up to 90 minutes showed no significant change in background signal. However, the S:N performance of gNBS, neither prepared by ultrasonic oscillation nor by solvent removal, also produced a lower signal over time as the S:N decreased at t=0. Therefore, the performance of the gNBS sensor depends on better S:N at t=0 and improved S:N detection in stimulating cultures.

[0470] More significantly, a low signal-to-noise ratio was observed when calculating the signal:noise ratio for graphene NBS at t=0, as measured by dividing the sample fluorescence by the control fluorescence. The gNBS sensor showed a small difference between the control and the sample at t=0. For multiple different graphene sensors in batches prepared without probe ultrasonic oscillation or thorough drying (in these examples CTSB, uPA, CTSK, CTSD, CTSE, MMP12, MMP13, MMP15, NE, MMP3, MMP9), the S:N ratio at t=0 was 4.1 + / - 1.6 (1 SD).

[0471] In contrast, when ultrasonic oscillation and drying were included, the S:N ratio at t=0 increased to 6.9 to 7.6; there was no significant difference compared to FeNBS. However, after 60 minutes of stimulation with serum, the S:N ratio at t=60 was 2.0 for the example FeNBS sensor CTSB. For CTSB prepared using CG KSU batch 120120, the SN ratio at t=60 was 9.8. For HEB CTSB100, the SN was 12.0; for HEB CTSB101, the SN was 18.5; and for CTSB102, the SN was 27. Therefore, when solvent removal and ultrasonic oscillation were included in the preparation, in this example, the amount of enzyme activity shown by a single serum sample increased from 2.0 to 27.

[0472] For a given sample, the S:N performance difference is evident in both the t=0 performance and the relative signal generated over time. Therefore, S:N is not dependent on the different performance of any single sensor. Instead, it is generally applicable to the framework rather than any specific enzyme activity. FeNBS and gNBS were compared in a single experiment with different batches of gNBS reflecting solvent removal and ultrasonic oscillation treatment. The S:N performance of gNBS improved with each iteration of the preparation scheme reflecting solvent removal and ultrasonic oscillation treatment.

[0473] When defining the test solution, the optimal formulation coordination is the best S:N performance as a measure of sensor stability, in which the optimal kinetics of activity increases over time, reflecting the provision of a healthy environment for enzyme activity.

[0474] This balance has been observed to be optimized using MES at a pH of 7.2. When contained at 155 mM, the inclusion of a monosodium salt maintains the physiological environment for enzyme activity. However, a range of monosodium salt concentrations from 0 mM to 1 M also affects the sensor's S:N ratio. As the results reported in Figure 14 show, increasing the monosodium salt concentration reduces the control signal at 60 minutes. Therefore, when the salt concentration increases, the "noise" attributed solely to the particles is lower. 155 mM was used as the concentration to reduce particle noise and support enzyme activity under physiological conditions. Example 28: Stability, reproducibility signal-to-noise ratio, and multiplexing capability of the biosensor in test solutions in some embodiments where a heating step is included.

[0475] The effect of heat treatment was observed when heat treatment was applied to serum before culture or when measurements were performed at temperatures above 37°C. For a typical sensor, in this example, the MMP10, heat treatment was performed by incubating at 65°C for 5 minutes before measurement, resulting in an SM / AC S:N of 1.2 without heat treatment and 1.3 with heat treatment. Therefore, heat treatment did not improve the low signal-to-noise ratio. After culture in the presence of serum, the signal of the untreated serum increased to 14.1 SM / AC after 90 minutes. In contrast, the SM / AC of the heat-treated sample increased to 26.8, representing a 190% increase in signal intensity. Therefore, for the t=0 signal, the effect of heat treatment was not significant; instead, both heat treatment (Fig. 44) and the increased measurement temperature (Fig. 45) provided more enzyme activity, which generated a larger signal over the kinetic interval of the measurement. When pretreated with the increased temperature (65°C), enzyme activity increased by an average of 3.2-fold. Similarly, the signal was increased by an average of 3.1-fold when the assay was incubated at 45°C and by an average of 1.7 to 1.9-fold when incubated at 41°C. This change was observed for all sensors, and the increase in measured activity was temperature-dependent. Therefore, 41°C showed more activity than 37°C, and 45°C showed more activity than 41°C.

[0476] The temperature was established and maintained in an oven or fluorometer with temperature control. Increased temperature will result in increased enzyme kinetics, providing improved detection of protease activity at elevated temperatures. Furthermore, the increased temperature is expected to modify the protein corona observed around the particles and described herein (Figure 46).

[0477] The expected temperature-dependent gradient balances the improvement in protease activity with temperature, which will denature other proteins in serum before precipitation occurs. Culturing serum before or during assays effectively improves the signal-to-noise ratio. Temperatures of 41°C, 45°C, 50°C, 55°C, 60°C, 65°C, 70°C, or 75°C, or the intervals between these targets, are considered effective in achieving the desired effect. Example 29: Exploding Graphene PEI Core Particles

[0478] The following exemplary experimental procedures are discussed using exploded graphene

[12]

[58] . However, in practice, any number of graphene layers can be used to synthesize biomarker detection for biosensors. In this regard, the graphene sheets or aggregates used have a size (diameter < 1 micrometer) that can be dispersed in water.

[0479] Exploded graphene typically consists of 5 to 7 stacked graphene layers. Each layer has an approximate diameter of 150 nm. [8] The surface reactions that form carboxylated graphene target only the two outer surfaces of the graphene stack and some potential edges.

[0480] Polyethyleneimine can covalently bond with carboxylic acid groups on the surface of carboxylated graphene (density of carboxylic acid groups, determined by pH titration: 1.76 ± 0.6 × 10⁻⁴ moles of COOH groups / gram). Example 30: Characterization of polymers or other organic or inorganic structures linked to graphene particles.

[0481] Polyethyleneimine is very hydrophilic (consistent logP = -1.94, solubility in water > 1.2 × 10⁵ g / L H₂O, which is necessary for dispersing graphene particles in aqueous buffers.

[58]

[0482] In principle, all polymers of the correct size can be linked, characterized by a consistent logP < 0, or a solubility in water > 1.0 × 10³ g / L H₂O.

[23] The photophysical properties of graphene

[54] allow for the quenching of linked organic fluorophores, metal particles and quantum dots at distances up to 20 nm, which is again greater than any other known energy transfer distance (Foster resonance energy transfer (FRET) and plasma quenching).

[54]

[0483] Therefore, the diameter of the conjoined polymer layer plus the colinked conjoined sequence does not exceed 20 nm. Otherwise, the fluorescence of the conjoined luciferase will not be enhanced during enzyme release. According to the graph of molecular weight vs. particle diameter of the emulsion polymer reported in Figure 50 (Loebach (1975), Figure 1

[61] ) applicable to this estimate, the permissible quality range of polymers with diameters up to 20 nm is 0.5 to 1.7 × 10⁶ (Mn) and 0.5 to 3.0 × 10⁶ (Mw). However, since the diameters of smaller polymers can be up to 20 nm, smaller polymers can be used. Example 31: Peptide conjoining

[0484] Based on their size, peptides can be classified into oligopeptides (2 to 20 residues) and polypeptides (>20 residues). Proteins consist of polypeptide chains with a length of more than 50 residues.

[61] The average length of a peptide can be estimated to be 0.35 to 0.40 nm per amino acid, depending on the peptide's 2D structure (α-helix or amorphous).

[62] Note that peptides forming β-sheets are not suitable for the design of biosensors because their release kinetics upon enzyme cleavage are very slow. Peptides with 50 amino acids have a length of 17.5 to 20 nm, depending on their 2D structure. It should also be noted that non-natural amino acids can be used for these peptides to anchor them to the surface of graphene or graphene-linked organic or inorganic shells, or for the linking of fluorophores (organic dyes or nanostructures).

[0485] Figure 51 shows the permissible ratio of peptide length (A) and polymer layer thickness (B) in a graphene biosensor for protease activity detection. The maximum layer thickness (peptide + polymer) does not exceed 20 nm. In graphene biosensors for cytokine / chemokine and arginase detection, the layer thickness is close to 20 nm before binding to its target or post-translational modification. Both of these events elongate the peptide sequence, thus leading to increased fluorescence from chain luciferases.

[0486] In principle, graphene particles can be surrounded by an inorganic water-soluble shell (e.g., a mesoporous silica nanolayer).

[63] In this case, the thickness requirements for the organic and inorganic nanolayers are the same: the maximum diameter of the mesoporous silica shell + peptide sequence must be less than or equal to 20 nm. Example 32: Linkage of organic molecules

[0487] Organic and inorganic compounds that provide functional groups can be linked to a graphene surface that has been chemically modified to provide one or more corresponding functional groups.

[0488] For example, a single-layer water-soluble organic molecule, such as an oligoethylene glycol providing an OH functional group, can be chained to the surface of graphene modified by adding a nitrobenzene group or graphene modified by providing an NH2 functional group.

[0489] As shown in Figure 52, instead of polymers, monolayer water-soluble organic molecules (e.g., oligoethylene glycol) can be chained to the surface of graphene. In principle, the requirements for logP and water solubility are the same as those for organic polymers.

[0490] Note that, according to molecular modeling (Chemdraw 3D), the maximum diameter of the amino-tetraethylene glycol azide (NH2-TEG-N3) is 1.5 nm. Therefore, in those exemplary embodiments, the size of the tetraethylene glycol unit is 1.4 nm. Thus, the number of glycol units (-O-CH2-CH2-) is less than 58, and the total length does not exceed 20 nm.

[0491] When coating graphene particles to configure a sensor, the length of the oligopeptide must be subtracted from the length of the oligoethylene oxide chain.

[0492] Note that, in principle, any linear or branched molecule that can be attached to the graphene surface and meets the required solubility in water (log P < 0 or solubility in water > 1.0 × 10³ g / L H2O

[23] ) can be used, as long as it meets the 20 nm energy transfer quenching distance requirement.

[0493] Those skilled in the art will be able to modify the above examples for different organic or inorganic materials that provide functional groups corresponding to the functional groups on the graphene surface. In particular, those skilled in the art will understand that any linear or branched molecule that can be attached to the graphene surface and achieves the target dispersibility (>0.1 mg / mL) in an aqueous solvent, along with the associated graphene core particles, can be used to provide the biosensors of this disclosure, provided that the coating material and peptide bond configuration meet the 20 nm energy transfer quenching distance requirement. Example 33: Titration of Carboxylated Graphene (CG)

[0494] 250 mg of carboxygraphene (CG) was dispersed in 50 mL of 0.05 M NaOH (NaOH solution prepared using HPLC and degassed with argon for 2 hours). The CG suspension was ultrasonically shaken for 10 minutes and stirred at 300 K for 24 hours. After stirring for 24 hours, the suspension was filtered to remove the CG, and all solutions (CG filtered solution, 0.05 M HCl, and 0.05 M NaOH) were degassed with argon overnight. The pH meter was calibrated with a standard pH solution before starting the titration the next day. 20 mL of 0.05 M HCl was added to 10 mL aliquots from the CG filtered solution, and back-titrated with 0.05 M NaOH (added in incremental steps). After each addition of NaOH, the pH was recorded after the pH meter stabilized and before the next addition of NaOH. For the blank titration, prepare 10 mL of 0.05 M NaOH using the same method as for carboxylated graphene, add 20 mL of 0.05 M HCl, and back titrate with 0.05 M NaOH.

[0495] As shown in Figure 53, for the same pH value of approximately 7.00, the difference in the volume of NaOH in the two titration curves gives the concentration of ionized groups (carboxyl groups) of carboxylated graphene per weight increment.

[0496] It should be mentioned that argon gas was maintained during the titration of both the carboxylated graphene filter solution and the blank.

[0497] For the same pH value of approximately 7.00, the difference in the volume of HCl in the two titration curves gives the concentration of ionized groups (carboxyl groups) per weight increment of carboxylated graphene. The titration curves indicate the presence of acidic groups (-COOH) of 1.765 × 10⁻⁴ and 1.8 × 10⁻⁴ mol / g (i.e., a surface density of 4 -COOH groups per nm²). Example 34: Signal-to-Noise Ratio

[0498] The structural features responsible for improving the signal-to-noise ratio include: configuring a biosensor to include fractal graphene as well as a coating and peptide bonds, said peptide bonds being configured to maintain a distance of less than 20 nm from the graphene surface of the chain-linked partially fluorescent dye (TCPP, and other dyes with high fluorescence quantum yield).

[0499] Most common sequences have 13 amino acid residues, resulting in a length of 5 ± 1 nm. The thickness of the covalently linked PEI layer is 10 nm. Therefore, the average position of the TCPP fluorophore is well below the 20 nm threshold.

[0500] Further examples of the signal-to-noise ratio achievable using the biosensors described herein are illustrated in Example 19 (see Figures 33 and 35 and relevant sections of this specification), Example 28 (see Figure 46 and relevant sections of this specification), and Example 46 (see Figure 54 and relevant sections of this specification). As those skilled in the art will understand, these examples demonstrate the operability of all embodiments maintaining a distance <20 nm between the graphene surface and the chain fluorophores. Example 35: Method for preparing core graphene particles for a biosensor

[0501] An exemplary procedure for preparing core particles according to this disclosure: Step 1: Select detonated graphene or multilayer graphene to ensure the desired ultraviolet / visible light absorption behavior (strong emission from ultraviolet to mid-infrared (100 to 1500 nm)). Step 2: For example, chemically transform the graphene surface by including an organic or inorganic shell to make the graphene particles dispersible in an aqueous buffer.

[0502] Due to the quenching properties of graphene, the diameter of the resulting organic or inorganic shell is less than 20 nm.

[0503] The main characterization steps of the obtained particles are: Step 1: UV / Vis absorption spectrum in chloroform in the range of 200 to 1500 nm; RAMAN and XRD were used to determine that the material is graphene, elemental analysis was used to determine that the material is 98++ carbon, TEM was used to determine the size distribution of graphene nanosheets, and SEM was used to determine the fractal properties of graphene. Step 2: FTIR was used to characterize the functional groups of the organic or inorganic shell around the graphene, RAMAN and XRD were used to determine that the material is graphene, elemental analysis was used to determine the chemical composition of the graphene core + shell, TEM was used to determine the size distribution of graphene nanosheets, and SEM was used to determine the fractal properties of graphene. When cross-polarization is used, solid-state NMR only shows the chemical composition of the shell because there are no hydrogen atoms in graphene.

[64] Example 36: Method for preparing the biosensor of the present disclosure

[0504] An exemplary procedure for preparing the biosensor of this disclosure includes the following steps: Step 1: Selecting detonated graphene or multilayer graphene to ensure the desired ultraviolet / visible absorption behavior (strong emission from ultraviolet to mid-infrared (100 to 1500 nm)). Step 2: Chemically transforming the graphene surface by including an organic and / or inorganic shell to make it dispersible in an aqueous buffer. Due to the quenching properties of graphene, the diameter of the resulting organic or inorganic shell should be less than 20 nm. Step 3: Synthesizing the desired peptide sequence and adding a fluorophore to the N-terminus of the peptide sequence. The peptide + fluorophore is then cleaved from the resin. Step 4: Linking the peptide + fluorophore to the organic or inorganic shell. Example 37: Method for detecting biomarkers using the biosensor of this disclosure.

[0505] An exemplary method for detecting biomarkers according to the method of this disclosure includes: 1) selecting an optimal range for linking the luciferase to graphene. For 100 mg of carboxygraphene, the optimal concentration of peptide + TCPP is 7.3 mg. This results in 1.6 ± 0.2 × 10⁻⁵ mol of peptide + TCPP (luciferase) / g carboxygraphene-PEI. At higher loadings, the biosensor responds significantly slower due to spatial interference with enzyme kinetics. The upper limit (when no activity is detected within one hour in the presence of 1 × 10⁻¹⁰ mol L⁻¹ of active protease) is 1.0 ± 0.25 × 10⁻³ mol of peptide + TCPP (luciferase) / g carboxygraphene-PEI. The lower limit (signal disappearance) is 1.0 ± 0.25 × 10⁻⁷ mol of peptide + TCPP (luciferase) / g carboxygraphene-PEI. 2) dispersing the biosensor in a defined aqueous buffer. The optimal concentration range for the biosensor solution is 0.1 to 0.03 mg / mL, with 0.3 mg / mL producing the highest fluorescence intensity. 3) Add liquid biopsy (blood, plasma, serum, saliva, bronchoalveolar lavage fluid, exhaled air condensate) to a specified buffer. The concentration of the targeted enzyme or protein should be in the range of femtomolar (10⁻¹⁶ mol / L) to micromolar (10⁻⁶ mol / L). 4) Incubate the dispersed biosensor + liquid biopsy at a specified temperature for a specified time, or record the increase in fluorescence over time at a specified temperature or at temperature intervals.

[0506] All biosensors used for measuring proteolytic activity share a highly selective common sequence. Biosensors used for arginase measurement rely on post-translational modifications (post-synthetic modifications: arginine to ornithine) that elongate the peptide. Binding of the target peptide to the protein receptor also elongates the former. If the distance to graphene exceeds 20 nm, all increases in distance between the fluorophore and graphene result in increased fluorescence (and subsequent activity detection). Otherwise, fluorescence quenching will dominate. Example 38: Method for multiplexing biomarkers using biosensors of this disclosure.

[0507] In principle, for the detection of one or more substances, the concentration range of dispersed biosensors and the preparation of liquid biopsies are the same. Due to kinetic aspects (the enzyme must reach the peptide sequence), the maximum density of peptide + fluorophores on the graphene surface cannot be increased.

[0508] As described above, the highest possible concentration of peptide + TCPP (luciferase) is 1.0 ± 0.25 × 10⁻³ mol / g, and the optimal concentration (the fastest signal enhancement within the concentration range of femto-micromolar protease action) is 1.6 ± 0.2 × 10⁻⁵ mol / g peptide + TCPP (luciferase) / g carboxygraphene-PEI. When using several peptide sequences + different luciferases for multiplexing, the fractions must be summed up to 1.6 ± 0.2 × 10⁻⁵ mol / g peptide + TCPP (luciferase) / g carboxygraphene-PEI to ensure optimal reaction kinetics. For slower sensors, the total concentration of peptide + different luciferases (all fractions) can reach 1.0 ± 0.25 × 10⁻³ mol / g.

[0509] Methods for linking peptides and fluorophores to the shell surrounding graphene include: 1) stepwise addition, and 2) parallel addition of all peptides and fluorophores. For either method, the chemical addition conditions must be optimized.

[0510] After chemically linking several peptides and fluorophores and undergoing several washing cycles (redispersed three times in DMF, followed by centrifugation, then redispersed three times in ether and centrifuged again, followed by drying at 40°C under argon for 24 hours), the multifunctional biosensor was dispersed in an analytical buffer, and the concentration of the linked fluorophores was measured using a calibration curve of the same dye in the analytical buffer via UV / Vis absorption spectroscopy.

[0511] Furthermore, additional modifications can be made to reduce the surface charge of graphene, making it suitable for bonding with cationic polymers that replace or exclude the aforementioned functionalizations. Example 39: Characterization of additional components of graphene particles and biosensors

[0512] It is suitable to make graphene water-dispersible and / or allow any surface reaction of an organic ligand or polymer having sufficient water solubility (consistent logP < 0, or solubility in water > 1.0 × 10³ g / L H₂O.

[23] ). Alternatively, inorganic shells, such as mesoporous silica, can also be used to disperse graphene.

[0513] The most important factors for organic or inorganic coatings are water solubility, the presence of functional groups of linker peptides and detectable components, and the requirement that the thickness around the graphene be less than 20 nm when linked to promote quenching of luciferases.

[0514] For peptide bonds, the specificity comes from the peptide sequence used. On resin, different fluorophores are linked to the N-terminal position of the peptide. Alternatively, fluorophores can be linked to the C-terminus, but this method is more complex and expensive (but feasible).

[0515] Multiplicity is achieved through the interaction of enzymes and proteins with each co-linked peptide in liquid biopsy, because cleavage and subsequent fluorescence enhancement occur much faster if the protease and its respective co-linked sequence match. The same applies to the successful binding of the target peptide sequence to its respective protein target.

[0516] In addition to the fluorescence from the attached dye, other detectable signals include: 1) phosphorescence from the attached nanostructure and dye; 2) excitation, in principle, can be achieved through multiphoton excitation; 3) photoacoustic features from the attached nanostructure and dye; 4) radioactive isotopes cut from the graphene surface for analysis; 5) stable free radicals (EPR probes) released from graphene covered with oxides of high-spin d- or f-block metals, or co-linked with another stable free radical; 6) MRI probes released from graphene covered with oxides of high-spin d- or f-block metal cations or supramolecularly bound high-spin d- or f-block metal cations. Example 40: Effects of dispersion and solvent removal on the absorbance efficiency of graphene biosensors.

[0517] Solutions of graphene biosensors from batches 1, 2, 3, and 4, reflecting the KSU scheme, HEB replication of the KSU scheme, ultrasonic oscillation treatment scheme (batch 3), and ultrasonic oscillation treatment scheme for eliminating solvent contamination (batch 4), were prepared as working solutions in 5 mM MES pH 7.4 containing 155 mM NaCl. The sensors were prepared at a concentration of 25 µg / mL. The working solutions were prepared and ultrasonically oscillated at 1 min / mL in a water bath. The sample was vortexed once, followed by three 2 µL samples, and the absorbance was determined by measuring the absorbance from 250 nm to 650 nm on a Nanodrop. The samples were then allowed to stand for 1 hour without any stirring. After 60 minutes, 2 µL of sample was collected by immersing the tip of a pipette into the sensor solution to a depth of 50%. The absorption spectrum was repeated on this t=60 sample. This figure shows the absorption spectra for each of the four batches of the NE and MMP15 biosensors. Absorbance efficiency is shown in the table, calculated as absorbance at 260 nm per mg / ml of biosensor. Analysis showed that the absorbance of the sensor increased when it was ultrasonically agitated and dried to remove solvent during preparation. This observation was independent of the biosensor measured (see Figure 55). Example 41: Effect of Buffer Formulation on Signal-to-Noise Ratio Performance of Graphene Biosensors

[0518] The solution of the uPA nanosensor was prepared in water at a working concentration of 300 μg / ml (10×). It was ultrasonically treated in an ultrasonically vibrating water bath for 10 min. The nanosensor was diluted 1:10 in each of the different buffer formulations reported in Figure 14. 125 µl of the biosensor solution was repeatedly dispensed into black 96-well plates. 5 µL of serum was added and the plates were incubated at 37 °C for 60 min. The emitted fluorescence was read by excitation at 421 nm and emission at 650 nm on a VarioSkan Lux disc reader. Assays included a control in which 5 μl of an appropriate buffer was added instead of serum. The biosensor was incubated with two different normal healthy donor serum samples or pooled normal serum (PNS). Buffer 1 was the initial formulation. Protease activity was observed under all buffer conditions. S:N is reported in Figure 56 (uPA biosensor), showing the SM / AC ratio as an estimate of S:N. It was noted that the NaCl-containing buffer significantly reduced both background and serum signal. Lowering AC contributes to improved signal-to-noise ratio (SNR) performance of gNBS. Compared to the initial buffer, NaCl-containing buffers 7 and 8 significantly reduced AC performance. This resulted in improved SN performance. Example 42: Effects of Buffer Formulation and Heat Treatment on Signal-to-Noise Ratio Performance of Graphene Biosensors

[0519] Working solutions for 18 different biosensors were prepared in HEPES or MES buffers supplemented with 10 μM divalent cations and 155 mM NaCl. Samples were heat-treated at 65 °C for 12 min (MES baseline) or without heat treatment (HEPES baseline) and incubated at 37 °C for 90 min. After 90 min, the emitted fluorescence was evaluated on VarioSkan Lux, and S:N performance was calculated based on SM / AC. The effect of protocol variations on the addition of monobasic salt ions and more robust pH control using MES buffers showed an average reduction in AC to 0.10 RFU or lower on the VarioSkan platform. In addition to the lower AC, the more stable pH control produced an average 4-fold increase in calculated S:N values ​​compared to the HEPES baseline (see Figure 57). Example 43: Colloidal Stability of Graphene Biosensors

[0520] Solutions of NE or MMP15 graphene biosensors from batches 1, 2, 3, and 4, reflecting the KSU scheme, HEB replication of the KSU scheme, ultrasonic oscillation treatment scheme (batch 3), and ultrasonic oscillation treatment scheme for eliminating solvent contamination (batch 4), were prepared as working solutions in 5 mM MES pH 7.4 containing 155 mM NaCl. The sensors were prepared at a working concentration of 25 µg / mL. As a comparison, an Fe biosensor formulation was also prepared at a working concentration of 300 µg / mL using the same buffer. The working solutions were prepared and ultrasonically oscillated in a water bath at a rate of 1 min / mL. The sample was vortexed once, followed by three 2 µL samples, and the absorbance was determined by measuring the absorbance from 250 nm to 650 nm on a Nanodrop. The samples were then allowed to stand for 1 hour without any stirring. After 60 minutes, 2 µL of sample was collected by immersing the tip of a pipette into the sensor solution to a depth of 50%. Repeat the absorption spectrum on the sample at t=60. The performance is shown as the percentage change in absorbance efficiency at OD270 or OD650.

[0521] Analysis showed that the stability of the sensor improved when it was acoustically treated and dried to remove solvent during preparation, as reflected in a decrease in absorbance change over 60 minutes per batch. This observation was independent of the biosensor being measured (Figure 58). Example 44: Evaluation of signal-to-noise ratio in different preparation schemes of graphene biosensors

[0522] Solutions of NE, CTSB, or MMP12 graphene biosensors from batches 1, 2, 3, and 4, reflecting the KSU protocol, HEB replication of the KSU protocol, ultrasonic oscillation treatment protocol (batch 3), and ultrasonic oscillation treatment protocol for eliminating solvent contamination (batch 4), were prepared as working solutions in 5 mM MES at pH 7.4 containing 155 mM NaCl and 10 µM divalent cations. The sensors were prepared at a working concentration of 25 µg / mL. As a comparison, an Fe biosensor formulation was also prepared at a working concentration of 300 µg / m...

Claims

1. A graphene core particle comprising: at least two pristine graphene nanosheets having pristine graphene nanosheet surfaces, stacked to form a pristine graphene aggregate; and a coating covering at least 50% of the surface of the pristine graphene nanosheets, the coating comprising an organic and / or inorganic compound, wherein the organic and / or inorganic compound has a solubility in water at 25°C of at least 10 g per liter of water and is covalently linked to the pristine graphene, or electrostatically attached to the pristine graphene in water, wherein the organic and / or inorganic compound is linked to the pristine graphene nanosheets to form the coating, wherein the pristine graphene of the pristine graphene nanosheets has a carbon content of at least 99%, an oxygen content of less than 1%, and is pure such that the pristine graphene is free of foreign substances and impurities, wherein the organic and / or inorganic compound of the pristine graphene nanosheets and / or the coating is configured to provide functional groups capable of binding corresponding functional groups in an aqueous solution.

2. The graphene core particle as claimed in claim 1, wherein the average size of the graphene core particle is from 10 nm to 5000 nm, and the coating covers at least 85% of the surface of the graphene core particle.

3. The graphene core particle as claimed in claim 1, wherein the graphene core particle comprises 2 to 100 stacked original graphene nanosheets and the average particle size is 900 nm or less, preferably 350 nm or less, more preferably 250 nm or less.

4. The graphene core particle as claimed in claim 1, wherein the graphene core particle comprises 2 to 100 stacked original graphene nanosheets and the coating covers at least 90% of the surface of the graphene core particle.

5. The graphene core particle as claimed in any one of claims 1 to 4, wherein the graphene core particle comprises 5 to 25 stacked original graphene nanosheets.

6. The graphene core particle as claimed in any one of claims 1 to 4, wherein the graphene core particle comprises 5 to 7 stacked original graphene nanosheets.

7. The graphene core particles as claimed in any one of claims 1 to 4, wherein the stacked original graphene nanosheets form particles with an average size of 150 nm.

8. The graphene core particles as claimed in any one of claims 1 to 4, wherein the coating comprises an organic polymer.

9. The graphene core particles as claimed in claim 8, wherein the organic polymer comprises one or more of polyethyleneimine, polycaprolactone, polyethylene glycol, polyvinyl alcohol, polyvinylpyrrolidone, (meth)acrylate polymers and (meth)acrylamide polymers, polystyrene-carboxylic acid, and polystyrene-amine.

10. The graphene core particles as claimed in any one of claims 1 to 4, wherein the coating covers at least 99% of the surface of the original graphene nanosheet.

11. The graphene core particle as claimed in any one of claims 1 to 4, wherein the thickness of the coating is equal to or less than 18 nm to allow the attached targeting peptide sequence to be at least 2 nm.

12. The graphene core particles as claimed in any one of claims 1 to 4, wherein the coating has a thickness of 8 to 17 nm, 2 to 18 nm, or 5 to 15 nm.

13. A graphene biosensor configured to detect a target biomarker, the graphene biosensor comprising: a graphene core particle as claimed in any one of claims 1 to 12, and a detectable portion capable of emitting a fluorescent signal, the detectable portion being covalently linked to the graphene core particle via peptide bonds, the peptide bonds comprising a recognition sequence specific to the target biomarker, wherein a coating and the peptide bonds are configured to set a quenching distance between a pristine graphene nanosheet and the detectable portion, wherein the pristine graphene nanosheet quenches the fluorescent signal of the detectable portion, wherein the peptide bonds are further configured to set an emission distance between the pristine graphene nanosheet and the detectable portion after the recognition sequence is recognized by the target biomarker, wherein the pristine graphene nanosheet does not quench the fluorescent signal of the detectable portion, and wherein the target biomarker-specific peptide bonds and the detectable portion thus form a target-specific detectable component, the target-specific detectable component being configured to emit a detectable fluorescent signal after the peptide bonds are modified by the target biomarker.

14. The graphene biosensor of claim 13, wherein the recognition sequence is specific to a biomarker selected from: proteases, kinases, arginases, cytokines / chemokines, dioxygenases, esterases, and combinations thereof.

15. The graphene biosensor of claim 14, wherein the protease is selected from serine protease, aspartic protease, cysteine ​​protease, metalloproteinase, apoptotic protease, urokinase, cathepsin, and combinations thereof.

16. The graphene biosensor of any one of claims 13 to 15, wherein the biomarker indicates a condition selected from cancerous or precancerous cell activity, bacterial activity, infection, inflammation, and combinations thereof.

17. The graphene biosensor of any one of claims 13 to 15, wherein the detectable portion is selected from chromophores / luminescent organisms: organic dyes, inorganic dyes, fluoresces, phosphors, light-absorbing particles, quantum dots, combinations thereof, and metallized complexes thereof.

18. The graphene biosensor of claim 17, wherein the chromophore / luminescent body is an organic dye selected from the group consisting of: coumarin, pyrene, anthocyanin, BODIPY dye, benzene, N-methylcarbazole, erythrosine B, N-acetylglucosamine, 2,5-diphenyloxazole, rubrene, and N-(3-sulfopropyl)acridine.

19. The graphene biosensor of claim 17, wherein the chromophore / luminescent body is an inorganic dye selected from porphyrin, phthalocyanine, dihydroporphyrin, and metallized chromophores.

20. The graphene biosensor of claim 19, wherein the porphyrin is selected from tetracarboxyphenylporphyrin (TCPP) and Zn-TCPP.

21. The graphene biosensor of claim 17, wherein the chromophore / luminescent body is selected from the group consisting of phosphorescent dyes, fluorescein, rhodamine, and anthracene.

22. The graphene biosensor as claimed in claim 17, wherein the quantum dots are selected from CdSe / ZnS core / shell quantum dots, CdTe / CdSe core / shell quantum dots, CdSe / ZnTe core / shell quantum dots, and alloy semiconductor quantum dots.

23. The graphene biosensor of any one of claims 13 to 15, wherein the coating comprises a polymer monolayer selected from polyethyleneimine, polycaprolactone, polyvinyl alcohol, polyvinylpyrrolidone, (meth)acrylate polymers and (meth)acrylamide polymers, polystyrene-carboxylic acid, polystyrene-amine, dextrose, pachymonopolysaccharide, chitosan, alginate, cellulose and hyaluronic acid, and mixtures or copolymers thereof.

24. The graphene biosensor of any one of claims 13 to 15, wherein the size of the graphene core particles is from about 50 nm to about 500 nm.

25. The graphene biosensor of any one of claims 13 to 15, comprising a plurality of detectable components connected to the graphene core particles via respective peptide bonds.

26. The graphene biosensor of any one of claims 13 to 15, comprising at least two distinct detectable portions connected to the graphene core particle by respective peptide bonds, each peptide bond having a recognition sequence specific to different target biomarkers.

27. A multi-graphene biosensor comprising: a graphene core particle as claimed in any one of claims 1 to 12 and a plurality of target-specific detectable components connected to the graphene core particle, wherein each target-specific detectable component is formed of a detectable portion capable of emitting a fluorescent signal, the detectable portion being connected to a peptide bond comprising a recognition sequence having specificity for a target biomarker, wherein the peptide bond of each target-specific detectable component is configured to provide a quenching distance between the detectable portion and the graphene core particle in combination with a coating of the graphene core particle, wherein the original graphene nanosheet quenches the fluorescent signal of the detectable portion, and wherein the recognition sequence for a target biomarker in each detectable component is configured to specifically detect the target biomarker, and the detectable component is selected to specifically detect a plurality of different target biomarkers.

28. The multiple graphene biosensor as claimed in claim 27, comprising up to 10 target-specific detectable components, each detectable component providing a detectable portion detectable across the entire ultraviolet / visible / near-infrared spectral range.

29. The multiple graphene biosensor as claimed in claim 27 or 28, comprising a target-specific detectable component oligopeptide with a density of 1.6 ± 0.2 × 10⁻⁵ moles / gram.

30. A composition comprising one or more graphene core particles as described in any one of claims 1 to 12 in combination with suitable additives and / or one or more graphene biosensors as described in any one of claims 13 to 29.

31. The composition as claimed in claim 30, wherein the composition is a pharmaceutical composition and the adjuvant is a pharmaceutically acceptable adjuvant.

32. A method for detecting the activity of a biomarker in a biological sample, comprising: Contact a biological sample with any one of claims 13 to 29 to generate a reaction solution; And to detect changes in the reaction solution.

33. The method as described in claim 32, further comprising: The reaction solution is exposed to an excitation light source; And to detect changes in the absorption or emission spectrum of the detectable portion as a function of the activity of the biomarker in the sample.

34. The method of claim 32, wherein the change includes a blue shift of the maximum absorption or emission value of the detectable portion after the contact relative to the absorption or emission spectrum before the contact.

35. The method of any one of claims 32 to 34, wherein the change comprises the appearance of a new visible color or luminescent band relative to the absorption or emission spectrum of the detectable portion prior to the contact.

36. The method of any one of claims 32 to 34, wherein the contact comprises culturing the sample with the biosensor for a period of less than 90 minutes, preferably less than 60 minutes.

37. The method of any one of claims 32 to 34, wherein the contact comprises providing a microplate comprising a plurality of micropores, one or more of the micropores comprising a plurality of said biosensors distributed therein, and adding the biological sample into the micropores to generate a respective reaction solution in each of the micropores.

38. The method of any one of claims 32 to 34, wherein the detection is preceded by heating the sample and / or test solution.

39. A system for detecting the activity of a biomarker in a biological sample, the system comprising a biosensor as described in any one of claims 13 to 29 and reagents for simultaneous or sequential use in a method for detecting the activity of a biomarker in a biological sample as described in any one of claims 32 to 38.

40. A method for multiplex detection of the activity of multiple biomarkers in a biological sample, the method comprising: A biological sample is brought into contact with a multiple biosensor as claimed in any one of claims 27 to 29, the multiple biosensor being configured to provide detectable components specific to a variety of the biomarkers, and the contact is made to produce a reaction solution. And to detect changes in the reaction solution, and / or to contact the biological sample with a multiple biosensor as claimed in any one of claims 13 to 26, each of the multiple biosensors providing a peptide bond specific to one of the multiple biomarkers.

41. The method of claim 40, wherein the detection is preceded by heating the sample and / or test solution.

42. The method as claimed in claim 40 or 41, wherein the sample comprises multiple samples and the contact is performed in parallel with the multiple samples.

43. A system for multiplex detection of the activity of multiple biomarkers in a biological sample, the system comprising: a multiplex biosensor as described in any one of claims 27 to 29 and / or a biosensor each providing a peptide bond specific to one of the multiple biomarkers; and multiple reagents for simultaneous or sequential use in a method for multiplex detection of the activity of biomarkers in a biological sample as described in any one of claims 40 to 42.

44. A method for preparing graphene core particles as described in any one of claims 1 to 12, the method comprising: Provide at least two pristine graphene nanosheets having pristine graphene surfaces, stacked together to form pristine graphene aggregates; And coating the graphene surface with a coating of organic and / or inorganic molecules.

45. A method for preparing a biosensor as described in any one of claims 13 to 26, the method comprising providing graphene core particles as described in any one of claims 1 to 12 and attaching a detectable portion to a graphene surface via peptide bonds.

46. ​​The method of claim 45, wherein the connection is prior to the dispersion of graphene core particles as described in any one of claims 1 to 12, and the connection is performed by contacting the dispersed graphene core particles with the detectable component comprising the detectable portion connected to the peptide bond.

47. A system for preparing a biosensor as claimed in any one of claims 13 to 25, the system comprising a graphene core particle as claimed in any one of claims 1 to 12, a peptide bond, a detectable portion, and a reagent for attaching the detectable portion to the surface of the graphene core particle via the peptide bond.

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