Characteristic Evaluation Method

Abstract

The present invention relates to a method for providing either quantitative information or qualitative information regarding the composition of a nanomaterial, such as a graphene-based material, by a process of contacting a plurality of portions of a sample of the nanomaterial with a plurality of responsive probes, measuring each characteristic of the responsive probes in the presence of the nanomaterial to provide a plurality of characteristic measurement values, and processing the plurality of characteristic measurement values to provide qualitative or quantitative information. The present invention also relates to a kit for carrying out the aforementioned method.

Description

Technical Field

[0001] The present invention relates to a method for providing information on the composition of a nanomaterial (such as a graphene-based material) using an array of responsive probes.

Background Art

[0002] “Graphene-based material” (GBM) samples such as graphene oxide (GO) contain flakes having specific profiles of different lateral dimensions, number of layers, defects, and degree and type of functionalization. Different manufacturing methods result in samples with different profiles and thus different properties (Non-Patent Document 1: P. Boggild, Nature, 2018, 562, 502).

[0003] There has long been anecdotal evidence that samples sold under the same label, such as “graphene” and “graphene oxide”, can vary widely, even among manufacturers or within batches from the same manufacturer. In one study, the structures of “graphene” samples taken from over 60 commercial products were analyzed to clarify the scale of this problem. There was little single-layer graphene, and large variations in size, the percentage of sp 2 -hybridized carbon (%), number of layers, etc. Some even had a carbon content of less than 60% (Non-Patent Document 2: A. P. Kauling, A. T. Seefeldt, D. P. Pisoni, R. C. Pradeep, R. Bentini, R. V. B. Oliveira, K. S. Novoselov and A. H. C. Neto, Advanced Materials, 2018, 30, 1803784). Such lack of reproducibility is an obstacle to realizing the promise of graphene / GBM.

[0004] Currently, there is an international standard (ISO) for graphene (ISO / TS 21356-1. Nanotechnologies - Structural Characterization of graphene - Part 1: Graphene from powders and dispersions, 2021), but for other GBMs (such as GO), it is still insufficient. The evaluation of structural and chemical properties called the "Gold standard" depends on the situation (Non-Patent Document 3: National Physical Lab, Good Practice Guide No.145: Characterisation of the Structure of Graphene), and there is a tendency to use a combination of SEM, TEM, AFM, XPS, Raman spectroscopy, and elemental analysis. These are expensive, time-consuming, and require sample preparation, data interpretation, expertise, and specialized equipment. This poor accessibility (or unavailability) is reflected in how much time has been spent on large-scale investigations regarding the supply of graphene despite the social interest.

[0005] An ideal QC method is one that can provide sufficient information necessary for decision-making inexpensively, quickly, with little need for infrastructure, and can be easily implemented at the engineer level. The dispersion method is attractive considering concerns about the safety of handling nanomaterial powders.

[0006] Several approaches have been considered, but each has problems. Size measurement by dynamic light scattering (DLS) is available and rapid, but it is not suitable for non-spherical species, and reproducibility may be a problem in an environment outside of experts. Gas adsorption (BET) complements detailed characterization but is limited to surface area measurement. Electrochemical methods have also been considered, but there has been little progress so far.

[0007] The objective of some embodiments of the present invention is generally to overcome some of the problems as described above related to GBM characterization and nanomaterial characterization.

Prior Art Documents

Non-Patent Literature

[0008]

Non-Patent Literature 1

Non-Patent Literature 2

Non-Patent Literature 3

Summary of the Invention

[0009] According to a first aspect of the present invention, a method for providing information on the composition of a nanomaterial (e.g., a graphene-based material (GBM)) is provided, the method comprising contacting a plurality of portions of a sample of the nanomaterial with a plurality of responsive probes, measuring the characteristics of each of the responsive probes in the presence of the nanomaterial to provide a plurality of characteristic measurement values, and processing the plurality of characteristic measurement values to provide qualitative or quantitative information regarding the composition of the nanomaterial including.

[0010] In one embodiment, the method comprises contacting a plurality of portions of at least two samples of the nanomaterial with a plurality of responsive probes, measuring the characteristics of each of the responsive probes in the presence of at least two samples of the nanomaterial to provide a plurality of characteristic measurement values, and A step of processing a plurality of characteristic measurement values to provide qualitative or quantitative information regarding the composition of at least one of at least two samples of a nanomaterial is included. The at least two samples may be at least three samples (e.g., at least four, or at least five samples).

[0011] Probe Typically, when a plurality of portions of a sample of a nanomaterial (e.g., GBM) are contacted with a plurality of responsive probes, each portion of the sample of the nanomaterial is contacted with a probe of a single identity. However, at least a portion of a sample of a nanomaterial (e.g., GBM) may be one that is contacted with a plurality of responsive probes having different identities, for example, each portion of the sample of the nanomaterial may be one that is contacted with a plurality of responsive probes having different identities.

[0012] The responsive probe may be a spectroscopic probe. The spectroscopic probe may be an infrared (IR) probe, an ultraviolet-visible (UV / Vis) probe, a nuclear magnetic resonance (NMR) probe, a Raman probe, an X-ray probe, or a fluorescence probe. The responsive probe may be a UV / Vis probe. The responsive probe may be a fluorescence probe. The responsive probe may be an electrochemical probe.

[0013] The measurement characteristics of each responsive probe in the presence of a nanomaterial (e.g., GBM) are determined by the identity of the responsive probe used. Based on the responsive probe used, one of ordinary skill in the art understands what characteristics should be measured. When spectroscopic probes are used, the characteristics to be measured are the spectroscopic characteristics associated with those spectroscopic probes. For example, when the responsive probe is a fluorescent probe, the characteristic to be measured for the fluorescent probe in the presence of the nanomaterial is fluorescence. The characteristic to be measured may be the fluorescence intensity at a specific wavelength. When the responsive probe is an ultraviolet-visible probe, the characteristics to be measured for the probe in the presence of the nanomaterial are the absorbance and / or emission of light in the ultraviolet-visible region. The characteristic to be measured may be the intensity of ultraviolet-visible light absorbed or emitted at a specific wavelength. The measurement wavelengths for each part of the sample may be different. This depends on the probe being tested. Multiple wavelengths may be measured for each part.

[0014] Spectroscopic characteristics, such as fluorescence and UV / Vis, can be measured by techniques well known in the art. In recent techniques, smart devices are used to measure electromagnetic waves, such as ultraviolet light. Accordingly, the spectroscopic characteristics of a responsive probe, such as fluorescence or UV / Vis, in the presence of a nanomaterial may be measured by capturing or photographing the responsive probe in the presence of the nanomaterial with a smart device, such as the camera of a smartphone, and processing the captured or photographed image to provide multiple characteristic measurements. The captured or photographed image may be processed by a fog network.

[0015] When the responsive probe is an electrochemical probe, the electrochemical characteristics of the electrochemical probe in the presence of the nanomaterial are measured. Electrochemical characteristics, such as electrochemical potential or electrochemical impedance, can be measured by techniques well known in the art.

[0016] At least one of the responsive probes may not form a covalent bond with the nanomaterial. Any of the responsive probes may not form a covalent bond with the nanomaterial.

[0017] The plurality of responsive probes may consist of two or more probes. The plurality of responsive probes may consist of three or more probes. The plurality of responsive probes may consist of four or more probes. The plurality of responsive probes may consist of five or more probes.

[0018] The responsive probe may be a fluorescent probe. In embodiments where the responsive probe is a fluorescent probe, the probe typically includes a plurality (e.g., at least three) of aromatic or heteroaromatic rings. The array of probes may include amphiphilic probes. The array of probes may comprise probes that include an aromatic moiety and a charged or bipolar moiety. The aromatic moiety may include a plurality (e.g., at least three) of fused aromatic or heteroaromatic rings, such as pyrene. The charged moiety may be sulfate, phosphate, secondary phosphate, quaternary ammonium cation, acetate, or nitrate. The charged moiety may be sulfate, phosphate, or a quaternary ammonium cation. It will be appreciated that the probes may be in the form of a salt before contacting a plurality of portions of a sample of the nanomaterial (e.g., GBM) with a plurality of responsive probes having charged sites. Thus, the aforementioned charged moiety may be paired with a suitable counterion, such as the counterion of sodium or bromide. The probe may further include a hydrocarbon spacer that links the aromatic moiety to the charged or bipolar moiety. The hydrocarbon spacer is alkylene, such as C1-C6-alkylene, and may optionally be substituted with one to four substituents independently selected from halo, OH, and C1-C4-alkyl, where chemically possible. The hydrocarbon spacer may be alkylene, such as C1-C6-alkylene optionally substituted with one to four OH groups, where chemically possible. Each probe in the array of probes may be as defined above.

[0019] The plurality of probes may include at least one probe selected from TIFF2025518456000002.tif88161 TIFF2025518456000003.tif66160.

[0020] The plurality of probes may include any two or more probes selected from TIFF2025518456000004.tif142161.

[0021] The plurality of probes may include at least one probe selected from TIFF2025518456000005.tif119131.

[0022] The probe array may include any two or more probes selected from TIFF2025518456000006.tif117130.

[0023] The above probes are exemplary fluorescent probes.

[0024] Polymeric probes such as polymer probes, conjugated polymer probes and biopolymer probes, two-dimensional (or 2D) nanomaterial probes (such as fluorescent carbon dots) and metal complex probes may also be used in the method of the present invention.

[0025] Nanomaterial In some embodiments, the nanomaterial is a two-dimensional nanomaterial, i.e., a nanosheet. The two-dimensional nanomaterial may be a graphene-based material (GBM) such as graphene. Graphene may be pristine graphene, oxidized graphene, reduced oxidized graphene, functionalized graphene, or graphene nanoplatelets. Graphene may be oxidized graphene or reduced oxidized graphene. Graphene may be oxidized graphene.

[0026] Since the monolayer of graphene has an atomic thickness of one atom, it can be described as a single atomic layer (referred to as a "layer"). Generally, the average thickness of graphene is less than 10 layers. Graphene may be monolayer graphene. Graphene may also be multilayer graphene. Graphene may have an average thickness of 1 to 3 layers, for example, 1 to 5 layers. Graphene may have an average thickness exceeding 5 layers.

[0027] The two-dimensional nanomaterial may be graphene or a two-dimensional polymer, such as a crystalline two-dimensional polymer.

[0028] The two-dimensional nanomaterial may be a non-carbon-containing two-dimensional material. Exemplary non-carbon-containing two-dimensional materials include borophene, germanene, silicene, stanine, plumbene, phosphorene, antimonene, bismuthine, transition metal dichalcogenides (TMDCs), hexagonal boron nitride (h-BN), black phosphorus (BP), and two-dimensional metal oxides.

[0029] In some embodiments, the nanomaterial is a one-dimensional nanomaterial, such as a nanotube, nanowire, and nanorod. The one-dimensional nanomaterial may be a carbon nanotube. The carbon nanotube may be a single-layer or multi-layer carbon nanotube. The carbon nanotube may be a pristine carbon nanotube or a functionalized carbon nanotube. For example, the carbon nanotube may be a carbon nanotube functionalized with a metal, halogen, or organic functional group. Also, the one-dimensional nanomaterial may be a graphene nanoribbon or a carbon nanobud.

[0030] In some embodiments, the nanomaterial is a zero-dimensional nanomaterial, such as a quantum dot. The zero-dimensional nanomaterial may be a carbon-containing zero-dimensional nanomaterial, such as a carbon nanodot and a closed (or closed; closed) fullerene. The closed fullerene may be a buckminsterfullerene.

[0031] Part A portion of the sample of the nanomaterial (e.g., GBM) may be a dispersion of the nanomaterial. The dispersion may be an aqueous dispersion. The dispersion may be a dispersion in an organic solvent. The organic solvent may be selected from the group including ether, methanol, ethanol, chloroform, carbon tetrachloride, benzene, tetrahydrofuran, dimethylacetamide, N-methyl-2-pyrrolidone, and DMSO. The dispersion may be formed by subjecting the nanomaterial (e.g., GBM) to ultrasonic treatment in a solvent, such as water or the aforementioned organic solvent. The dispersion may be formed by subjecting the nanomaterial (e.g., GBM) to ultrasonic treatment in a solvent for less than 60 seconds, for example, between 10 and 50 seconds.

[0032] Thus, the step of contacting a plurality of portions of the sample of the nanomaterial (e.g., GBM) with a plurality of responsive probes may include adding a plurality of responsive probes to a plurality of dispersions of the sample of the nanomaterial.

[0033] The concentration of the nanomaterial (e.g., GBM) in the dispersion may be 0.5 mg / mL or less, for example, 0.2 mg / mL or less. The concentration of the nanomaterial in the dispersion may be 0.07 to 0.13 mg / mL. The concentration of the nanomaterial in the dispersion may be 0.1 mg / mL or less, for example, 0.05 mg / mL or less. The concentration of the nanomaterial in the dispersion may be 0.02 mg / mL or less.

[0034] After adding a plurality of responsive probes to a plurality of dispersions of the sample of the nanomaterial (e.g., GBM), the concentration of the probes in the dispersion may be 0.5 mg / mL or less, for example, 0.2 mg / mL or less. The concentration of the probes in the dispersion may be 0.07 to 0.13 mg / mL. The concentration of the probes in the dispersion may be 0.05 to 0.15 mM.

[0035] The dispersion may contain a buffer solution. The buffer solution may be a phosphate buffer solution. The buffer solution may be a neutral buffer solution. The buffer solution may maintain the pH of the dispersion in the range of 6 to 8, for example, 6.5 to 7.5.

[0036] The pH of the dispersion may be in the range of 6 to 8, for example, 6.5 to 7.5. The pH of the dispersion may be about 7.0.

[0037] The dispersion may contain a total ion strength adjustment buffer (TISAB).

[0038] The inventors have found that some dispersion conditions, such as pH, ionic strength, probe concentration, dispersion solvent, number of probe identities per dispersion, and the presence of non-responsive competitive binders, affect the interaction between the responsive probe and the nanomaterial (e.g., GBM). Therefore, by changing such dispersion conditions (e.g., pH and / or ionic strength), it becomes possible to more easily obtain information regarding the composition of the nanomaterial.

[0039] Therefore, two or more dispersions (i) the concentration of the nanomaterial (e.g., GBM), (ii) the concentration of the responsive probe, (iii) the solvent of the dispersion, (iv) the pH of the dispersion, (v) the ionic strength of the dispersion, and / or (vi) the absence or presence of a non-responsive competitive binder, and optionally the concentration of the non-responsive competitive binder if present may vary in at least one condition selected from.

[0040] Each dispersion (i) the concentration of the nanomaterial (e.g., GBM); (ii) the concentration of the responsive probe (iii) the solvent of the dispersion (iv) the pH of the dispersion (v) the ionic strength of the dispersion; and / or (vi) the absence or presence of a non-responsive competitive binder, and optionally the concentration of the non-responsive competitive binder if present It may also vary under at least one condition selected from the following.

[0041] At least two of the dispersions may differ in the identity of the responsive probe, and at least two of the dispersions may differ in at least one condition selected from (i) to (vi). Each solution may differ in the identity of the responsive probe, and each solution may differ in at least one condition selected from (i) to (vi). Each dispersion may contain responsive probes of the same identity, and at least two of the dispersions may differ in at least one condition selected from (i) to (vi). Each dispersion may contain responsive probes of the same identity, and each dispersion may differ in at least one condition selected from (i) to (vi).

[0042] Two or more portions (e.g., dispersions) of the nanomaterial sample may be in contact with probes having the same identity but different pH and / or ionic strength. At least two dispersions may have different pH values although there is no difference in the identity of the responsive probe. None of the dispersions differ in the identity of the responsive probe, but each dispersion may have a different pH. The pH of the dispersions is independently selected to be in the range of 6 to 8, for example, in the range of 6.5 to 7.5. At least one of the dispersions (e.g., each dispersion) may contain a buffer. The buffer solution may be a phosphate buffer solution. The buffer may be a neutral buffer. The buffer may maintain the pH of the dispersion in the range of 6 to 8, for example, in the range of 6.5 to 7.5.

[0043] At least one of the dispersions may contain two responsive probes having different identities.

[0044] The dispersion may contain dispersion A and dispersion B, where dispersion A contains two responsive probes with different identities and dispersion B contains two responsive probes with different identities. One of the responsive probes in dispersion A may have the same identity as one of the responsive probes in dispersion B (and the other responsive probe in dispersion A has a different identity from the other responsive probe in dispersion B). Alternatively, none of the responsive probes in dispersion A may have the same identity as any of the responsive probes in dispersion B.

[0045] At least one of the dispersions may comprise a non-responsive competing binder. The dispersion may contain dispersion C and dispersion D, where dispersion C contains a responsive probe and dispersion D has a responsive probe with a similar identity to the probe in dispersion C and a non-responsive competing binder. The dispersion may contain dispersion E and dispersion F, where dispersion E contains a probe and a non-responsive competing binder and dispersion F contains a probe with a different identity from the probe in dispersion E and a non-responsive competing binder with a similar identity to the non-responsive competing binder in dispersion E.

[0046] Typically, after contacting a plurality of portions of a sample of the nanomaterial with a plurality of responsive probes, the plurality of portions of the sample of the nanomaterial are not washed (e.g., may remain in a dispersed state) before measuring the characteristics of each of the responsive probes in the presence of the nanomaterial.

[0047] When measuring the characteristics of each responsive probe in the presence of the nanomaterial, some of the responsive probes may not interact with the nanomaterial.

[0048] The method may include measuring the respective properties of the responsive probes in the presence of the nanomaterial in order to provide a plurality of characteristic measurement values within less than 1 hour after bringing a plurality of portions of the sample of the nanomaterial into contact with a plurality of responsive probes. The method may include measuring the respective properties of the responsive probes in the presence of the nanomaterial in order to provide a plurality of characteristic measurement values within less than 30 minutes (e.g., less than 15 minutes) after bringing a plurality of portions of the sample of the nanomaterial into contact with a plurality of responsive probes. Typically, the method includes measuring the respective properties of the responsive probes in the presence of the nanomaterial in order to provide a plurality of characteristic measurement values within 5 to 15 minutes after bringing a plurality of portions of the sample of the nanomaterial into contact with a plurality of responsive probes.

[0049] Processing of characteristic measurement values To provide either qualitative or quantitative information regarding the composition of a nanomaterial (e.g., GBM), the process of processing a plurality of characteristic measurements may consist of the process of deconvoluting (or analyzing; deconvolute) the plurality of characteristic measurements to provide either qualitative or quantitative information regarding the composition of the nanomaterial. By processing the plurality of characteristic measurements, comparable plottable data (e.g., plottable in a 1D, 2D, or 3D plot) that can provide either qualitative or quantitative information regarding the composition of the nanomaterial may be generated. The plurality of characteristic measurements may be processed by machine learning. The machine learning may be supervised, unsupervised, or semi-supervised. The machine learning may be supervised machine learning, such as linear regression. The machine learning may be unsupervised machine learning. The machine learning may be random forest-based regression or a neural network. Linear dimensionality reduction techniques include principal component analysis (PCA), factor analysis (FA), linear discriminant analysis (LDA), truncated singular value decomposition (SVD), etc. The linear dimensionality reduction technique may be unsupervised. Preferably, the plurality of characteristic measurements are processed by PCA to provide qualitative or quantitative information (e.g., qualitative information). The plurality of characteristic measurements may be processed by random forest-based regression to provide quantitative information. Processing the plurality of characteristic measurements to provide either qualitative or quantitative information regarding the composition of a nanomaterial (e.g., GBM) may be performed on a smart device, such as a smartphone.

[0050] To clarify, when processed, the plurality of characteristic measurements, collectively, provide either qualitative or quantitative information regarding the composition of the nanomaterial. The qualitative or quantitative information provided is typically information that cannot be provided when only a portion of a sample of the nanomaterial is contacted with a single responsive probe and the characteristics of the responsive probe are measured in the presence of the nanomaterial to provide a single characteristic measurement value.

[0051] Machine learning model Machine learning (or a machine learning algorithm) is applied to a dataset (i.e., training data) to create a machine learning model. The machine learning model can then be applied to a dataset (i.e., test data) to make predictions regarding the test data (based on the learning data).

[0052] In an embodiment, by applying a machine learning model to a plurality of characteristic measurement values, the plurality of characteristic measurement values are processed, and either qualitative information or quantitative information regarding the composition of the nanomaterial is provided. In this scenario, the plurality of characteristic measurement values are test data, i.e., a plurality of test data characteristic measurement values.

[0053] The machine learning model may be created by applying machine learning to characteristic measurement values of a plurality of training (or training) data. The plurality of training data characteristic measurement values may be provided by contacting a plurality of portions of a sample of the nanomaterial with a plurality of responsive probes and measuring the respective characteristics of the responsive probes in the presence of the nanomaterial to provide the plurality of training data characteristic measurement values. Then, machine learning is applied to the plurality of training data characteristic measurement values, and a machine learning model is created.

[0054] Accordingly, the method of the first aspect of the present invention is a step of contacting a plurality of portions of a sample of the nanomaterial with a plurality of responsive probes, a step of measuring the respective characteristics of the responsive probes in the presence of the nanomaterial to provide characteristic measurement values of a plurality of training data, a step of applying machine learning to the characteristic measurement values of the plurality of training data to create a machine learning model, a step of contacting a plurality of portions of a sample of the nanomaterial with a plurality of responsive probes, a step of measuring the characteristics of each responsive probe in the presence of the nanomaterial to provide a plurality of test data characteristic measurement values, and A step of applying a machine learning model to a plurality of test data characteristic measurement values to provide qualitative or quantitative information regarding the composition of a nanomaterial may be included.

[0055] A plurality of portions of a sample of a nanomaterial that ultimately provide a plurality of training data characteristic measurement values may have known values for variables that can define the composition of the nanomaterial. A plurality of portions of a sample of a nanomaterial that ultimately provide a plurality of test data characteristic measurement values may have unknown values for variables that can define the composition of the nanomaterial.

[0056] Qualitative information In some embodiments, by processing a plurality of characteristic measurement values, qualitative information regarding the composition of a nanomaterial (e.g., GBM) is provided. The qualitative information may be relative qualitative information. The qualitative information may be the degree of similarity of the composition of the nanomaterial to at least one other sample of the nanomaterial. When a plurality of portions of at least two samples (e.g., at least three, four, or five samples) of a nanomaterial are contacted with a plurality of responsive probes, the qualitative information may be the degree of similarity of the composition of at least one of the samples to other samples of the nanomaterial.

[0057] While not wishing to be bound by theory, the composition of the nanomaterial (e.g., GBM) is thought to affect the degree to which the responsive probe interacts with the nanomaterial sample. This results in a shift in a measurable property of the probe, such as fluorescence intensity at a particular wavelength, with generally a greater interaction resulting in a larger shift and a weaker interaction resulting in a smaller shift. However, different probes do not respond in the same way to differences in the composition of the nanomaterial. Thus, when an array of probes contacts multiple portions of a nanomaterial sample, a “fingerprint” of different shifts in measurable properties is generated. Similarly, a given probe and nanomaterial may interact differently under different conditions, such as pH or ionic strength. Again, the resulting variations differ depending on the composition of the nanomaterial. Thus, the fingerprint can be generated by contacting the probes with the nanomaterial under different conditions, even using a smaller number of probes. Then, multiple property measurements may be processed, e.g., by PCA, to qualitatively compare the compositions of different nanomaterial (e.g., GBM) samples.

[0058] Accordingly, the method of the present invention may be used to determine how similar batches of different nanomaterials (e.g., GBM) are to one another, e.g., batches manufactured by a particular method or obtained from a particular supplier.

[0059] Quantitative information In some embodiments, processing the multiple property measurements provides quantitative information regarding the composition of the nanomaterial (e.g., GBM).

[0060] The quantitative information is an estimated value for a variable that can define the composition of the nanomaterial (e.g., GBM), and the estimated value may be provided by determining the degree of similarity of the composition of the nanomaterial to at least one sample of the nanomaterial having a known value for that variable.

[0061] The quantitative information is an estimated value for a variable that can define the composition of a nanomaterial (e.g., GBM), and the estimated value may be provided by determining the degree of similarity of the composition of the nanomaterial for at least two samples of the nanomaterial having known values for that variable.

[0062] The method may be a method for providing an estimated value of a variable that can define the composition of a nanomaterial having an unknown value for that variable. The method contacting a plurality of portions of at least three samples of the nanomaterial with a plurality of responsive probes, wherein at least two samples of the nanomaterial have different known values for the variable and at least one sample of the nanomaterial has an unknown value for the variable; measuring each characteristic of the responsive probes in the presence of at least three samples of the nanomaterial to provide a plurality of characteristic measurement values; and processing the plurality of characteristic measurement values to provide quantitative information for at least one sample of the nanomaterial having an unknown value for the variable, where the quantitative information is an estimated value for the variable and the estimated value is provided by determining the degree of similarity of the composition of at least one sample of the nanomaterial having an unknown value for the variable and the composition of at least two samples of the nanomaterial having known values for that variable may comprise.

[0063] The method contacting a plurality of portions of at least two samples of the nanomaterial with a plurality of responsive probes, wherein at least two samples of the nanomaterial have different known values for the variable; measuring each characteristic of the responsive probes in the presence of at least two samples of the nanomaterial to provide a plurality of characteristic measurement values; processing the plurality of characteristic measurement values to provide reference data regarding the composition of at least two samples of the nanomaterial; and To provide an estimated value of a variable of a nanomaterial, a step of comparing data on the composition of the nanomaterial (having an unknown value of the variable) with reference data may be included.

[0064] The method may include a step of contacting a plurality of portions of at least three samples of the nanomaterial with a plurality of responsive probes, wherein at least three samples of the nanomaterial have different known values for the variable. The method may include a step of contacting a plurality of portions of at least four samples of the nanomaterial with a plurality of responsive probes, wherein at least four samples of the nanomaterial have different known values for the variable.

[0065] To provide an estimated value, the reference data may be plotted so as to provide a linear trend line, and the data on the composition of the nanomaterial may be compared with the linear trend line.

[0066] Variables that can define the composition of a nanomaterial (e.g., GBM) include, but are not limited to, the surface area of the nanomaterial, the degree of functionalization of the nanomaterial (e.g., oxygen content), the sp 3 degree of hybridization, the amount of defects in the nanomaterial, and the thickness of the nanomaterial. The nanomaterial may be graphene oxide and the variable may be the degree of oxygen-containing group modification (e.g., the degree of acetylation of hydroxy groups).

[0067] Kit According to a second aspect of the present invention, a kit is provided, the kit comprising a plurality of responsive probe solutions, wherein (i) the identity of the responsive probes, (ii) the solvent of the solution, (iii) the pH of the solution, and / or (iv) the ionic strength of the solution are different for at least one characteristic selected therefrom, a plurality of responsive probe solutions, and Software for processing a plurality of measurement values of the characteristics of each responsive probe to provide either qualitative or quantitative information about the composition of the nanomaterial (e.g., GBM), or a link (e.g., hyperlink), in the presence of the nanomaterial is included.

[0068] In one embodiment, the kit comprises a plurality of responsive probe solutions, (i) the identity of the responsive probe, (ii) the solvent of the solution, (iii) the pH of the solution, (iv) the ionic strength of the solution, (v) the concentration of the responsive probe, and / or (vi) the absence or presence of a non-responsive competitive binder, and optionally the concentration of the non-responsive competitive binder if present wherein at least one characteristic selected from the group consisting of is different among the plurality of responsive probe solutions, and software for processing a plurality of measurement values of the characteristics of each responsive probe in the presence of the nanomaterial (e.g., GBM) to provide qualitative or quantitative information about the composition of the nanomaterial, or a link (e.g., hyperlink) is included.

[0069] The plurality of responsive probe solutions may consist of two or more solutions. The plurality of responsive probe solutions may consist of three or more solutions. The plurality of responsive probe solutions may consist of four or more solutions. The plurality of responsive probe solutions may consist of five or more solutions.

[0070] At least two solutions may differ in the identity of the responsive probe. Each solution may differ in the identity of the responsive probe.

[0071] At least two solutions do not differ in the identity of the responsive probe, but the solutions may differ in at least one property selected from (ii) the solvent of the solution, (iii) the pH of the solution, and / or (iv) the ionic strength of the solution. Any of the solutions do not differ in the identity of the responsive probe, but the solutions may differ in at least one property selected from (ii) the solvent of the solution, (iii) the pH of the solution, and / or (iv) the ionic strength of the solution.

[0072] The above solutions may include solution A and solution B. Solution A contains two responsive probes with different identities, and solution B contains two responsive probes with different identities. One of the responsive probes in solution A may have the same identity as one of the responsive probes in solution B (and the other responsive probe in solution A has a different identity from the other responsive probe in solution B). Alternatively, none of the responsive probes in solution A may have the same identity as any of the responsive probes in solution B.

[0073] At least one of the solutions may contain a non-responsive competing binder. The solution may include solution C and solution D, where solution C contains a responsive probe and solution D contains a responsive probe with the same identity as the probe in solution C and a non-responsive competing binder. The solution may include solution E and solution F, where solution E contains a probe and a non-responsive competing binder, and solution F contains a probe with a different identity from the probe in solution E and a non-responsive competing binder with the same identity as the non-responsive competing binder in solution E.

[0074] At least two of the solutions may have different pH values, although the identity of the responsive probe is the same. Each solution may have a different pH, although there is no difference in the identity of the responsive probe among the solutions. The pH of the solutions may be independently selected to be in the range of 6 to 8, for example, in the range of 6.5 to 7.5. At least one of the solutions (e.g., each solution) may contain a buffer. The buffer solution may be a phosphate buffer solution. The buffer solution may be a neutral buffer solution. The buffer may maintain the pH of the solution in the range of 6 to 8, for example, in the range of 6.5 to 7.5.

[0075] The solution may be an aqueous solution. The solution may contain an organic solvent. The organic solvent may be selected from the group consisting of ether, methanol, ethanol, chloroform, carbon tetrachloride, benzene, tetrahydrofuran, dimethylacetamide, N-methyl-2-pyrrolidone, and DMSO.

[0076] At least one of the solutions (e.g., each solution) may contain a total ion strength adjustment buffer (TISAB).

[0077] The kit may include a solution containing a non-responsive competitive binder.

[0078] The responsive probe may be as defined above in the first aspect of the present invention.

[0079] The software may be software for deconvoluting a plurality of measured values of each characteristic of the responsive probe in the presence of a nanomaterial (e.g., GBM) in order to provide either qualitative information or quantitative information regarding the composition of the nanomaterial. The software for processing a plurality of characteristic measurement values generates plotable data (e.g., plotable in a 1D, 2D, or 3D plot) that can be used to provide either qualitative information or quantitative information regarding the composition of the nanomaterial.

[0080] The software may be machine learning software. The machine learning may be with monitoring, without monitoring, or with semi - monitoring. The machine learning may be machine learning with monitoring, for example, it may be linear regression software. The machine learning may be machine learning without monitoring. The machine learning may be a random forest - based regression or a neural network. The software may be dimensionality reduction technology software, for example, linear dimensionality reduction technology software. Linear dimensionality reduction technologies include principal component analysis (PCA), factor analysis (FA), linear discriminant analysis (LDA), and truncated singular value decomposition (SVD). The linear dimensionality reduction technology may be without monitoring. To provide qualitative information, the software is preferably PCA. To provide quantitative information, preferably, the software is a random forest - based regression.

[0081] The kit may further include at least one sample of a nanomaterial (e.g., GBM) having known values for variables that can define the composition of the nanomaterial. Variables that can define the composition of the nanomaterial (e.g., GBM) include, but are not limited to, the surface area of the nanomaterial, the degree of functionalization of the nanomaterial (e.g., oxygen content), the sp 3 degree of hybridization, the amount of defects in the nanomaterial, and the thickness of the nanomaterial.

[0082] The kit may further include a buffer solution. The buffer solution may be a phosphate buffer solution. The buffer solution may be a neutral buffer solution. The kit may further include a plurality of buffer solutions.

[0083] The kit may further include a microplate, for example, a 96 - well microplate. The kit may further include a pipette. The kit may further include instructions regarding how to use the kit to obtain information about the composition of the nanomaterial (e.g., GBM), for example, the instructions may outline the method according to the first aspect of the present invention.

[0084] The kit may be for implementing the method according to the first aspect of the present invention.

Brief Description of the Drawings

[0085] Hereinafter, embodiments of the present invention will be further described with reference to the accompanying drawings.

Figure 1

Figure 2a

Figure 2b

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

[0086] The term "nanomaterials" is intended to cover any material having at least one nanoscale dimension, and "nanoscopic" is from 1 to 100 nanometers. Nanomaterials can be roughly classified by the total number of their nanoscale dimensions, as seen in Table 1 below, TIFF2025518456000007.tif39157

[0087] Graphene is sp 2It is a name given to a flat sheet in which hybrid carbon atoms are densely packed in a two-dimensional (2D) honeycomb lattice, such as a flat single layer. Usually, graphene is composed of layers stacked up to 10 layers or less.

[0088] The term "graphene-based material (GBM)" is intended to target graphene and derivatives of graphene that have a substantially sp 2 hybrid structure and take a flat, planar or laminated structure. Also, the "graphene-based material" shall include materials obtained by laminating more than 10 layers of graphene, that is, graphite and its derivatives. Graphene-based materials include, but are not limited to, graphene, such as pristine graphene, graphene oxide, reduced graphene oxide, functionalized graphene and doped graphene, graphite and graphite oxide.

[0089] A "responsive probe" is a chemical entity that has a measurable property, such as fluorescence, and can interact with a nanomaterial (e.g., GBM) to cause a change in its measurable property. Examples of measurable properties of a responsive probe include the degree to which the probe can absorb, emit, and / or reflect electromagnetic radiation, such as infrared, visible light, ultraviolet, and X-rays. Other measurable properties of a responsive probe include nuclear magnetic resonance and electrochemical properties, such as electrochemical potential or electrochemical impedance.

[0090] A "non-responsive competing binder" is a chemical entity that either has no measurable property or has a measurable property different from that of the responsive probe but still has the potential to interact with the nanomaterial. Thus, the non-responsive competing binder may be capable of influencing the extent to which the nanomaterial brings about a change in the measurable property of the responsive probe. The non-responsive competing binder may be a small molecule (i.e., a molecule having a molecular weight of less than 5000 g / mol (or a salt thereof)). Non-polymeric non-responsive competing binders may also be used in the present invention, such as polymers (e.g., sodium poly(4-styrenesulfonate)), conjugated polymers, and biopolymers. The non-responsive competing binder may be a metal complex.

[0091] The responsive probe (and, if present, the non-responsive competing binder) can interact with the nanomaterial (e.g., GBM) by intermolecular attractive forces or attractions, such as hydrogen bonding and / or van der Waals forces. Alternatively, the responsive probe (and, if present, the non-responsive competing binder) can interact with the nanomaterial by forming covalent or divalent bonds.

[0092] A "dispersion" is a system in which nanomaterial particles are dispersed (suspended) in a liquid phase (the phase is liquid at room temperature). The liquid phase is typically a solvent.

[0093] Throughout the description and claims of this specification, the words "comprise" and "contain" and their variations mean "include but not limited to", and are not intended to exclude (nor do they exclude) other parts, additives, components, integers or steps. Throughout the description and claims of this specification, unless the context otherwise requires, the singular form includes the plural form. In particular, when an indefinite article is used, the specification should be construed as intending not only the singular form but also the plural form, unless the context otherwise requires.

[0094] Features, integers, characteristics, compounds, chemical moieties or groups described in connection with a particular aspect, embodiment or example of the invention are to be understood as applicable to other aspects, embodiments or examples described herein, unless incompatible therewith. All features disclosed in this specification (including the appended claims, abstract and drawings), and / or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. The invention is not limited to the details of the foregoing embodiments. The invention extends to any novel feature, or any novel combination of features disclosed in this specification (including the appended claims, abstract and drawings), or to any novel step, or any novel combination of steps of any method or process so disclosed.

[0095] Attention is directed to all papers and documents filed in connection with this application and published in general with this specification, simultaneously therewith or before, the contents of all such papers and documents being incorporated herein by reference.

Examples

[0096] Throughout this specification, these abbreviations have the following meanings. TIFF2025518456000008.tif55154

[0097] The structures of the probes used in the examples are shown in Table 2 below. TIFF2025518456000009.tif123164

[0098] The details of the graphene oxide samples used in the examples are shown in Table 3 below. TIFF2025518456000010.tif110151

[0099] Materials and methods 1-Pyrenebutanol, 1-pyrenesulfonic acid (probe 4), and graphene oxide samples GO(a) were obtained as commercial products from Sigma-Aldrich. Pyranine (probe 3) was obtained as a commercial product from Alfa Aesar. Riboflavin 5'-monophosphate (probe 2) was obtained as a commercial product from Fluorochem. Graphene oxide samples GO(b), GO(c), and GO(d) were provided by academic institutions. Graphene oxide sample GO(e) was obtained as a commercial product from Graphitene, and GO(f) was obtained from GOGraphene. Deionized water (18.2 MΩcm) was used throughout.

[0100] Samples GO(h) - GO(k) were prepared by modifying the commercially available sample GO(g) by modifying the method reported by A. Talyzin et al. in Phys. Chem. Phys, 2020, 22, 21059 - 21067.

[0101] Graphene oxide (50 mg), anhydrous pyridine (5 mL), and dry acetic anhydride in an amount varying from 80 μL to 2 mL (depending on the degree of desired surface modification) were added to a 20 mL vial under a nitrogen atmosphere. The reaction mixture was stirred at 60 °C for different times (from 1 hour to 1 week depending on the degree of desired surface modification). The amounts of acetic anhydride and stirring times to achieve different surface modification degrees for samples GO(h) to GO(k) are shown in Table 4 below. TIFF2025518456000011.tif47161

[0102] Each mixture was filtered with diethyl ether and then (i) washed with warm methanol for several cycles, (ii) washed with water for 4 cycles, and (iii) centrifuged (at 4000 rpm for 30 minutes). The sample was then dried in a freeze dryer for 2 days to prepare a fine powder sample.

[0103] The degree of modification (i.e., the percentage of acetylated OH groups on the surface of GO) was determined using a separate established method independent of the well-plate analysis test (reported by C. N. R. Rao et al., Chem. Phys., 2017, 683, 459-466). Briefly, 1-(bromoacetyl)pyrene, an activated fluorescent molecule, was reacted with the (non-acetylated) alcohol groups remaining on the material surface. Unreacted 1-(bromoacetyl)pyrene can be determined by spectrofluorimetric analysis, and thus the number of unmodified alcohol groups can be determined. By comparing the amount of unmodified alcohol groups GO(g) in the original material, it is possible to quantify the degree of surface modification.

[0104] Well-plate fluorescence studies were performed using a BioTek Cytation 5 Cell Imaging Multi Mode Reader.

[0105] Synthesis of Compound 1, Probe 1 and Probe 5 The syntheses of Compound 1, Probe 1, and Probe 5 are outlined in the following schematic diagrams. TIFF2025518456000012.tif46155

[0106] Synthesis of 1-(4-bromobutyl)pyrene (Compound 1) Compound 1 was prepared according to the method disclosed by K. W. J. Heard, at al., ACS Omega, 2019, 4, 1969-1981.

[0107] Synthesis of sodium (1-pyrenyl)butyl sulfonate (Probe 1) Probe 1 was prepared according to the method disclosed by K. W. J. Heard, at al., ACS Omega, 2019, 4, 1969-1981.

[0108] Synthesis of N,N,N-trimethyl-4-(pyren-1-yl)-butan-1-aminium bromide (Probe 5) Probe 5 was prepared according to the method disclosed in M. S. Becherer, et al., Chem. Eur. J., 2009, 15, 1637-1648.

[0109] Analysis test design Figure 1 shows a workflow based on the following exemplary analytical tests.

[0110] Method A, well plate fluorescence study : First, 0.1 mg / mL of each probe in distilled water was added to 0.1 mg / mL of each graphene oxide sample in distilled water, varying the volume in the well plate to a total of 40 μL. The GO samples were sonicated for 30 seconds before addition. Then, this plate was analyzed with a fluorescence plate reader (Analytical Test 1). A modified version of this method was used for further analysis using 33 μL of each probe at a concentration of 1 mM in distilled water or 0.2 M, pH 7.0 phosphate buffer, together with 40 μL of a 0.01 mg / mL GO sample (Analytical Tests 2 - 5).

[0111] Method A, data analysis : Table 5 shows the excitation wavelength of each probe and the target wavelength of the emission spectrum at which intensity values are obtained. TIFF2025518456000013.tif50157

[0112] The intensity of each sample at the target wavelength of the relevant probe in the sample was collated. Intensity data was collected by exciting near the absorption maximum wavelength of each probe and collecting the emission intensity near the emission maximum wavelength. This data can be tabulated, as seen in, for example, Figure 2a.

[0113] This multivariate data was processed to obtain a two-dimensional plot showing the properties of the GO being characterized. For example, for Figure 2b, the data in the table of Figure 2a was subjected to standard principal component analysis (PCA). PCA is a standard approach and can be achieved using various standard tools. The data in the table of Figure 2a was subjected to PCA using the scikit-learn library in a Python environment. After PCA, the output data was exported to Excel and plotted in a 2D (two-dimensional) plot. The principal components (PCs) were plotted, and for example, in Figure 2, the first two PCs are plotted. Usually, these are labeled with the percentage of the variance of the raw data explained by each PC. For example, in Figure 2, PC1 explains 73.8% of what is observed in the raw data. Repeated analyses may be averaged to show experimental error. For example, in Figure 2b, an ellipse representing 95% confidence limits is drawn around the mean value of the total analysis of a particular GO sample (using the Excel Add-In XRealStats). The positions of the different samples on the resulting output plot (e.g., Figure 2b) provide a reading of the properties of the GO samples.

[0114] Method B, well plate fluorescence test : 127 μL of distilled water and 33 μL of each solution probe (0.1 mM in distilled water) were added to 40 μL of each modified graphene oxide (suspension 0.1 mg / mL in distilled water) in the wells of a well plate, making a total of 200 μL. The GO-modified samples were sonicated for 30 seconds and left to stand for 15 minutes before addition. This plate was analyzed with a fluorescence plate reader.

[0115] The excitation wavelength and data processing were carried out in the same way as in Method A, except that the ellipse drawn on the PCA plot to represent the 95% confidence limits was created in Excel using the XLSTAT Add-In.

[0116] Method A was used for analytical tests 1 - 5. Method B was used for analytical test 6.

[0117] Analysis test 1 Probes 1 to 4 were added together with GO(a) to GO(d). The fluorescence intensity of each well was recorded, and the values were analyzed using PCA. As a result of this PCA, GO(a) and GO(d) were separated, but GO(b) and GO(c) were not well separated. Since GO(b), GO(c), and GO(d) were obtained from the same source (Figure 2b), these three samples were expected to cluster, but this was only seen for GO(b) and GO(c).

[0118] Analysis test 2 To examine whether pH has a significant effect on the binding of the probe, the same analysis was performed using a 0.2 M, pH 7.0 phosphate buffer (Figure 3). The change in the two-dimensional position by GO(d) suggests that pH affects the binding, and thus the separation, of the samples in PCA. In the buffer system, results close to the expected ones were obtained, such as GO(b), GO(c), and GO(d) falling into one cluster. GO(a) remained well separated from the other GO samples, suggesting that GO(a) is essentially different from the other samples. Since pH seemed to have a significant effect on binding, this buffer system was used in all subsequent analytical tests to mitigate the effect of this variable.

[0119] Analysis test 3 To establish whether the addition of the fifth probe significantly changes the separation, the GO samples were combined with probe 5, and the data were used to add to the data of the previous experiment (Figure 4). It was observed that PC1 and PC2 had a lower percentage of variance than the data obtained from the four probes. However, since the exact nature of the future GO samples is unknown and the positively charged nature of probe 5 may have a substantial impact, probe 5 was left to add probe diversity.

[0120] Analysis test 4 To determine whether the observed differences were due to calculation errors or minor concentration differences, the varying concentration of GO(a) was analyzed together with the original concentrations of GO(b) to GO(d) (Figure 5). As a result, since the half and quarter concentrations of GO(a) did not occupy the same cluster as GO(b) to GO(d), it was suggested that concentration was only one factor affecting the results.

[0121] Analysis test 5 Samples GO(e) and GO(f) were analyzed. The PCA plot shows that each of these two samples occupies a spatially distinct region compared to the other GOs (Figure 6).

[0122] All commercially available samples were not only clearly separated from non-commercially available samples but also separated individually. Each of the non-commercially available samples that were considered similar was not separated in this PCA.

[0123] Analysis test 6 Samples GO(g) to GO(k) were analyzed according to Method B. The resulting PCA plot showed a systematic change according to the degree of surface modification of the samples (Figure 7).

[0124] A linear plot of PC1 (taking the average of all analysis replicates of the samples) of samples GO(g) to GO(j) against the degree of surface modification shows a systematic relationship suitable for quantifying the degree of surface modification (Figure 8).

[0125] As an example of quantification, when the average of PC1 determined for samples GO(g) to GO(j) is plotted against the degree of surface modification, a linear relationship is observed (as in Figure 8). Linear regression (using Excel, refer to the plot) provides an equation that can also be used for quantification when the degree of surface modification is not yet known. Applying this linear relationship to the average value of PC1 determined for sample GO(k), which is -1.45675, it is possible to quantify the degree of modification as 39.5% (as follows: degree of modification = -8.6491(PC1 (平均)) + 26.9). Separately from this, it was determined that the degree of surface modification of GO(k) is 38% (using the method reported by C.N.R. Rao et al., Chem. Phys., 2017, 683, 459 - 466), and the quantification of surface modification was confirmed (error < 5%).

Claims

1. A process of bringing multiple parts of a nanomaterial sample into contact with multiple responsive probes, A process of measuring the properties of each responsive probe in the presence of nanomaterials to provide multiple characteristic measurements, and A process of processing multiple characteristic measurements in order to provide qualitative or quantitative information regarding the composition of nanomaterials. A method for providing information regarding the composition of nanomaterials, including [specific elements].

2. The method according to claim 1, wherein qualitative information regarding the composition of a nanomaterial is provided by a step of processing multiple characteristic measurements.

3. The method according to claim 2, wherein the qualitative information is the degree of similarity of the composition of the nanomaterial to at least one other sample of the nanomaterial.

4. The method according to claim 1, wherein quantitative information regarding the composition of a nanomaterial is provided by a step of processing multiple characteristic measurements.

5. The method according to claim 4, wherein the quantitative information is an estimate of a variable that can define the composition of the nanomaterial, and the estimate is provided by determining the degree of similarity of the composition of the nanomaterial to at least one sample of nanomaterial having known values ​​of the variable.

6. The method according to any one of claims 1 to 5, wherein principal component analysis is used to process multiple characteristic measurements.

7. The method according to claim 1, wherein the plurality of probes consist of four or more responsive probes.

8. The method according to claim 7, wherein the plurality of probes consist of five or more responsive probes.

9. The method according to claim 1, wherein the responsive probe is a fluorescent probe, and each property of the responsive probe in the presence of a nanomaterial is fluorescence.

10. The method according to claim 9, wherein the array of probes includes amphiphilic probes.

11. The method according to claim 9 or 10, wherein the array of probes includes probes having an aromatic portion and a charged portion or a bipolar portion.

12. The method according to claim 11, wherein the probe further includes a hydrocarbon spacer that connects the aromatic portion to the charged portion or the bipolar portion.

13. The array of fluorescent probes The method according to claim 9, comprising at least one probe selected from:

14. The method according to claim 1, wherein the responsive probe is an ultraviolet-visible probe, and each property of the responsive probe in the presence of a nanomaterial is the absorbance and / or emission of light in the ultraviolet-visible region.

15. The method according to claim 1, wherein a portion of the nanomaterial sample is a dispersion of nanomaterials.

16. The method according to claim 15, wherein the dispersion is an aqueous dispersion.

17. The method according to claim 15 or claim 16, wherein the dispersion comprises a buffering agent.

18. The composition according to claim 1, wherein the nanomaterial is a graphene-based material.

19. The method according to claim 18, wherein the graphene-based material is graphene.

20. The method according to claim 18, wherein the graphene-based material is graphene oxide.

21. The method according to claim 18, wherein the graphene-based material is reduced graphene oxide.

22. (i) Types of responsive probes, (ii) Solvent of the solution, (iii) pH of the solution, and / or (iv) Ionic strength of the solution Multiple responsive probe solutions, each having at least one different property, selected from the following, Software for processing multiple measurements of the characteristics of each responsive probe in the presence of graphene-based materials, in order to provide qualitative or quantitative information, or a link thereto, regarding the composition of graphene-based materials. A kit that includes the following:

23. Multiple responsive probe solutions are available. (i) to (iv) at least one characteristic selected from (i) to (iv), and / or (v) Absence or presence of non-responsive competing binders, and optionally, the concentration of non-responsive competing binders if present. The kit according to claim 22, which differs from the above.

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