Devices and methods for detecting pathogens

Abstract

The present invention is directed to a sensing device comprising a plurality of capacitance sensors comprising carbon dots. Methods of using the sensing device of the present invention, such as to determine the presence of an analyte or species of interest in a sample or at a target location, are further provided.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims priority to U.S. Provisional Application No. 63 / 112,260, titled "DEVICE AND METHODS FOR DETECTING BACTERIA," filed on 11 November 2020, the contents of which are incorporated herein by reference in their entirety.

[0002] In some embodiments, the present invention relates to a device and method for monitoring microbial contamination, such as by detecting volatile compounds derived from microorganisms. [Background technology]

[0003] Bacteria are known to release a variety of volatile molecules, the type and concentration of which are strain-dependent. Bacterial volatile compounds such as alcohols, aldehydes, and ketones have been used as microbial biomarkers, and bacterial volatile metabolite mixtures have been employed as a unique "odor profile" medium for bacterial identification. In particular, colorimetric arrays have been developed to sense volatile compounds, allowing for differentiation between different bacterial strains. Optical and chemically resistant gas sensing methods have also been used for bacterial analysis utilizing specific biomarkers. A fundamental limitation of these vapor-based bacterial detection schemes is that they cannot be used for continuous monitoring because samples must be collected (usually manually) and analyzed ex situ. This aspect excludes a wide range of important bacterial sensing applications in healthcare, environmental monitoring, and homeland security.

[0004] Among the various gas sensing technologies developed, the "artificial nose" is attracting considerable attention. The artificial nose aims to effectively mimic the function of physiological organs, particularly their exceptional selectivity between different vapor molecules and gas mixtures. Reported artificial nose platforms rely on various physical mechanisms, such as changes in the electrical resistance of conductivity sensors, the absorption and desorption of heat in calorimetry sensors, and changes in the electrical conductance of semiconductor field-effect transistors.

[0005] The C-dot IDE capacitive artificial nose has been successfully applied to continuous monitoring and bacterial identification, highlighting its availability as a general platform for sensors.

[0006] There remains a great need for devices and methods for non-invasive bacterial growth detection, including the continuous monitoring and identification of different bacteria. [Overview of the project]

[0007] In some embodiments, the present invention relates to an apparatus for determining the presence of an analyte at a given location, a system comprising the same, and a method for using the same.

[0008] In some embodiments, the present invention is partly based on research showing that the device of the present invention, e.g., the C-dot IDE capacitive nose, has been successfully employed for continuous real-time monitoring of bacterial growth. Importantly, the recorded unique capacitance signals enable the identification of different Gram-positive and Gram-negative bacteria. In general, the novel capacitive C-dot based nose can be easily implemented as a portable vapor sensor for continuous non-invasive monitoring and identification of bacterial growth in a variety of applications, including medical diagnostics, food processing, and environmental monitoring.

[0009] The present invention can be used, for example, as a safety measure where it is necessary to eliminate the possibility of bacteria being present. Such safety-related applications may include water safety, food safety, and human health safety (such as in hospitals or airports).

[0010] The present invention can be used for the measurement and monitoring of analytes, such as microorganisms and / or VOCs secreted therefrom, in a wide range of locations and applications, including water safety, food safety, hospitals, veterinary medicine, and airports.

[0011] In some embodiments, applying the method of the present invention is a prerequisite for decision-making, such as determining whether an analyte is present and which analyte is present, and therefore, an action to neutralize the analyte is taken.

[0012] In some embodiments, the training phase includes recording capacitance values ​​under baseline conditions. In some embodiments, the baseline conditions are referred to as or used as a reference value (e.g., a blanc measurement or standard measurement). In some embodiments, any capacitance output at a location calculated or determined according to the method disclosed herein is compared to a blanc measurement or standard measurement. In some embodiments, the method includes receiving a capacitance value and comparing it to a blanc measurement or standard measurement to obtain a relative capacitance value or an absolute capacitance value, e.g., an unknown measurement or analyte.

[0013] In some embodiments, baseline conditions are provided under specific relative humidity conditions. In some embodiments, baseline conditions include multiple relative humidity conditions.

[0014] In some embodiments, the relative humidity is at least 10%, at least 40%, at least 60%, at least 70%, or at least 90%, or any value and range in between. Each possibility represents a separate embodiment of the present invention.

[0015] In some embodiments, the relative humidity ranges from 10% to 99%.

[0016] One skilled in the art will understand that humidity can change the capacitance strength.

[0017] In some embodiments, the method is performed in a specific or particular humidity environment.

[0018] In some embodiments, the method includes calibrating capacitance changes induced by the presence of an analyte in multiple humidity environments (e.g., measuring ammonia exposures to 13%, 44%, 64%, 77%, and 98% as described below), and generating a calibration curve based on the different capacitance values obtained.

[0019] In some embodiments, the method includes measuring the relative humidity value and the capacitance signal at the target location.

[0020] According to a first aspect, an apparatus is provided that includes a plurality of molecular sensors, each of the plurality of molecular sensors includes a carbon dot in contact with an electrode, each of the plurality of molecular sensors is operably communicated with a control unit, and each of the plurality of molecular sensors is configured to generate different capacitance signals in response to an analyte.

[0021] According to another aspect, a system is provided that includes the apparatus of the present invention and a current source operably communicated with the electrode.

[0022] In another embodiment, a method is provided for determining the presence of an analyte at a target location, the method comprising: (a) providing a system of the present invention, the providing being in close proximity to a location suspected to contain an analyte; (b) applying a voltage to the system such that a plurality of molecular sensors generate a plurality of capacitance data; and (c) determining the presence of an analyte at a target location by applying a machine learning model to target capacitance data obtained from the target location.

[0023] In some embodiments, the multiple molecular sensors include at least three molecular sensors.

[0024] In some embodiments, the analyte is a gaseous analyte.

[0025] In some embodiments, the analyte includes volatile organic compounds (VOCs).

[0026] In some embodiments, the VOCs are derived from microorganisms.

[0027] In some embodiments, the microorganism is selected from the group consisting of bacteria and fungi.

[0028] In some embodiments, the electrodes are configured to receive direct current (DC) or alternating current (AC).

[0029] In some embodiments, multiple molecular sensors are in contact with a substrate.

[0030] In some embodiments, each carbon dot is characterized by a different polarity.

[0031] In some embodiments, each carbon dot is characterized by a different water contact angle.

[0032] In some embodiments, the different polarities of each carbon dot are predetermined so that each molecular sensor can generate a different capacitance signal in response to the analyte.

[0033] In some embodiments, the carbon dots include poly-p-phenylenediamine.

[0034] In some embodiments, the control unit includes a processor.

[0035] In some embodiments, the system is configured for the detection of (a) a predetermined analyte, (b) a predetermined microbial species, and (c) any one combination of (a) and (b).

[0036] In some embodiments, detection is based on multiple capacitance signals acquired from multiple molecular sensors.

[0037] In some embodiments, the analyte includes VOCs, VOC-secreting microorganisms, or a combination thereof.

[0038] In some embodiments, the location is indoors or outdoors.

[0039] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as those commonly understood by those skilled in the art relating to the present invention. Similar or equivalent methods and materials may be used in carrying out or testing embodiments of the present invention, but exemplary methods and / or materials are described below. In case of any conflict, the patent specification, including the definitions, shall prevail. Furthermore, the materials, methods, and examples are illustrative and not necessarily intended to be limiting.

[0040] Further embodiments and the full scope of applicability of the present invention will become apparent from the detailed description below. However, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from the detailed description, it should be understood that the detailed description and preferred embodiments are given only as examples, illustrating specific embodiments of the invention. [Brief explanation of the drawing]

[0041] [Figure 1] This includes a scheme demonstrating the non-limiting manufacturing of a carbon dot comb electrode capacitance vapor sensor. C dots are separated according to color / polarity using liquid chromatography and deposited on a commercially available IDE. When the C dot IDE is exposed to vapor molecules, different capacitance changes are recorded depending on the type of deposited C dots and gas molecules.

[0042] [Figure 2A-2B] This includes micrographs and graphs illustrating the characteristics of carbon dot IDE sensors. (Figure 2A) Optical image (left) and atomic force microscope (AFM) image of an IDE showing unevenly distributed C dots deposited on the IDE surface between gold fingers. (Figure 2B) Water contact angles (WCA) recorded for three C dot IDEs. The control sample corresponds to an IDE without deposited C dots.

[0043] [Figure 3A-3C](Figure 3A) Capacitance conversion recorded for the red C-dot IDE, orange C-dot IDE, and blue C-dot IDE during gas molecule exposure and subsequent purging, respectively (all vapor molecule concentrations were determined by GC-MS and were 35 ppmv). Arrows indicate gas injection time. Gas purging was performed after the capacitance reached a flat region. (Figure 3B) Capacitance dose-response curves for (i) NH3 and (ii) DMF recorded on the red C-dot IDE sensors. Linear fitting of data points is presented. R² greater than 0.98 was obtained in all linear fits. (Figure 3C) Bar graph showing the capacitance change at saturation after exposure of the C-dot IDE to gas molecules at a concentration of 35 ppmv.

[0044] [Figure 4A-4B] The graph includes graphs showing impedance spectroscopy of carbon dot IDEs upon exposure to different vapors. (Figure 4A) Nyquist plots of orange C-dot IDEs recorded at the indicated relative humidity (RH) levels: 43% (i), 64% (ii), and 97% (iii). (Figure 4B) Nyquist plots of orange C-dot IDEs recorded after exposure to different gas molecules (RH was 64%; gas molecule concentrations were all 35 ppmv).

[0045] [Figures 5A-5E]The graph includes a graph showing the monitoring of growth and differentiation of bacteria using a carbon dot IDE artificial nose (e.g., an exemplary sensor not limiting to the present invention). (Figure 5A) Experimental setup. The C-dot IDE, containing red C-dots, orange C-dots, and blue C-dots respectively, provides continuous monitoring of capacitance changes induced by volatile molecules released by bacteria. (Figure 5B) Time-dependent capacitance response curves recorded for different bacteria: Escherichia coli (i); Pseudomonas aeruginosa (ii); Bacillus subtills (iii); and Staphylococcus aureus (iv). Red curve: red C-dot IDE, orange curve: orange C-dot IDE, blue curve: blue C-dot IDE. The curves represent the average of three repeats per electrode. (Figure 5C) Capacitance changes recorded after 20 hours of bacterial growth. (Figure 5D) Principal component analysis (PCA) showing capacitance response cluster differentiation according to bacterial strain. Turbidity assay: Evaluate bacterial growth of four bacterial strains, Escherichia coli(i), Pseudomonas aeruginosa(ii), Bacillus subtilis(iii), and Staphylococcus aureus(iv), cultured in Luria-Bertani(LB) medium at 37°C and 28°C for both Gram-negative and Gram-positive bacteria. (Figure 5E) Time-dependent capacitance response curves recorded for different bacteria: Escherichia coli(i), Pseudomonas aeruginosa(ii), Bacillus subtilis(iii), and Staphylococcus aureus(iv) were cultured in Luria-Bertani(LB) medium at 37°C and 28°C for both Gram-negative and Gram-positive bacteria, respectively.

[0046] [Figures 6A-6E]This includes images and graphs representing the spectral characteristics of C dots. (Figures 6A-6B) are photographs of C dots in aqueous solutions under visible light (Figure 6A) and UV λ=356nm light (Figure 6B). (Figures 6C-6E) are graphs representing the PL emission spectra of blue C dots (Figure 6C), orange C dots (Figure 6D), and red C dots (Figure 6E) in aqueous solutions at different excitation wavelengths (300-540nm in 20nm increments).

[0047] [Figures 7A-7F] High-resolution XPS spectral scans of all C dots are shown, displaying the N1s and O1s spectra. Figures 7A–7C represent the N1s spectra of the blue C dot (Figure 7A), orange C dot (Figure 7B), and red C dot (Figure 7C), respectively. Figures 7D–7F represent the O1s spectra of the blue C dot (Figure 7D), orange C dot (Figure 7E), and red C dot (Figure 7F), respectively. The surfaces of these samples were further investigated using the results of the X-ray photoelectron spectroscopy (XPS). In the high-resolution spectra, the N1s band can be deconvoluted into two peaks at 399.9 eV and 401.5 eV, respectively, representing the CN bond (green curve, assigned by **) and the NH bond (pink curve, assigned by *). The O1s band contains three peaks at 531.4, 532.3, and 533.2 eV for C=O, CO, and COO-, respectively. Results from XPS spectra showed that the composition of hydrophilic functional groups on the surface not only contributes to their optical properties but also causes redshift along with a relative increase in NH bond peaks. However, it also affects the overall polarity of the particle surface. Furthermore, the red C dots showed the highest nitrogen content (14.83%) compared to the orange and blue C dots, which had 12.37% and 6.47%, respectively. This firmly establishes differences in the degree of surface oxidation that primarily control the surface state of individual C dots, along with tunable photoluminescence properties.

[0048] [Figure 8]This graph shows the Fourier transform infrared (FTIR) spectra of three C dots. Blue C dot (*), orange C dot (**), red C dot (***). The FTIR spectra reveal that the C dots exhibit characteristic stretched oscillations in the absorption bands of the C=O and C=N stretched peaks at approximately 1560–1670 cm⁻¹, the CH3 band peak at approximately 1400–1500 cm⁻¹, and the CO and CN stretched peaks at approximately 1250–1350 cm⁻¹. A comparison of the FTIR spectra of the three samples reveals two important observations. One is the enhancement of typical stretched oscillations of CN / CO and C=O / C=N bonds with C dot polarity, which indicates an increase in the degree of oxidation associated with the emission redshift of the C dots. The other is that the CH3 oscillation band is strongly discrete for the blue C dot, but small and negligible for the orange and red C dots. Nonpolar C dots are assumed to be substantially lacking hydrophilic surface groups.

[0049] [Figure 9] This scheme represents a non-limiting exemplary configuration of the capacitive sensor of the present invention, comprising a sensing unit including C dots deposited on an electrode.

[0050] [Figure 10A-10C] Figure 10A shows a scheme illustrating a non-limiting configuration of a virus-aerosol apparatus used for detecting viruses in the air, Figure 10B shows a bar graph illustrating capacitance changes after 2 minutes of exposure to different viruses and controls using only DMEM medium, and Figure 10C shows the detection of virus species in gaseous samples at different concentrations. [Modes for carrying out the invention]

[0051] In the following detailed description, numerous specific examples are described in detail to provide a complete understanding of the present invention. However, as will be understood by those skilled in the art, the examples described herein can be carried out without these specific details. In other examples, methods, procedures and components are not described in detail so as not to obscure the relevant features described. Some features or elements described in relation to one embodiment may be combined with features or elements described in relation to other embodiments. For clarity, descriptions of the same or similar features or elements may not be repeated.

[0052] In some embodiments, the present invention relates to a capacitance sensor array comprising at least three sensing elements, each composed of carbon dots (e.g., amino, hydroxy, carboxy, carbonyl, and imino, or any combination thereof) having different content of polar surface groups, characterized by different surface polarities, and based on surprising findings that they exhibit remarkable sensitivity to a wide range of volatile compounds (VCs). In some embodiments, the array of the present invention is configured to produce different capacitance values ​​(e.g., capacitance profiles) in response to exposure to a particular analyte or species (VC). Thus, the array of the present invention is implemented to distinguish VC profiles produced by different species (pathogens) of interest based on the differences in sensitivity and / or selectivity of each sensing element to a particular VC. In some embodiments, the VC is pathogen-derived VC.

[0053] The capacitive sensor array-based device (sensor) of the present invention has been successfully implemented by the inventors for highly selective detection of specific pathogens (bacteria and viruses) of interest in gaseous samples.

[0054] In some embodiments, the terms “capacitive sensor array of the present invention,” “C-dot IDE capacitive sensor,” “C-dot IDE electrode,” or “C-dot IDE sensor” are used interchangeably herein.

[0055] In some embodiments, the terms “microbe” or “microorganism” and “pathogen” are used interchangeably herein and include bacteria, fungi, viruses, and any other pathogens.

[0056] In one aspect of the present invention, a sensor array is provided, the array comprising a plurality of capacitive sensors, each capacitive sensor comprising a sensing element comprising carbon dots electrically connected to two electrodes, and each carbon dot of the plurality of capacitive sensors characterized by a different surface polarity. In some embodiments, each of the plurality of capacitive sensors comprises carbon dots characterized by similar surface polarity. In some embodiments, each carbon dot independently comprises hydrophilic surface groups. In some embodiments, each capacitive sensor is characterized by (i) different surface polarities of carbon dots, and (ii) different sensitivities and / or selectivity for a particular species of interest (e.g., a particular VC).

[0057] In some embodiments, the multiple capacitance sensors include at least three capacitance sensors. In some embodiments, the multiple capacitance sensors include 3, 4, 5, 10, 30, or 100 sensors (including any range in between), each sensor containing carbon dots having different surface polarities. In some embodiments, the surface polarity of the carbon dots is predetermined by (i) the chemical composition of the hydrophilic surface groups, (ii) the weight content of the hydrophilic surface groups in the carbon dot, or both (i) and (ii). In some embodiments, the terms “capacitive sensor” and “sensor” are used interchangeably herein. In some embodiments, the terms “analyte” and “species” are used interchangeably herein and refer to microorganisms and / or VC.

[0058] As used herein, “carbon dots” or C dots generally refer to graphene frameworks and carbon nanoparticles, generally having a spherical structure. In some embodiments, the carbon dots of the present invention are heterogeneously shaped. In some embodiments, the carbon dots of the present invention lack a defined shape (e.g., the particles have a random shape). In some embodiments, the carbon dots of the present invention are substantially spherical, elliptical, and / or cylindrical. The C dots of the present invention are in the nanoscale range of about 2 to 100 nanometers (nm), but many are in the sizes of 2 to 7 nm, 1 to 5 nm, 1 to 10 nm, 2 to 15 nm, and / or 2 to 5 nm (including any range in between). Hydrophilic groups on the surface of the carbon dots contribute to the surface polarity of the C dots. In some embodiments, the hydrophilic groups predetermine the affinity and / or sensitivity of the carbon dots to a particular VC and predetermine the fluorescence spectrum of the carbon dots. For example, hydrophilic (or polar) carbon dots are characterized by red (also referred to herein as “red dots”) and a maximum emission of about 620–670 nm, as shown in Figures 6A–6E; moderately hydrophilic carbon dots are characterized by orange (also referred to herein as “orange dots”) and a maximum emission of about 550–600 nm; and hydrophobic (or substantially nonpolar) carbon dots are characterized by blue (also referred to herein as “blue dots”) and a maximum emission of about 400 nm. Carbon dots characterized by different polarities and / or different content of hydrophilic surface groups can be obtained, for example, by utilizing nano-size particle separation techniques (such as silica column separation), as described herein (Examples).

[0059] In some embodiments, each carbon dot comprises a carbon-based core (e.g., in the form of a polyaromatic graphene-like structure) and an outer portion (or shell) containing hydrophilic surface groups. In some embodiments, each carbon dot comprises a hydrophobic (e.g., substantially nonpolar) core bonded to the shell. In some embodiments, the shell is at least partially in contact with the core. In some embodiments, the hydrophilic surface groups are covalently bonded to the outer portion (or outer surface) of the carbon-based core to form the shell.

[0060] In some embodiments, hydrophilic surface groups are chemisorbed and / or physically adsorbed onto a carbon-based core. In some embodiments, hydrophilic surface groups are stably bonded to a carbon-based core. In some embodiments, the term “stably” refers to the chemical stability of the bond (e.g., substantially lacking bond cleavage) over a period of time from one day to one year (including any range in between) under ambient conditions (temperatures below 100°C, normal pressure or vacuum, and optionally ambient atmosphere).

[0061] In some embodiments, the shell is in the form of layers. In some embodiments, the shell is in the form of continuous layers and optionally has a substantially uniform thickness. In some embodiments, the shell substantially surrounds a carbon-based core. In some embodiments, the shell is a porous shell. In some embodiments, the shell comprises multiple layers.

[0062] In some embodiments, the hydrophilic surface group comprises heteroatoms (such as nitrogen and oxygen atoms). In some embodiments, each carbon dot comprises a hydrophilic surface group, and the surface group comprises nitrogen and oxygen atoms. In some embodiments, the hydrophilic surface group comprises any of the following: amine group, imine group, carbonyl group, carboxyl group, ester, ether, nitro, cyano, amide, and hydroxyl group, or any combination thereof.

[0063] In some embodiments, the array of capacitive sensors in the present invention includes at least a first sensor, a second sensor, and a third sensor, and optionally one or more additional sensors, wherein the carbon dots of the first sensor, the second sensor, the third sensor, and optionally the additional sensors feature different surface polarities. In some embodiments, each of the first sensor, the second sensor, and the third sensor, and optionally each of the one or more additional sensors, includes a sensing element, the sensing element includes a plurality of carbon dots featuring substantially the same polarity. In some embodiments, each of the first sensor, the second sensor, and the third sensor, and optionally each of the one or more additional sensors, includes one or more sensing elements. In some embodiments, the array of the present invention is a parallel circuit including a plurality of capacitive sensors connected in parallel. In some embodiments, the sensing element of the first sensor includes carbon dots (also referred to herein as polarity C dots) featuring a surface polarity greater than that of the carbon dots of the second and third sensors. In some embodiments, the sensing element of the second sensor includes a carbon dot (also referred to herein as a medium-polar C dot) having a surface polarity greater than that of the carbon dot of the third sensor. In some embodiments, the sensing element of the third sensor includes a substantially non-polar carbon dot (also referred to herein as a non-polar C dot).

[0064] In some embodiments, the different surface polarities of the carbon dots are predetermined by the different weight content of hydrophilic surface groups within the carbon dots. In some embodiments, the atomic ratios of carbon atoms to heteroatoms (e.g., O and N) in the carbon dots of the first, second, and third sensors are 0.2–1.9, 2–2.9, and 3–8, respectively. In some embodiments, the atomic ratios of (i) the carbon content and (ii) the total nitrogen and oxygen content of the carbon dots of the first, second, and third sensors are 0.2–1.9, 2–2.9, and 3–8, respectively.

[0065] In some embodiments, the atomic ratio between carbon atoms and heteroatoms (e.g., O and N) in the carbon dots of the first sensor is 0.2–1.9, 0.2–0.5, 0.5–1, 1–1.9, 1–2, 1–2.1, 1–1.4, 1–1.2, 1.2–1.4, 1.4–1.9, 1.4–2, 1.4–1.6, 1.4–1.8, 1.4–1.5, 1.5–1.8, 1.8–2.1 (including any range in between). In some embodiments, the atomic ratio between carbon atoms and heteroatoms (e.g., O and N) in the polar C dot is up to 2.3, up to 2, up to 1.8, up to 1.7, up to 1.6, up to 1.4, up to 1 (including any range in between). In some embodiments, the atomic ratio between carbon atoms and heteroatoms (e.g., O and N) in the polar C dot is at least 0.2, at least 0.3, at least 0.5, at least 0.7, at least 0.8, at least 0.9, at least 1, at least 1.2, and at least 1.4 (including any range in between).

[0066] In some embodiments, the atomic ratio between carbon atoms and heteroatoms (e.g., O and N) in the carbon dots of the second sensor is 2–2.9, 2–3, 2–2.2, 2–2.6, 2–2.8, 2.2–2.4, 2.4–2.9, 2.4–3, 2.4–2.6, 2.2–2.8, 2.2–2.9, 2.2–3, 2.5–2.8, 2.8–3.1 (including any range in between). In some embodiments, the atomic ratio between carbon atoms and heteroatoms (e.g., O and N) in the medium-polarity C dot is up to 3.1, up to 3, up to 2.8, up to 2.7, up to 2.6, up to 2.4, up to 2.2 (including any range in between). In some embodiments, the atomic ratio between carbon atoms and heteroatoms (e.g., O and N) in the polar C dot is at least 1.8, at least 2, at least 2.1, at least 2.2, and at least 2.4 (including any range in between).

[0067] In some embodiments, the atomic ratio between carbon atoms and heteroatoms (e.g., O and N) in the carbon dot of the third sensor is 3-8, 2.8-5, 2.8-4, 2.8-3, 3-4, 4-5, 3-3.6, 3.6-5, 3.6-4, 3-3.2, 3.2-4, 3.2-3.6, 3.4-4, 3.4-5, 4-4.5, 4.5-5, 5-6, 6-8 (including any range in between).

[0068] In some embodiments, the molar ratio between the amine group and the imine group of the polar C dot is 1.5–3, 1.5–2, 1.5–1.7, 1.7–3, 1.7–2, 1.7–2.5, 1.7–2.8, 1.7–2.2, 1.5–2.2, 1.5–2.5, 1.5–3.5, 1.5–4 (including any range in between).

[0069] In some embodiments, the molar ratio between the amine group and the imine group of the medium polar C dot is 0.8–1.4, 0.8–1.5, 0.7–1.5, 0.8–1, 0.8–1.3, 0.8–1.7, 1–1.3, 1–1.5, 1–1.4 (including any range in between).

[0070] In some embodiments, the molar ratio between the amine group and the imine group of the medium polar C dot is 0.1–1, 0.1–0.8, 0.1–0.2, 0.2–0.8, 0.2–0.5, 0.5–0.8, 0.8–1 (including any range in between).

[0071] In some embodiments, the nitrogen content of the polar carbon dots is 13-20 at%, 12.8-20 at%, 12.8-13 at%, 13-15 at%, 15-17 at%, 17-20 at%, 13-17 at%, 13-25 at%, and 13-30 at% (including any range in between).

[0072] In some embodiments, the nitrogen content of the medium-polarity carbon dots is 10–12.9 at%, 10–11 at%, 10–13 at%, less than 12.9 at%, less than 13 at%, less than 12.6 at%, less than 12.5 at%, or less than 12 at% (including any range in between).

[0073] In some embodiments, the nitrogen content of the nonpolar carbon dots is 1-9.9 at%, 0.5-10 at%, 0.5-1 at%, 1-5 at%, 0.5-5 at%, 0.5-1 at%, 1-8 at%, 1-9 at%, 5-10 at%, 5-9 at%, 9-10 at%, 9-9.9 at%, less than 10 at%, less than 11 at%, less than 10.5 at%, less than 9.9 at%, less than 9 at% (including any range in between).

[0074] In some embodiments, the oxygen content of the polar carbon dots is 20-30 at%, 20-23 at%, 20-25 at%, 20-27 at%, 20-29 at%, 23-25 ​​at%, 25-30 at% (including any range in between).

[0075] In some embodiments, the oxygen content of the medium-polarity carbon dots is 10-20 at%, 10-13 at%, 10-15 at%, 10-17 at%, 10-19 at%, 10-19.9 at%, 13-15 at%, 15-20 at%, 15-18 at%, 15-19 at%, 15-19.9 at%, less than 20 at%, less than 23 at%, less than 21 at%, less than 22 at% (including any range in between).

[0076] In some embodiments, the oxygen content of the nonpolar carbon dots is 1-10 at%, 1-9.9 at%, 1-5 at%, 1-7 at%, 1-9 at%, 3-5 at%, 5-9 at%, less than 10 at%, less than 12 at%, less than 11 at%, less than 9.9 at%, less than 9.5 at%, less than 9 at% (including any range in between).

[0077] In some embodiments, the carbon content of the polar carbon dots is 50-65 at%, 40-65 at%, 40-50 at%, 50-55 at%, 50-60 at%, 60-65 at%, 45-60 at%, less than 68 at%, less than 66 at%, less than 65 at%, less than 64 at% (including any range in between).

[0078] In some embodiments, the carbon content of the medium-polarity carbon dots is 66-79 at%, 66-70 at%, 66-72 at%, 68-70 at%, 68-79 at%, 70-79 at%, 70-75 at%, 75-79 at%, 75-80 at%, 68-75 at%, less than 80 at%, less than 83 at%, less than 81 at%, less than 82 at%, less than 79 at%, less than 78 at%, less than 75 at% (including any range in between).

[0079] In some embodiments, the carbon content of the nonpolar carbon dots is 80-95 at%, 78-90 at%, 78-95 at%, 80-98 at%, 80-85 at%, 80-90 at%, 85-90 at%, 85-93 at%, 90-95 at%, less than 98 at%, less than 97 at%, less than 95 at%, less than 93 at%, less than 90 at%, at least 80 at%, at least 78 at%, and at least 82 at% (including any range in between).

[0080] In some embodiments, polar, medium-polar, and non-polar carbon dots are characterized by XPS spectra, as shown in Figures 7A to 7F.

[0081] In some embodiments, each of the polar, medium-polar, and non-polar carbon dots is characterized by an IR spectrum, as shown in Figure 8. In some embodiments, each of the polar and medium-polar carbon dots has an IR spectrum of approximately 1560–1670 cm⁻¹. -1 At C=O and C=N, the extension peak is observed, at approximately 1250-1350 cm. -1 CO and CN extension peaks are observed. In some embodiments, the IR spectrum of the nonpolar C dot is approximately 1560–1670 cm⁻¹. -1 , and / or approximately 1250-1350cm - The peak at 1 is substantially absent. In some embodiments, the IR spectrum of the nonpolar C dot shows aliphatic and / or aromatic hydrocarbon peaks (e.g., around 1400–1500 cm⁻¹). -1 ) includes.

[0082] In some embodiments, polar, medium-polar, and non-polar carbon dots are characterized by different water contact angles. In some embodiments, polar carbon dots are characterized by water contact angles of less than 50, less than 40, less than 35, less than 30, less than 25, or at least 10, or at least 15° (including any range in between). In some embodiments, medium-polar carbon dots are characterized by water contact angles of less than 70, less than 65, less than 60, less than 55, or at least 40, or at least 50° (including any range in between).

[0083] In some embodiments, the nonpolar carbon dots are characterized by a water contact angle of less than 100, less than 90, less than 85, less than 80, less than 75, or at least less than 65, or at least 70° (including any range in between).

[0084] In some embodiments, the carbon dots of the present invention are substantially metal-free.

[0085] In some embodiments, the carbon dots of the present invention are characterized by particle sizes in the ranges of 1-100 nm, 1-10 nm, 1-5 nm, 5-10 nm, 10-20 nm, 20-50 nm, 10-50 nm, and 50-100 nm (including any range in between). In some embodiments, the carbon dots of the present invention are characterized by particle sizes in the ranges of 1-5 nm, 1-3 nm, 3-5 nm, 5-7 nm, and 7-10 nm (including any range in between).

[0086] In some embodiments, the term "particle size" refers to the average cross-sectional size of carbon dots.

[0087] In some embodiments, the term “average cross-sectional size” may refer to the average of at least 70%, 80%, 90%, or 95% of the carbon dots, or in some embodiments, to the median size of a plurality of carbon dots. In some embodiments, the term “average cross-sectional size” may refer to the number average of a plurality of carbon dots. In some embodiments, the term “average cross-sectional size” may refer to the average diameter of substantially spherical carbon dots.

[0088] In some embodiments, each of the polar, medium-polar, and non-polar carbon dots of the present invention is characterized by different sensitivities to a particular VC, such as those described herein.

[0089] In some embodiments, each of the polar, medium-polar, and non-polar carbon dots of the present invention is characterized by enhanced binding affinity to the target VC compared to a control. In some embodiments, the binding affinity of the carbon dot to the target VC is enhanced by at least 10%, at least 20%, at least 50%, at least 100%, at least 2 times, at least 3 times, at least 5 times, at least 10 times, at least 100 times, at least 1,000 times, at least 10,000 times, and at least 1,000,000 times (including any range in between) compared to the binding affinity to the control.

[0090] In some embodiments, each of the polar, medium-polar, and non-polar carbon dots of the present invention is characterized by enhanced binding affinity to the desired VC compared to a control, where the concentrations of the desired VC and the control in the sample are substantially the same (e.g., less than 1000 ppm, less than 100 ppm, less than 50 ppm, less than 40 ppm, or about 35 ppm (including any range in between)). As used herein, the term "ppm" refers to ppmv, i.e., parts per million of a volume, as known in the art.

[0091] In some embodiments, the term “enhanced binding affinity” refers to the affinity ratio between (i) the binding affinity of the carbon dot to the VC of interest and (ii) the binding affinity of the carbon dot to the control. In some embodiments, enhanced binding affinity refers to an enhanced change in the capacitance of the carbon dot (and / or sensing unit) upon exposure of the carbon dot (and / or sensing unit) to the VC of interest, compared to the control. In some embodiments, the VC of interest is derived from a microorganism (e.g., bacteria or viruses). The binding affinity to a particular VC may be estimated from the response intensity of each sensor (e.g., each of the first, second, and / or third sensors in the sensor array of the present invention) upon exposure to that particular VC. For example, enhancing the binding affinity of the carbon dot to a particular VC will enhance the response of the sensor and / or array of the present invention. Thus, the affinity ratios of polar, medium-polar, or non-polar carbon dots predetermine the selectivity and / or sensitivity of each of the first, second, and / or third sensors described herein, respectively.

[0092] In some embodiments, the different surface polarities of the polar, medium-polar, and non-polar carbon dots predetermine their different affinities to a particular VC. In some embodiments, the different surface polarities of the polar, medium-polar, and non-polar carbon dots predetermine that each sensor in the array is configured to generate a different capacitance signal in response to a particular gas molecule or analyte (e.g., VC).

[0093] In some embodiments, the term "response" refers to a signal generated by the sensor in response to a change in the capacitance of the sensing unit.

[0094] In some embodiments, the control is a gas. In some embodiments, the control refers to any atmospheric gas (e.g., N2, O2, CO2, Ar, water, or any combination thereof). In some embodiments, the concentration of the control in a given gaseous sample is substantially the same as or higher than the concentration of the VC of interest. For example, the concentration of the VC of interest in a gaseous sample (e.g., ammonia, DMF, hexane) may be 1–1000 ppm, 1–10 ppm, 10–50 ppm, 50–100 ppm, 100–200 ppm, 200–500 ppm, 500–1000 ppm (including any range in between), and the concentration of the control in the gaseous sample (e.g., water vapor) may be about 1% (or about 60%).

[0095] In another embodiment, a sensor comprising an array of the present invention is provided, the array comprising a plurality of capacitive sensors, each sensor comprising at least two electrodes and at least one sensing unit electrically connected to the at least two electrodes and comprising a structure made from a plurality of carbon dots of the present invention, the sensor being configured to detect one or more desired VCs in a sample.

[0096] In some embodiments, the sensor and / or system of the present invention is configured to determine or predict the presence and concentration of one or more target microorganisms in a sample. In some embodiments, the sample is a gaseous sample. In some embodiments, the gaseous sample includes one or more target VCs and a control. As used herein, the term VC (or VOC) refers to small organic and / or inorganic molecules (typically having a molecular weight of less than 1000 Da or less than 500 Da) characterized by a high vapor pressure at room temperature (e.g., at least 10 to 10 atm). In some embodiments, the target VC is devoid of any one of the gases in the atmosphere.

[0097] In some embodiments, the sensor (and / or system comprising the same) of the present invention is configured to determine or predict the presence and concentration of a target species at a given location. In some embodiments, the location is selected from solids, liquids, and gases (including any range between them). In some embodiments, the target species is a microorganism (e.g., a virus, bacteria, and fungi or a combination thereof). In some embodiments, the target species is VC. In some embodiments, the array (and / or system comprising the same) of the present invention is configured to detect the presence of a microorganism within a specific location.

[0098] In some embodiments, each of the first, second, and / or third sensors in the sensor array of the present invention is characterized by sensitivity and / or binding affinity to a particular VC, the sensitivity and binding affinity being described herein. In some embodiments, each of the first, second, and / or third sensors in the sensor array of the present invention is characterized by different sensitivity and / or different binding affinity to a particular VC.

[0099] As used herein, the term “sensitivity” refers to the ratio between the signal intensity of the sensor in response to a particular VC of interest and the signal intensity of the sensor in response to a control (e.g., referred to as 1), where the control and the VC of interest are present in the sample.

[0100] Therefore, the term "enhanced" when referring to sensitivity relates to the enhancement of the signal in response to the target VC compared to the sensor's response to a control (where the signal intensity in response to the control is optionally referred to as 1). In some embodiments, the target VC comprises one or more VCs. In some embodiments, the target VC comprises polar VCs, non-polar VCs, medium-polar VCs, or any combination thereof. In some embodiments, one or more target VCs are present in the sample in the ppb and / or ppm range. In some embodiments, the concentration of the control in the sample is substantially the same as or higher than the concentration of the target VC.

[0101] In some embodiments, the nonpolar VC is a nonpolar compound (e.g., hexane, xylene, toluene, benzene) characterized by a dipole moment of 0–0.5, 0–0.4, 0–0.1, 0.1–0.5, 0.4–0.5 (including any range in between).

[0102] In some embodiments, the polar VC is a polar compound (e.g., ammonia, water, ethanol, butanol, acetone, aldehyde) characterized by a dipole moment of at least 0.5, at least 0.6, at least 0.8, at least 1, at least 1.5, or more (including any range in between).

[0103] In some embodiments, the first sensor (including the polar C dot) is optionally characterized by its sensitivity to polar VCs, including amines, hydroxyl, carboxyl, carbonyl, or any combination thereof. Non-limiting examples of polar VCs include, but are not limited to, ammonia, alcohols (methanol, butanol), or any combination thereof. In some embodiments, the first sensor (including the polar C dot) is characterized by enhanced sensitivity to polar VCs compared to the second and / or third sensor (including the non-polar C dot), the enhancement being as described herein.

[0104] In some embodiments, the second sensor (including a medium-polarity C dot) is characterized by its sensitivity to a medium-polarity VC (e.g., DMF).

[0105] In some embodiments, the third sensor (including non-polar C dots) is characterized by its sensitivity to non-polar VCs that include an aromatic ring, an alkyl chain, or both. In some embodiments, the third sensor is characterized by having enhanced sensitivity to non-polar VCs compared to the first and / or second sensors (where enhancement is as described herein). Non-limiting examples of non-polar VCs include aliphatic hydrocarbons that include partially unsaturated hydrocarbons (e.g., hexane, pentane, methane, alkenes, alkynes), aromatic hydrocarbons (e.g., toluene, benzene), or any combination thereof, but are not limited thereto.

[0106] In some embodiments, the sensing unit includes at least two electrodes that contact a plurality of carbon dots. In some embodiments, the carbon dots are bonded to the outer surface of the electrodes. In some embodiments, the carbon dots are in the form of a layer on the electrodes. In some embodiments, the carbon dots are in the form of a multi-layer structure on the electrodes.

[0107] In some embodiments, the surface roughness of the sensing unit is about 1 nm, about 0.5 nm, about 2 nm, about 3 nm, about 5 nm, about 8 nm, about 10 nm, about 0.1 nm (including any range therebetween). An exemplary surface roughness of the sensing unit is as shown in FIG. 2A.

[0108] Non-limiting examples of the size of such sensing elements are 0.1 μm 2 ~1 cm 2 、0.1 μm 2 ~1 μm 2 、1 μm 2 ~10 μm 2 、10 μm 2 ~100 μm 2 、100 μm 2 ~1 mm 2 、1 mm 2 ~1 cm 2These are cross-sectional dimensions within the range (including any range in between), specifically the ranges of 1nm to 100nm, 1nm to 5nm, 5nm to 10nm, and 10nm to 100nm (including any range in between).

[0109] Herein, we refer to Figure 9, which illustrates some embodiments of the capacitance sensor of the present invention. The capacitance sensor 100 may include two electrodes 110 and 120 and a sensing element 130 connected to the electrodes 110 and 120, which includes a structure made from the particles 10 disclosed below. The electrodes 110 and 120 are configured to receive electricity. The electrodes 110 and 120 may be connected to a processor / computing device capable of detecting changes in the capacitance of the sensing element 130 in response to changes in the nearby chemical environment, for example, due to the presence of a species of interest (e.g., VC).

[0110] In some embodiments, the sensing element 130 may include a plurality of carbon dots 10. In some embodiments, each sensing element 130 includes a carbon dot having a different surface polarity. The sensing element 130 may include carbon dots that are coupled to or in contact with the conductive material 140. The conductive material 140 is further electrically connected to the electrodes 110 and 120. The conductive material 140 may be in the form of an electrode array.

[0111] In some embodiments, the sensitivity and / or selectivity of the system and / or array of the present invention for the target species (e.g., VC) is enhanced by at least 10%, at least 20%, at least 50%, at least 100%, at least 2 times, at least 3 times, at least 5 times, at least 10 times, at least 100 times, at least 1,000 times, at least 10,000 times, and at least 1,000,000 times (including any range in between) compared to a control.

[0112] In some embodiments, the term “sensitivity” refers to the ratio between the signal intensity of a system and / or array responding to a species of interest (e.g., VC, or VC derived from microorganisms) and the signal intensity of a system and / or array responding to a control (e.g., atmospheric gas).

[0113] In some embodiments, the sensor of the present invention is configured to detect the presence and / or concentration of a target VC at a given location. In some embodiments, the sensor of the present invention is configured to determine or predict the presence of a target microorganism in a sample by analyzing capacitance values ​​obtained from the sample. In some embodiments, the sensor of the present invention is configured to determine or predict the presence of a target microorganism at a given location, and the sample is associated with that location. In some embodiments, the location is selected from a gaseous sample, a liquid sample, a solid sample, or any combination thereof.

[0114] In some embodiments, the sensitivity of the sensor is predetermined by the affinity and / or sensitivity of each sensing unit in the array to a particular VC. In some embodiments, the sensor is configured to selectively predict or determine the presence of a target VC in a sample.

[0115] In some embodiments, the sensor is configured to selectively detect (or predict) the presence of a target microorganism in a sample. In some embodiments, the sensor is characterized by a detection (or prediction) threshold for a microbial load (e.g., bacterial load) in a sample (e.g., a liquid sample) of at least 10 CFU / ml, at least 100 CFU / ml, at least 500 CFU / ml, at least 800 CFU / ml, at least 900 CFU / ml, or at least 1,000 CFU / ml, at least 10,000 CFU / ml, or at least 100,000 CFU / ml (including any range in between).

[0116] method In another embodiment, a method is provided for determining the presence of a target species in a sample, and this method is a. Exposing a sensor to a sample, wherein the sensor comprises a sensing unit, and the sensing unit comprises a sensor array of the present invention or at least one capacitive sensor as described herein. b. Supplying electricity to the sensor and thereby obtaining multiple capacitance values ​​generated by the sensor, c. Analyzing capacitance values ​​to determine or predict the presence and / or concentration of a target species in a sample. In some embodiments, the sample is a gaseous sample. In some embodiments, the sample is a liquid or solid sample. In some embodiments, the target species is as described herein (e.g., microorganisms such as VC, bacteria, fungi, viruses). In some embodiments, obtaining multiple capacitance values ​​involves receiving multiple capacitance signals generated by at least one capacitance sensor of the present invention or by a sensor array of the present invention (e.g., by each of at least one sensors in the array).

[0117] In another embodiment, a method is provided for predicting the presence of a target microorganism, which can be performed by at least one processor or computing device, the method comprising: a. receiving a plurality of capacitance signals generated by at least one capacitance sensor in response to exposure to a sample containing a plurality of VCs; b. extracting at least one capacitance feature from the capacitance signals of at least one respective capacitance sensor; c. introducing at least one capacitance feature into a machine learning (ML) model, the ML model being trained to predict the presence of a target microorganism in the sample based on the capacitance feature; and d. in an inference stage, applying the ML model to the capacitance signals obtained from the sample to predict the presence of a target microorganism in the sample, wherein the at least one capacitance sensor is or comprises the sensor of the present invention (for example, having a sensing unit comprising any combination thereof, such as any of the first, second, and third sensors of the present invention or a sensor array as described herein). In some embodiments, the at least one capacitance sensor comprises a plurality of capacitance sensors. In some embodiments, at least one capacitive sensor comprises a sensor array including a plurality of capacitive sensors, each of which comprises a sensing unit including a carbon dot as described herein. In some embodiments, each of the capacitive sensors comprises a carbon dot characterized by substantially the same or different polarities. In some embodiments, at least one capacitive sensor comprises the sensor array of the present invention.

[0118] In some embodiments, the plurality of capacitance signals in step a include capacitance signals generated by at least one capacitance sensor. In some embodiments, the plurality of capacitance signals in step a include capacitance signals generated by each of a plurality of capacitance sensors. In some embodiments, the plurality of capacitance signals in step a include capacitance signals generated by a plurality of sensors in the sensor array of the present invention. In some embodiments, the plurality of capacitance signals in step a include capacitance signals generated by each of the sensors in the sensor array of the present invention.

[0119] In some embodiments, providing electricity may include providing electricity (e.g., AC, DC, or both) to at least a portion of the sensors in the array. In some embodiments, providing electricity may include measuring at least one of the capacitance, conductivity, resistance, impedance, current, and voltage of a sensor when the sensor is exposed to one or more VCs, one or more of which optionally originate from the microorganism of interest.

[0120] When the sensor is exposed to the target VC, the capacitance of the sensing element changes. In some embodiments, measurements relating to the capacitance of the sensing element between two electrodes before, during, and after exposure to the target VC indicate the amount and / or type of target VC attached to / coupled to the sensing element. In some embodiments, the measurements are selected from time capacitance, time-dependent capacitance, base capacitance (e.g., background capacitance), and maximum capacitance. In some embodiments, the measurements are positive and / or negative capacitance values.

[0121] In some embodiments, the measured values ​​(e.g., positive and / or negative capacitance values) may be further analyzed and manipulated (by a computing device) to further extract one or more features (e.g., capacitance features). In some embodiments, the measured values ​​are further processed, for example, using arbitrary mathematical correlations (e.g., first derivative) to extract at least one feature therefrom. In some embodiments, the extracted features (e.g., capacitance features) are selected from the maximum decrease capacitance, the difference between the maximum and minimum capacitance values, the average capacitance value, the maximum capacitance value, the minimum capacitance value, the first derivative, the second derivative, the signal-to-noise ratio, the slope, the decrease slope, the rise time, the overshoot value relative to the steady state value, the vibration decay in time, and the vibration frequency. In some embodiments, the extracted features include positive and / or negative values. In some embodiments, the extracted features include absolute values.

[0122] In some embodiments, the measured or extracted features indicate the presence, absence, and / or quantity of the target microorganism in the sample. In some embodiments, the measured and / or extracted features may be analyzed to predict the presence and / or quantity of the target microorganism. In some embodiments, the analysis includes comparing the extracted features with pre-stored data, for example, using any mathematical correlation and / or calibration curve. In some embodiments, the mathematical correlation is one of linear correlation, parabolic correlation, polynomial correlation, logarithmic correlation, exponential correlation, and power correlation. In some embodiments, the measured and / or extracted features may be analyzed using software (e.g., PCA) or a machine learning model to generate an index of the presence and / or concentration of the target microorganism in the sample.

[0123] In some embodiments, the analysis includes extracting at least one capacitance feature from the measurements and introducing at least one capacitance feature into a machine learning (ML) model, which is trained to predict the presence of a target microorganism in the sample based on the capacitance feature.

[0124] In some embodiments, the method further includes an inference step for generating an indicator of the presence and / or concentration of the target microorganism in the sample based on predictions.

[0125] In some embodiments, the inference step includes applying an ML model to the capacitance signal obtained from the sample to generate an indicator of the presence and / or concentration of the target microorganism in the sample based on the prediction.

[0126] In some embodiments, the analysis includes introducing multiple capacitance signals into an ML model, which is trained to predict the presence of a target microorganism in a sample based on the capacitance signals, and generating an index of the presence and / or concentration of the target microorganism in the sample based on the prediction.

[0127] In some embodiments, the analysis includes extracting capacitance features from measured capacitance values, introducing the capacitance features into an ML model which is trained to predict the presence of a target microorganism based on the capacitance features, and generating an index of the presence and / or concentration of the target microorganism in the sample based on the prediction.

[0128] In some embodiments, extracting capacitance features includes sampling capacitance signals acquired from at least one of each capacitance sensor at predetermined time intervals and generating capacitance features representing the sample vectors for at least one of each capacitance sensor. In some embodiments, extracting capacitance features includes sampling capacitance signals acquired from each of the capacitance sensors in an array at predetermined time intervals and generating capacitance features representing the sample vectors for each of the capacitance sensors.

[0129] In some embodiments, extracting capacitance features further includes calculating the maximum change in capacitance (e.g., based on a sample vector) and generating a capacitance feature representing the calculated maximum capacitance. In some embodiments, the maximum change in capacitance refers to the change in capacitance upon exposure to the target VC compared to exposure to a control (e.g., atmospheric gas), which is also referred herein to as baseline capacitance. In some embodiments, the maximum capacitance is calculated by subtracting the baseline capacitance from the maximum capacitance value obtained when the capacitance sensor is exposed to the target VC.

[0130] In some embodiments, training an ML model includes (i) receiving a number of training samples, the training samples being labeled according to the presence of a species of interest (e.g., a microorganism), and (ii) using the labeled training samples as supervisory data for training an ML model to predict the presence and / or concentration of the microorganism of interest in the samples.

[0131] In some embodiments, the training sample is labeled according to the presence and / or concentration of the target VC. In some embodiments, the training sample includes at least one capacitance feature associated with each of at least one capacitance sensors. In some embodiments, the training sample includes at least one capacitance feature associated with a specific capacitance sensor in the array and labeled according to the presence or absence and / or concentration of the target microorganism in the sample. In some embodiments, each training sample includes at least one capacitance feature associated with a specific capacitance sensor in the array and a label indicating the presence or absence and / or concentration of the target VC in the sample. In some embodiments, the training sample includes a plurality of capacitance features, each capacitance feature associated with a specific capacitance sensor in the array, and the training sample is labeled according to the presence, absence, and / or concentration of one or more target microorganisms (or VC) in the sample.

[0132] In some embodiments, the method is characterized by a predictive accuracy of at least 80%, at least 85%, at least 90%, at least 95%, and at least 98% (including any range in between).

[0133] In some embodiments, the analysis step is performed by at least one computing device. In some embodiments, one of steps a to c is performed by at least one processor. In some embodiments, the at least one processor resides in an operable communication non-temporary computer-readable storage medium storing program instructions, which are executable by at least one hardware processor.

[0134] In some embodiments, one of steps a to c is performed by at least one computing device. In some embodiments, the computing device includes a non-temporary computer-readable storage medium storing program instructions, which are executable by at least one hardware processor.

[0135] Computing devices may include, for example, a central processing unit (CPU) processor, a chip, or any suitable computing or computing device, an operating system, memory, executable code, a storage system, an input device, and an output device, which may be a processor or controller. A processor (or, optionally, one or more controllers or processors across multiple units or devices) may be configured to perform the methods described herein and / or to perform or function as various modules, units, etc. Two or more computing devices may be included in a system according to embodiments of the present invention, and one or more computing devices may function as components of the system.

[0136] The operating system is or may include any code segment (e.g., similar to the executable code described herein) designed and / or configured to perform tasks including coordinating, scheduling, arbitrating, monitoring, controlling, or other management operations of a computing device, such as scheduling the execution of a software program or task, or making a software program or other module or unit communicative. The operating system may be a commercial operating system. The operating system may be any component, and it should be noted that, for example, in some embodiments, the system may include computing devices that do not require or do not include an operating system.

[0137] Memory may be, for example, random access memory (RAM), read-only memory (ROM), dynamic RAM (DRAM), synchronous DRAM (SD-RAM), double data rate (DDR) memory chips, flash memory, volatile memory, non-volatile memory, cache memory, buffers, short-term memory units, long-term memory units, or other suitable memory units or storage units, or may include them. Memory may be, or include, multiple, possibly different memory units. Memory may be a non-temporarily readable medium of a computer or processor, or a non-temporarily stored medium of a computer, such as RAM. In one embodiment, a non-temporarily stored medium such as memory, a hard disk drive, or another storage device may store instructions or code that, when executed by a processor, cause the processor to perform a method as described herein.

[0138] The executable code may be any executable code, such as an application, program, process, task, or script. The executable code may be executed by a processor or controller under the control of the operating system. For example, the executable code may be an application that is TBD, as further described herein. For clarity, a single item of executable code in a system according to some embodiments of the present invention may include multiple executable code segments similar to executable code that are loaded into memory and cause a processor to execute the methods described herein.

[0139] The storage system may be, for example, a flash memory known in the art, a memory located inside or embedded in a microcontroller or chip known in the art, a hard disk drive, a CD recordable (CD-R) drive, a Blu-ray disc (BD), a Universal Serial Bus (USB) device, or other suitable removable and / or fixed storage unit, or may include such a unit. Data TBDs may be stored in the storage system, or loaded from the storage system into memory, where they may be processed by a processor or controller. In some embodiments, some of the components described herein may be omitted. For example, the memory may be a non-volatile memory having the storage capacity of the storage system. Furthermore, the storage system may be embedded in memory or contained within memory.

[0140] The input device may be any suitable input device, component, or system, such as a detachable keyboard or keypad, or mouse, and may include such devices. The output device may include one or more (potentially detachable) displays or monitors, speakers, and / or any other suitable output devices. Any applicable input / output (I / O) device may be connected to the computing device. For example, a wired or wireless network interface card (NIC), a Universal Serial Bus (USB) device, or an external hard drive may be included in the input and / or output devices. It will be recognized that any number of suitable input and output devices may be operably connected to the computing device.

[0141] Systems according to some embodiments of the present invention may include, but are not limited to, a plurality of central processing units (CPUs) or any other suitable multipurpose or specific processor or controller, a plurality of input units, a plurality of output units, a plurality of memory units, and a plurality of storage units.

[0142] The cache memory may be, for example, a Layer 1 (L1) cache module, a Layer 2 (L2) cache module, and / or a Layer 3 (e.g., L3) cache memory module, or may include them, as is known in the art. The cache memory may include, for example, an instruction cache memory space and / or a data cache memory space, and may be configured to cooperate with one or more processors and / or one or more processing cores to perform at least one method according to embodiments of the present invention. The cache memory may typically be implemented on the same die or chip as the processor and may therefore be characterized by a memory bandwidth higher than the bandwidth of the memory and storage system.

[0143] In some embodiments, the sample comprises multiple gas species (e.g., VC). In some embodiments, the sample comprises one or more target gas species (e.g., VC derived from microorganisms) and at least one additional gas species comprising one or more atmospheric gases (e.g., N2, O2, CO2, and / or water).

[0144] In some embodiments, this method is for selectively detecting or predicting the presence of a target species in a sample. In some embodiments, the target species is VC, and the concentration of VC in the gaseous sample is at least 1 ppb, at least 10 ppb, at least 50 ppb, at least 100 ppb, at least 500 ppb, at least 1 ppm, at least 10 ppm, at least 20 ppm, at least 25 ppm, at least 30 ppm, at least 35 ppm, at least 40 ppm, at least 50 ppm, at least 60 ppm, at least 70 ppm, at least 80 ppm, at least 90 ppm, and at least 100 ppm (including any range in between).

[0145] In some embodiments, the target species is a virus, and the concentration of the virus in the gaseous sample is at least 10 3 , at least 10 4 , at least 10 5 , at least 10 6 , at least 10 7 , at least 10 8 It is billions / ml (including any range within that).

[0146] In some embodiments, the species of interest is a microorganism (e.g., bacteria and / or fungi), and the microbial load in the sample (e.g., liquid sample, gaseous sample, solid sample) is at least 10 CFU / ml, at least 100 CFU / ml, at least 500 CFU / ml, at least 800 CFU / ml, at least 900 CFU / ml, or at least 1000 CFU / ml (including any range in between).

[0147] In some embodiments, step a of the method is performed under operable conditions. In some embodiments, the operable conditions include (i) an exposure time of at least 1 second, at least 2 seconds, at least 5 seconds, at least 8 seconds, at least 10 seconds, at least 30 seconds, at least 60 seconds, at least 1.5 minutes, at least 2 minutes, at least 3 minutes, at least 5 minutes, at least 10 minutes, or 10 seconds to 10 minutes, 10 seconds to 60 seconds, 1 minute to 2 minutes, 1 minute to 10 minutes, 2 minutes to 5 minutes, 5 minutes to 10 minutes (including any range in between); and (ii) an operable temperature of -10 to 100°C, 0 to 10°C, 0 to 25°C, 10 to 30°C, 10 to 20°C, 10 to 40°C, 20 to 30°C, 30 to 40°C, 40 to 50°C (including any range in between). In some embodiments, the operable conditions include a temperature of 10 to 50°C. In some embodiments, step a of the method includes an exposure time sufficient to obtain a stable capacitance signal (for example, the capacitance value remains substantially unchanged such that the first derivative of the time-dependent capacitance signal is approximately zero).

[0148] In some embodiments, the operable conditions further include a pressure of 0.5 to 2 bar and relative humidity of 1 to 100%, 1 to 97%, 60 to 70%, 10 to 100%, 1 to 10%, 10 to 30%, 30 to 100%, 40 to 100%, 40 to 60%, 40 to 70%, 40 to 80%, 60 to 80%, and 80 to 100% (including any range in between).

[0149] In some embodiments, exposure involves providing the sensor of the present invention in close proximity to a location suspected to contain the target species (e.g., microorganisms). In some embodiments, exposure involves bringing the sensor into contact with a flowing gas sample. In some embodiments, the flowing gas sample is 1 to 1000 cm³. 3 / min, 1~10cm 3 / min, 10~20cm 3 / min, 10~80cm 3 / min, 10~100cm 3 / min, 100~200cm 3 / min, 200~500cm 3 / min, 500~1000cm 3 It is characterized by the flow velocity per minute (including any range within that period).

[0150] The present invention can be embodied in other specific forms without departing from its spirit or essential features. Therefore, the embodiments described herein should be considered illustrative in all respects rather than limiting the invention as described herein. Thus, the scope of the invention is indicated by the appended claims rather than by the foregoing description, and all modifications that fall within the meaning and scope of equivalence of the claims are intended to be encompassed therein.

[0151] General definition As used herein, the term “approximately” refers to ±10%.

[0152] The words "comprises," "comprising," "includes," "including," "having," and their cognates all mean "to include, but not limited to."

[0153] The term "consisting of" means "including and limiting to."

[0154] The phrase "consisting essentially of" means that a composition, method, or structure may include additional components, processes, and / or parts only if the additional components, processes, and / or parts do not substantially alter the basic and novel features of the claimed composition, method, or structure.

[0155] The term “exemplary” is used herein to mean “serving as an example, illustration, or illustration.” Any embodiment described as “exemplary” should not necessarily be construed as being preferable or advantageous to other embodiments, and / or preclude the incorporation of features from other embodiments.

[0156] The term “optionally” is used herein to mean “provided in some embodiments and not provided in other embodiments.” Any particular embodiment of the present invention may include multiple “optionally” features, provided that such features do not conflict.

[0157] As used herein, the singular forms "a," "an," and "the" include multiple references unless otherwise clearly indicated by the context. For example, the terms "one compound" or "at least one compound" may include multiple compounds, including mixtures thereof.

[0158] Embodiments of the present invention are not limited in this respect, but descriptions using terms such as “process,” “calculate,” “compute,” “determine,” “establish,” “analyze,” and “check” may refer to the operation and / or process of a computer, computing platform, computing system, or other electronic computing device that manipulates and / or converts data represented as physical (e.g., electrical) quantities in computer registers and / or memory to other data similarly represented as physical quantities in computer registers and / or memory, or other non-temporary information storage media, which can store instructions for performing an operation and / or process.

[0159] Embodiments of the present invention are not limited in this respect, but as used herein, the terms “plurality” and “a plurality” may include, for example, “multiple” or “two or more.” The terms “plurality” or “a plurality” may be used throughout this specification to describe two or more components, devices, elements, units, parameters, etc. The term “set” may include one or more items as used herein.

[0160] Unless expressly stated otherwise, the method embodiments described herein are not limited to a specific order or sequence. In addition, some of the described method embodiments or elements thereof may occur or be executed simultaneously, at the same time, or concurrently.

[0161] Throughout this application, various embodiments of the invention may be presented in range form. It should be understood that the range form is merely for convenience and brevity and should not be interpreted as a definitive limitation on the scope of the invention. Therefore, a range description should be considered to specifically disclose all possible subranges and the individual values ​​within those ranges. For example, a range description such as 1-6 should be considered to specifically disclose subranges such as 1-3, 1-4, 1-5, 2-4, 2-6, 3-6, and the individual numbers within those ranges, such as 1, 2, 3, 4, 5, and 6. This applies regardless of the width of the range.

[0162] As used herein, the term “substantially” means at least 80%, at least 85%, at least 90%, at least 92%, at least 95%, at least 96%, at least 97%, at least 98%, and at least 99% (including any range or value in between). Whenever a numerical range is indicated herein, it means including any quoted digit (fraction or integer) within that range. The phrases “in the range between” the first and second indicated numbers and “in the range between” the first and second indicated numbers are used interchangeably herein and mean including the first and second indicated numbers and all fractions and integers between them.

[0163] Furthermore, for example, all numerical values ​​referring to the amount or range of elements constituting a compound are approximations that can vary by up to 20%, and sometimes up to 10%, from the stated value (+) or (-). It should be understood that the term "approximately" is always preceded by the term "approximately," even if not explicitly stated.

[0164] The term “essentially consisting of” is used to define a formulation that includes the enumerated elements but excludes other elements that may have essential significance with respect to the formulation. Thus, “consisting of” shall mean excluding trace elements or more of the other elements. Embodiments defined by each of these transitional terms are within the scope of the invention. As used herein, the term “method” refers to methods, means, techniques, and procedures for accomplishing a given task, including but not limited to methods, means, techniques, and procedures known from or readily developed from known methods, means, techniques, and procedures by practitioners of the fields of chemistry, pharmacology, biology, biochemistry, and medicine.

[0165] As used herein, the term “treat” includes inhibiting, substantially inhibiting, delaying, or reversing the progression of a condition, substantially improving the clinical or aesthetic symptoms of a condition, or substantially preventing the appearance of the clinical or aesthetic symptoms of a condition.

[0166] As used herein, the term "alkyl" refers to aliphatic hydrocarbons containing linear and branched groups. In some embodiments, alkyl groups have 1 to 20 carbon atoms, 1 to 10, 1 to 5, 5 to 10, 10 to 15, or 15 to 20 carbon atoms (including any range in between).

[0167] In some embodiments, alkyls include short alkyls and / or long alkyls. In some embodiments, alkyls have 21 to 100 or more carbon atoms. In the context of the present invention, a “long alkyl” is an alkyl having at least 20 carbon atoms in its back chain (the longest path of continuous covalent atoms). Thus, short alkyls have 20 or fewer back chain carbons (e.g., 2, 3, 4, 5, 6, 8, 10, 15, or 20). Alkyls may or may not be substituted as defined herein.

[0168] As used herein, the term "alkyl" also encompasses saturated or unsaturated hydrocarbons, and therefore the term further encompasses alkenyls and alkynyls.

[0169] The term "alkenyl" represents an unsaturated alkyl group having at least two carbon atoms and at least one carbon-carbon double bond, as defined herein. The alkenyl may be substituted with or unsubstituted with one or more substituents, as described above.

[0170] As defined herein, the term "alkynyl" refers to an unsaturated alkyl group having at least two carbon atoms and at least one carbon-carbon triple bond. The alkynyl may be substituted with or unsubstituted with one or more substituents as described above.

[0171] The term "cycloalkyl" refers to a monocyclic or fused ring (i.e., a ring sharing an adjacent pair of carbon atoms) group that does not have a fully conjugated pi-electron system of one or more rings. Cycloalkyl groups may or may not be substituted, as shown herein.

[0172] The term "aryl" refers to an all-carbon monocyclic or fused polycyclic (i.e., a ring sharing adjacent carbon atom pairs) group having a fully conjugated pi-electron system. The aryl group may or may not be substituted, as shown herein.

[0173] The term "alkoxy" refers to both O-alkyl and -O-cycloalkyl groups as defined herein. The term "aryloxy" refers to -O-aryl groups as defined herein.

[0174] Each of the alkyl, cycloalkyl, and aryl groups in the general formulas herein may be substituted with one or more substituents, so that each substituent can independently be, for example, a halide, alkyl, alkoxy, cycloalkyl, nitro, amino, hydroxyl, thiol, thioalkoxy, carboxy, amide, aryl, and aryloxy, depending on the substituent and its position within the molecule. Additional substituents are also conceivable.

[0175] The terms “halide,” “halogen,” or “halo” represent fluorine, chlorine, bromine, or iodine. The term “haloalkyl” represents an alkyl group as defined herein, further substituted by one or more halides. The term “haloalkoxy” represents an alkoxy group as defined herein, further substituted by one or more halides. The term “hydroxyl” or “hydroxy” represents an -OH group. The term “mercapto” or “thiol” represents an -SH group. The term “thioalkoxy” represents both -S-alkyl groups and S-cycloalkyl groups as defined herein. The term “thioaryloxy” represents both -S-aryl groups and -S-heteroaryl groups as defined herein. The term “amino” represents an -NR'R'' group having R' and R'', or a salt thereof, as described herein.

[0176] The term "heterocyclyl" refers to a monocyclic or fused ring group having one or more atoms in the ring, such as nitrogen, oxygen, and sulfur. The ring may also have one or more double bonds. However, the ring does not have a fully conjugated pi-electron system. Typical examples include piperidine, piperazine, tetrahydrofuran, tetrahydropyran, and morpholino.

[0177] The term "carboxy" refers to a -C(O)OR' group or its carboxylate salt, where R' is hydrogen, alkyl, cycloalkyl, alkenyl, aryl, heteroaryl (e.g., optionally bonded via a ring carbon or heteroatom) or heterocyclyl (e.g., optionally bonded via a ring carbon or heteroatom), as defined herein.

[0178] In some embodiments, R' and R'' are identical or different, and each of R' and R'' is independently selected from hydrogen, alkyl, cycloalkyl, alkenyl, aryl, heteroaryl (e.g., optionally bonded via ring carbon or heteroatom), or heterocyclyl (e.g., optionally bonded via ring carbon or heteroatom), as defined herein.

[0179] The term "carbonyl" represents the -C(O)R' group, where R' is as defined above. The term also includes its thio derivatives (thiocarboxy and thiocarbonyl).

[0180] The term "thiocarbonyl" represents a -C(S)R' group, where R' is as defined above. The "thiocarboxyl" group represents a -C(S)OR' group, where R' is as defined herein. The "sulfinyl" group represents a -S(O)R' group, where R' is as defined herein. The "sulfonyl" or "sulfonate" group represents a -S(O)2R' group, where R' is as defined herein.

[0181] The "carbamyl" or "carbamate" group represents the -OC(O)NR'R'' group, where R' is as defined herein and R'' is as defined for R'. The "nitro" group refers to the -NO2 group. As used herein, the term "amide" encompasses C-amides and N-amides. The term "C-amide" represents the -C(O)NR'R" terminal group or C(O)NR' bond group, as these terms are defined above herein, where R' and R'' are as defined herein. The term "N-amide" represents the -NR"C(O)R' terminal group or NR'C(O) bond group, as these terms are defined herein, where R' and R'' are as defined herein.

[0182] The "cyano" group refers to the "-CN" group. The terms "azo" or "diazo" represent the -N=NR' terminal group or N=N- bonded group, where these terms are as defined herein above, and R' is as defined herein above. The term "guanidine" represents the -R'NC(N)NR"R"' terminal group or R'NC(N)NR"- bonded group, where these terms are as defined herein above, and in the formula, R', R'', and R'' are as defined herein. As used herein, the term "azide" refers to the -N3 group. The term "sulfonamide" refers to the -S(O)2NR'R'' group, where R' and R'' are as defined herein.

[0183] The terms "phosphonyl" or "phosphonate" represent an -OP(O)-(OR')2 group, where R' is as defined herein above. The term "phosphinyl" represents an -PR'R'' group, where R' and R'' are as defined herein above. The term "alkylaryl" represents an alkyl group as defined herein, substituted with an aryl group as described herein. An example alkylaryl is benzyl.

[0184] The term "heteroaryl" refers to a monocyclic or fused ring (i.e., a ring sharing adjacent pairs of atoms) group having one or more atoms in the ring, such as nitrogen, oxygen, and sulfur, and further having a fully conjugated pi-electron system. As used herein, the term "heteroaryl" refers to an aromatic ring in which at least one atom forming the aromatic ring is a heteroatom. Heteroaryl rings can be formed by 3, 4, 5, 6, 7, 8, 9, and more than 9 atoms. Heteroaryl groups can be optionally substituted. Examples of heteroaryl groups include, but are not limited to, aromatic C3-8 heterocyclic groups containing one oxygen or sulfur atom, or two oxygen atoms, or two sulfur atoms, or up to four nitrogen atoms, or combinations of one oxygen or sulfur atom and up to two nitrogen atoms, and their substituted derivatives, as well as benzo and pyrido condensation derivatives linked via one of the ring-forming carbon atoms, for example. In certain embodiments, the heteroaryl is selected from oxazolyl, isoxazolyl, oxadiazolyl, thiazolyl, isothiazolyl, pyridinyl, pyridadinyl, pyrimidinal, pyrazinyl, indolyl, benzimidazolyl, quinolinyl, isoquinolinyl, quinazolinyl, or quinoxalinyl.

[0185] For clarity, it is understood that certain features of the Invention described in the context of separate embodiments may also be provided in combination in a single embodiment. Conversely, for brevity, various features of the Invention described in the context of a single embodiment may also be provided separately, in any preferred partial combination, or as suitable in any other described embodiment of the Invention. Certain features described in the context of different embodiments should not be considered essential features of those embodiments unless the embodiments would not function without those elements.

[0186] The various embodiments of the present invention are presented for illustrative purposes only and are not intended to be exhaustive, nor are they limited to the embodiments disclosed. Many modifications and variations will be apparent to those skilled in the art, as they do not deviate from the scope and spirit of the described embodiments. The terminology used herein has been selected to best represent the principles of the embodiments, their practical applications or technical improvements to market-found technologies, or to enable those skilled in the art to understand the embodiments disclosed herein.

[0187] Various embodiments and aspects of the present invention are described above herein and experimentally supported in the following examples, as claimed in the following claims. [Examples]

[0188] With reference to the following embodiments, these embodiments illustrate some embodiments of the present invention in a non-limiting manner, along with the above description.

[0189] Materials and methods material Urea, p-phenylenediamine, citric acid, cobalt chloride hexahydrate (CoCl2·6H2O), lithium chloride (LiCl2), magnesium chloride (MgCl2), potassium carbonate (K2CO3), sodium chloride (NaCl), potassium chloride (KCl), potassium sulfate (K2SO4), toluene, n-hexane, dimethylformamide, ethyl acetate, methanol, and ammonium were purchased from Sigma Aldrich. Luria-Bertani (LB) agar was purchased from Pronadisa (Spain). Comb-shaped gold electrodes (dimensions: 10 × 6 × 0.75 mm; glass substrate; insulating layer: EPON SU8 resin; electrode material: Au; electrode thickness: 150 nm; microelectrode: 10 μm, microelectrode gap: 10 μm; number of fingers: 90 pairs) were purchased from MicruX Technologies (Oviedo, Spain). The bacteria used in the study were Escherichia coli DH10B wild type, Pseudomonas aeruginosa PAO1 wild type, Bacillus subtilis PY79, and Staphylococcus aureus wild type strain (generously provided by Professor Ariel Kushmaro of Ben Gurion University). Ultra-high purity distilled water (Millipore) was used in all experiments.

[0190] C-dot synthesis The synthesis of C dots employed a modified, reported procedure for constructing multiple polar C dots. Briefly, 0.2 g of urea, 0.1 g of citrate, and 0.2 g of p-phenylenediamine were dissolved in 50 mL of distilled water. The solution was then heated in a Teflon autoclave at 180 °C for 10 hours. After cooling to room temperature, the suspension was centrifuged twice at 11,000 rpm for 5 minutes, discarding larger aggregates. The resulting solution was purified by silica column chromatography using a mixture of toluene and methanol as the eluent. After collecting different fluorescent C dots exhibiting different colors / polarities, the C dots were dispersed in water before electrode deposition.

[0191] C-dot IDE sensor construction To prepare the C-dot IDE capacitance electrode, the inventors utilized a recently developed protocol. Briefly, a C-dot suspension (15 μL) was drop-cast onto a comb-type electrode (IDE) and allowed to dry overnight at room temperature. Before measurement, the resulting electrode was held at room temperature in an N2 environment.

[0192] Characteristic evaluation Atomic force microscopy (AFM): Using an AC160TS (Olympus) probe with a tip radius of 9 nm and a force constant of approximately 26 Nm-1, AFM images were acquired in AC mode (tapping mode) using the Cypher-ES, Asylum Research (Oxford Instrument) model. C-dot IDE samples and control IDE samples were measured in the capacitance detection region between gold IDE electrodes.

[0193] Water Contact Angle (WCA): The hydrophobicity of carbon dots was determined using a contact angle meter (Attension Theta Lite, Biolin Scientific, Finland). The contact angle was measured by applying 5 μL of water to the surface of the deposited C-dot sample and the control sample. The average WCA value was calculated.

[0194] Steam detection The setup of the gas apparatus for vapor generation and sensing (Figure 1) was based on a recent publication. Briefly, for the vapor sensing experiment, the inventors used an inert gas carrier—dry nitrogen—and split it into two components, bubbling one of the carrier flows through volatile organic compounds (n-hexane, toluene, dimethylformamide, ethyl acetate, methanol, and ammonium) at a variable flow rate. To detect changes in capacitance, a C-dot IDE electrode was placed in a detection chamber connected to an LCR meter (Keysight Technologies, E4980AL Precision LCR Meter). Vapor concentration was measured in the range of 5–95 ppmv by gas chromatography-mass spectrometry (Agilent 7890B / 5977A Series Gas Chromatograph / Mass Selective Detector). To calibrate the vapor concentration, the inventors used a mass flow controller (MFC) to determine the exact concentration that correlated with the GC-MS calibration curve. To generate different relative humidity (RH) environments, the inventors bubbled saturated aqueous solutions of different salts (potassium carbonate, cobalt chloride, and potassium sulfate, producing RH = 43%, 64%, and 97%, respectively) in closed glass containers at a constant temperature (25Co). The RH values ​​were verified using a standard humidity sensor (TH210, KIMO, Instruments, France).

[0195] All gas detection measurements were performed at 64% RH. Before testing, each electrode was saturated with 64% RH.

[0196] Capacitance measurements were performed at room temperature using a gas concentration of 35 ppmv under standard conditions when a C-dot IDE electrode was exposed to target vapor. Capacitance values ​​were recorded after generating a clear baseline with exposure to 64% RH, with data collected every 1.3 seconds. Changes in capacitance were recorded upon addition to various vapor analytes by generating specific flows (calibrated to the desired gas concentration). After reaching the saturated capacitance value, gas molecules were removed by flushing with N2 gas passing through a saturated CoCl2 aqueous solution (generating RH=64% vapor).

[0197] Bacterial growth and vapor detection Four bacterial strains were cultured in 37Co and 28Co Luria-Bertani (LB) medium for both Gram-negative and Gram-positive bacteria, respectively. Single bacterial colonies from LB agar plates were inoculated into 10 mL of LB broth and maintained at an appropriate temperature (37Co or 28Co) for 12 hours in a shaking incubator (220 rpm). The bacterial concentration in the medium was obtained by measuring the optical density at 600 nm (OD600). When OD600 reached 0.5, 50 μl of the bacterial culture was grown on solid LB agar in a 20 mL bail, kept under conditions adjusted using a water bath and maintained at a constant temperature. Bacterial gas release was monitored by placing an electrode 2.5 cm above the sample. Initial capacitance was obtained, and capacitance changes were measured at different time points.

[0198] Data Analysis The IDE capacitance value is defined as follows:

number

[0199] C is capacitance in farads (F), η is the number of fingers, ε0 is the permittivity of free space (ε0 = 8.854 × 10¹² F / m), ε r∫ is the relative dielectric constant (commonly known as dielectric constant), l is the length of the inter-digital electrode, t is the thickness of the inter-digital electrode, and d is the distance between the electrodes. IDE capacitance sensing relies on the modulation of the dielectric constant of the material placed on the electrodes. The dielectric constant is modulated by the absorption of various gas analyzers, causing capacitance change effects.

[0200] The sensor's capacitance response ΔC is defined as Cgas-C0, where Cgas and C0 are the saturated capacitance value after adding a gas analyte measured at a specific concentration under the same humidity (64% RH), and the capacitance baseline value measured at 64% RH, respectively. To compare electrodes, the baseline was adjusted to 0 nF (because all electrodes showed high initial capacitance values ​​in nF units).

[0201] Gas chromatography-mass spectrometry (GC-MS) The concentration of the analyte was detected using GC-MS at a specific flow rate (controlled by a mass flow controller). The unit's Agilent 7890B GC was connected to an Agilent 5977A single-quadrupole mass-selective detector. This instrument was equipped with a 100-vial autosampler, NIST02 MS, and ACD Labs MS Manager (software package for mass spectrum interpretation and structural elucidation). The column type was 35% phenylmethylsiloxane for MS; length 30 m; 0.25 mm, ID & 0.25 μm film thickness; the temperature was programmed to 25°C for 1 minute, 3°C / min up to 70°C, and 10°C / min up to 280°C. The transfer line temperature was 280°C, and the total run time was 37 minutes. A carrier (helium) gas flow rate of 2 ml / min was applied. Sample analysis was performed by solution (calibration) and vapor injection (splitless) into the GC with a sample size of 20 μl.

[0202] Concentration Determination - For each analyte, the inventors created calibration curves with known concentrations (5 ppmv to 95 ppmv) dissolved in suitable organic solvents. (Standard solutions (toluene, n-hexane, dimethylformamide, ethyl acetate, butanol, and ammonium of 99% purity or higher) were prepared using high-purity solvents.) All standards were prepared in methanol solution, except for the methanol standard prepared in acetonitrile. To construct the calibration curves, the results were quantified based on peak area using the extraction ion method performed by Masshunter qualitative analysis software. Target peak identification was confirmed with pure material. Analyte vapors were measured at various flow rates and examined using GC-MS in injection mode. The flow rates were then adjusted to produce a gas concentration of 35 ppmv for each analyte.

[0203] Impedance measurement Complex impedance spectra were measured from 1 Hz to 100 kHz for C-dot IDEs held at different humidity levels using an LCR meter (Keysight Technologies, E4980AL Precision LCR Meter) with a test voltage of 1 V at room temperature. To create various RH environments, saturated aqueous solutions of K2CO3, CoCl2, and K2SO4 were placed in airtight glass containers at 25°C to obtain atmospheres of 43%, 64%, and 97% RH. Each electrode was placed in a detection chamber connected to an LCR meter, where the real and imaginary values ​​of the impedance, Z' and Z'', were measured using the following impedance equation.

number

[0204] Here, R is resistance, f is frequency, and C is capacitance.

[0205] Machine Learning (ML) To report an unbiased and reliable estimate of the accuracy of the ML model, the inventors used a leave-one-out cross-validation procedure, as recommended for evaluating chemometric models with small sample sizes. The leave-one-out procedure is performed by training the model N times, where N is the number of readings from different sensors. In each training iteration, the inventors trained the model with all readings except those used to evaluate its predictive performance. In particular, each available reading is used only once to evaluate the model. The leave-one-out cross-validation procedure allows for obtaining an unbiased estimate of accuracy using the largest available training set (N-1).

[0206] Example 1 The objective of this study was to design a simple, highly sensitive artificial nose for continuous monitoring of vapor molecules. Figure 1 shows the design of a C-dot-based capacitive vapor sensor. C-dots exhibiting different polarities were synthesized from paraphenyldiamine, urea, and citrate as carbonaceous precursors and separated according to their polarity by liquid chromatography. In non-limiting examples, blue C-dots showed lower abundances of polar units on their surface, while orange and red C-dots contained higher concentrations of polar residues such as hydroxyl, carboxyl, and amine (different colors of chromatographically separated C-dot solutions are shown in Figure 1). Isolated C-dots exhibiting different polarities and colors were each drop-cast onto commercially available comb electrodes (IDEs; Figure 1, center). As shown on the right side of Figure 1, the capacitance measured by the C-dot IDE changed when the C-dot IDE was exposed to gas molecules. Importantly, the degree and direction of capacitance change (e.g., increase or decrease) differed significantly for each C-dot IDE electrode, depending on the polarity of both the deposited C-dot species and the detected gas molecules.

[0207] Figure 2 shows the characterization of the C-dot IDE system, particularly the investigation of the integration of C-dots on the electrode surface and their effects. The atomic force microscope (AFM) image in Figure 2A demonstrates the deposition of C-dots unevenly distributed in the space between the gold fingers containing the comb-shaped "comb." The diameter of the C-dots was approximately 5 nm, as is evident from the AFM height profile in Figure 2A.

[0208] Figure 2B presents the water contact angle (WCA) of IDEs coated with different C-dots, confirming the significant effect of C-dot polarity on the macroscopic IDE surface properties. In fact, Figure 2B demonstrates a direct relationship between the surface polarity of the C-dots and the degree of hydrophobicity of the electrodes. For example, the WCA of electrodes coated with red C-dots, which exhibit the highest polarity among the C-dots used, decreased from 300 to 230, reflecting the abundant polarity units on the C-dots (Figure 2B). In comparison, the WCA increased to 580 and 740 for IDEs coated with orange and blue C-dots, respectively, explaining the lower polarity of these C-dots, which has a more pronounced effect on the hydrophobicity of the electrodes.

[0209] Figure 3 shows the capacitance profiles of C-dot IDEs measured during exposure to different gases. Figure 3A shows the capacitance curves recorded for three C-dot IDE sensors during exposure to toluene (representing a non-polar gas molecule), dimethylformamide (DMF, representing a medium-polarity substance), and ammonia (a highly polar molecule). The C-dot IDEs were initially exposed to 64% humidity (room temperature), resulting in the adsorption and equilibration of water molecules on the electrode surface coated with C-dots. Figure 3A shows the capacitance changes induced by the three gas molecules at each electrode. Both toluene and dimethylformamide (DMF) caused a decrease in capacitance, to varying degrees depending on the deposited C-dots. In contrast, ammonia resulted in higher capacitance at all three electrodes (Figure 3A). Importantly, the capacitance curves in Figure 3A highlight a fast capacitance response of approximately 50 seconds (depending on the gas species). Such a capacitance response is one of the fastest recorded for capacitive vapor sensors and explains the rapid adsorption of gas molecules to the electrode surface. Furthermore, Figure 3A also demonstrates that purging the C-dot IDE with air (64% humidity) restores its initial capacitance value, making it easier to reuse the C-dot IDE sensor for multiple measurements.

[0210] Figure 3B presents capacitance dose-response graphs recorded when a red C-dot IDE is exposed to different concentrations of NH3 and DMF vapor [concentrations were determined by gas chromatography-mass spectrometry (GC-MS)]. The dose-response analysis in Figure 3Bi shows two linear regions, one from 0 to 50 ppmv and the other from 50 to 100 ppmv. The two linear capacitance response domains likely correspond to different mechanisms of ammonia adsorption onto the C-dot IDE surface. Different NH3 concentration-dependent surface adsorption regimes have been reported, showing monolayer formation of NH3 at low concentrations and multilayer aggregates at high ammonia concentrations. In the case of exposure of the red C-dot IDE sensor to DMF, a single linear dependence is evident (Figure 3Bii), which likely reflects a single adsorption process of DMF molecules. Note that the recorded negative capacitance change explains the low dielectric constant of DMF gas molecules adsorbed on the electrode surface. Both dose-response curves in Figure 3B show a detection threshold of approximately 5 ppmv, which underlies the excellent sensitivity of the C-dot IDE platform.

[0211] The bar graph in Figure 3C summarizes the capacitance response signals induced at all three electrodes by gas target molecules across a wide polarity range (all gas concentrations are 35 ppmv). The graph in Figure 3C shows significant variations in the capacitance response for each gas target (i.e., capacitance "fingerprint"), depending on both the polarity of the gas molecule and the polarity of the C dots deposited on the electrode surface. For example, sensors containing blue C dots showed significant negative capacitance signals when relatively nonpolar ethyl acetate, toluene, or hexane were added, while more polar gas molecules such as ammonia, methanol, or butanol had little effect on the decrease in capacitance (or increase in capacitance in the case of ammonia).

[0212] Importantly, the capacitance response data in Figure 3C demonstrate that the correlation between the polarity of the gas molecules and the electrode-deposited C dots constitutes a core determinant influencing both the magnitude and direction (negative / positive) of the sensor signal. For example, Figure 3C reveals that IDE sensors coated with non-polar blue C dots exhibited the most pronounced (negative) capacitance signals for the non-polar gases toluene and hexane, while highly polar red C dot IDE electrodes showed the highest (and most positive) capacitance changes upon exposure to polar gases ammonia and methanol. Interestingly, the orange C dot IDE sensor electrodes featured the highest sensitivity (e.g., the most pronounced capacitance decrease) to ethyl acetate and DMF, indicating intermediate polarity among the gases investigated. While recent studies have reported polarity-based modulation of the optical properties of C dots, the data in Figure 3 represent the first example of a macroscopic and coordinated effect of polarity-dependent transformation occurring in C dot systems.

[0213] Capacitive response profiles of gas molecules using the C-dot IDE electrode system outlined in Figure 3 can be used for selective detection of gas targets via a machine learning (ML)-based detection model, demonstrating the sensor's applicability as an effective "artificial nose" (Table 1). In a non-restrictive example, the ML strategy employed used capacitance change values ​​obtained for different electrodes as input attributes to train a model designed to identify which gas molecules induce a given sensor reading. In a non-restrictive example, instead of individually training dedicated binary models for each gas, the gas identification scheme implemented by the inventors is formulated as a multi-label classification task. This model allows a single sensor reading to be assigned to many labels (gases) simultaneously. In particular, the multi-label classifier can better capture the statistical interactions between electrode values ​​in the presence of a gas mixture. In a non-restrictive example, the inventors employ the Rakel++ algorithm to solve the multi-label classification task by constructing an ensemble of models, each of which considers a random subset of gases. To train all the basic models, the inventors used the “random forest” algorithm, which trains many decision trees independently while injecting randomness to ensure diversity between trees. The inventors focused on random forests because this approach fits well with a relatively limited number of readings (as in this case), excluding the application of other machine learning methods (such as deep learning) that require much larger training sets. [Table 1]

[0214] Accuracy: The percentage of correct predictions (both "true positive" and "true negative") among all measurements. AUC: The area under the receiver operating characteristic (ROC) curve describes the quality of prediction for "true positive" versus "false positive" readings. The top of the table shows the predictive performance of the ML model for each gas individually, while the bottom shows the subset accuracy for correctly detecting different gas mixtures.

[0215] Table 1 highlights the excellent predictive performance of the ML-based model applied here (details of the application of the ML model to the capacitance response data are described in the Experiments section). Specifically, Table 1 shows that the obtained “precision” values ​​(corresponding to the percentage of correct detections of both “true positive” and “true negative” in all tested cases) were almost all above 80%, with an average of close to 90%, indicating relatively accurate prediction of detected gas molecules. Similarly, the AUC, the area under the receiver operating characteristic (ROC) curve reflecting the trade-off between true positive and false positive rates, was around 0.9 (average 0.87), indicating satisfactory “true positive” prediction even with a strict threshold.

[0216] Furthermore, the fabricated C-dot IDE electrodes were subjected to stability testing. Stability testing was performed by recording the capacitance change signal during the adsorption and desorption of DMF at a concentration of 35 ppmv. The red C-dot IDE electrodes were tested at several point points, from the preparation day (day 0) to 1, 5, 10, 15, and 30 days later. Even after 30 days, the capacitance results showed excellent stability and reproducibility. All electrodes were maintained under the same temperature conditions in an N2 environment.

[0217] Table 1 further demonstrates that the ML model utilizing the C-dot IDE capacitance signal can also accurately predict the gas mixture composition. To illustrate this aspect, the inventors evaluated “subset accuracy,” a very stringent evaluation parameter requiring that the predicted set of gases in the mixture perfectly match the true set of gases (e.g., detecting only some gases or extra gases is considered a false positive). Importantly, as shown in Table 1, the ML model achieved a relatively high average subset accuracy of 83%. Such predictive performance highlights the ability of the C-dot IDE platform to detect individual gas targets in the mixture. Overall, the ML analyses outlined in Table 1 highlight predictive performance that is equivalent to or better than reported ML applications in chemical assays.

[0218] To decipher the mechanistic basis of the remarkable selectivity and sensitivity of the C-dot IDE capacitive gas sensor, the inventors performed electrochemical impedance spectroscopy (Figure 4). Generally, impedance measured in capacitive systems is strongly dependent on the charge transfer process occurring at the electrode-vapor interface. Therefore, impedance spectroscopy shows remarkable sensitivity to electrode surface characteristics and reveals the surface characteristics and processes resulting from the adsorption of gas molecules.

[44] Figure 3A shows Nyquist plots recorded for the orange C-dot IDE under different humidity conditions (i.e., different RH values). The semicircle diameters in the Nyquist plots shown in Figure 4A represent the charge transfer resistance (Rct) at the electrode surface. Importantly, Figure 4A shows that the Rct decreases as the C-dot IDE is placed in a higher humidity environment.

[0219] The close relationship between humidity and charge transfer resistance reflects the affinity of water molecules to the electrode surface, particularly their binding to polar residues (mainly OH and COOH units) on the C-dot surface. Therefore, a higher concentration of physically adsorbed water molecules on the C-dot IDE surface will result in a smaller Rct due to the conductivity of the water molecules. Indeed, the nearly linear relationship between the real and imaginary impedance values ​​(i.e., a semicircle reduction corresponding to a very small Rct) was evident at RH=97%, which is due to the substantial concentration of water molecules adsorbed on the C-dot IDE surface.

[0220] Figure 4B shows Nyquist plots recorded at RH=64% for the orange C-dot IDE after exposure to various gases (gas concentration was 35 ppmv). Figure 4B reveals the close relationship between gas molecule polarity and impedance changes. In non-limiting examples, exposure to DMF, BuOH, and toluene resulted in significantly more pronounced Rct (e.g., wider semicircles; Rct values ​​extracted from Nyquist plots are outlined in Table S1).

[0221] [Table 2]

[0222] Table S1 shows the response time and recovery time in seconds based on the capacitance change curves induced by three gas molecules at each electrode. The response time was considered to be the gas saturation time that elicits a constant capacitance signal. The recovery time consists of the time it takes for the capacitance signal to return from the point where it reaches 64%RH to the baseline capacitance signal.

[0223] The mechanistic image derived from the impedance spectroscopy data in Figure 4B highlights the substitution of adsorbed water on the electrode surface by vapor molecules. In non-limiting examples, two factors shape the change in capacitance and its magnitude. When adsorbed water molecules are replaced by gases exhibiting lower polarity and lower dielectric constant than water (DMF, BuOH, and toluene), Rct decreases (due to the presence of less polar adsorbed molecules), and in parallel, the capacitance becomes more negative (this explains the low dielectric constant of the adsorbed molecular layer). Importantly, the degree of water substitution in the C-dot IDE depends on the "matching" between the polarity of the electrode-displayed C-dot and the vapor molecules. For example, in the case of the orange C-dot IDE sensor, the Rct (Figure 4B) and capacitance changes (Figure 2C) induced by DMF were far more pronounced than with toluene, although DMF exhibits higher polarity and a greater dielectric constant than toluene. This result is due to a better matching between the polarity of DMF and the orange C-dot.

[0224] In contrast to the relatively low polarity of DMF, BuOH, and toluene, ammonia was highly polar, resulting in a lower Rct (smaller diameter semicircle, Figure 4B, black curve). This increased conductance is attributed to the formation of an ammonia layer physically adsorbed onto the aqueous layer. Such “double-layer” ammonia adsorption previously reported on metal surfaces is due to extensive hydrogen bonding between the adsorbed ammonia molecules and the sedimentary water. This phenomenon also explains the recorded higher dielectric constant and consequently higher capacitance (i.e., Figure 3C).

[0225] Figure 5 illustrates the use of a C-dot IDE artificial nose for both continuous monitoring of bacterial growth and identification of bacterial species. Figure 5A shows the experimental setup. A C-dot IDE sensor was placed at a short distance over the surface (solid agar matrix) on which bacteria were grown. Capacitive signals induced by volatile compounds released by the growing bacteria were continuously monitored to generate a real-time in-line profile of the gas molecules released by the bacteria. Importantly, although the scheme in Figure 5A shows a single C-dot IDE sensor, an array containing different C-dot species (i.e., blue C-dot IDE, orange C-dot IDE, and red C-dot IDE) can be used simultaneously to function as an artificial nose for bacterial detection.

[0226] Figure 5B shows the capacitance response curves induced by volatile compounds released by different bacterial stains using a C-dot IDE artificial nose. The graphs in Figure 5B show the increase or decrease in capacitance induced in three C-dot IDE sensors (containing blue C-dots, orange C-dots, and red C-dots, respectively) when exposed to the same amount of bacterial cells initially placed on the agar surface beneath the sensor electrode (e.g., Figure 5A). The experimental data in Figure 5B reveal significant differences in the capacitance signals produced by each bacterial species. In non-limiting examples, E. coli and P. aeruginosa produced increases in capacitance on the three C-dot IDE sensing platforms to varying degrees, while the growth of B. subtilis and S. aureus induced decreases in recorded capacitance. The different capacitance profiles are due to the different compositions of volatile compounds, including amines, sulfides, and hydrocarbons, released by different bacterial species. In particular, the graph in Figure 5B reveals a significant difference in capacitance response between Gram-negative bacteria (E. coli and P. aeruginosa) and Gram-positive bacteria (B. subtilis and S. aureus), which reflects the higher concentration of volatile polar molecules released by Gram-negative bacteria compared to the more abundant nonpolar gaseous compounds secreted by Gram-positive bacterial cells. Specifically, the time-dependent capacitance curves in Figures 5B and 5E closely follow the bacterial growth curves determined by conventional turbidity assays.

[0227] The bar graph in Figure 5C summarizes the capacitance changes recorded after 20 hours of exposure to growing bacteria inoculated at the same initial concentration with C-dot IDE, demonstrating that the C-dot IDE artificial nose can distinguish each bacterial species by the "capacitive fingerprints" produced by the three C-dot IDE electrodes (blue, orange, and red; Figure 5C). In a non-limiting example, P. aeruginosa produced high positive capacitance responses at the three electrodes, resulting in capacitance change ratios of 1.00:0.63:0.26 (red C-dot IDE: orange C-dot IDE: blue C-dot IDE). E. coli, in comparison, showed much lower capacitance changes and different signal ratios of 1.00:0.54:0.33. Figure 5C shows that a significant difference in capacitance response is equally evident between the more negative capacitance changes induced by B. subtilis (capacitance change ratio of 0.55:1.00:0.47; red C-dot IDE: orange C-dot IDE: blue C-dot IDE) and S. aureus (capacitance change ratio of 0.43:1.00:0.77).

[0228] Capacitive response data of bacteria obtained using the C-dot IDE artificial nose were classified according to principal component analysis (PCA) (Figure 5D), highlighting the feasibility of distinguishing between bacterial species. In a non-restrictive example, Figure 5D shows a score plot in the first two principal component spaces, where PC1 accounts for the largest total variation (95.10%), and each point represents three independent capacitance measurements. Importantly, clustering of experimental data points in the PCA plot reveals no overlap between the different bacterial species tested, demonstrating that the C-dot IDE artificial nose readily distinguishes between bacteria. Notably, excellent selectivity was achieved without relying on the recognition of specific bacterial metabolites, a difficult task used in most previously reported vapor-based bacterial sensing techniques. Characteristic capacitance fingerprints observed for the tested bacterial species were obtained with only three electrodes, highlighting the applicability of the C-dot IDE artificial nose for the detection and growth monitoring of different bacterial strains.

[0229] In conclusion, the inventors present a novel capacitive nose technology for real-time vapor sensing based on IDEs coated with carbon dots exhibiting defined surface polarity and optical properties. In particular, the high surface area and variations in C-dot surface polarity provide excellent sensitivity and selectivity. Using a C-dot IDE array containing three C-dot species (highly polar red C-dots, medium-polar orange C-dots, and relatively nonpolar blue C-dots), a clear change in capacitance was obtained depending on the C-dot polarity. In non-limiting examples, experimental data show significant variability in vapor-induced capacitance changes depending on the matching between the polarities of both the electrode-deposited C-dots and the gas molecules. In particular, the application of machine learning models utilizing capacitance response data yielded excellent predictability for both individual gases and synthesized gas mixtures. Impedance spectroscopy revealed a possible mechanism underlying the capacitance conversion of the C-dot IDE sensor, pointing to the substitution of C-dot adsorbed water by gas molecules as a major factor influencing capacitance changes. The C-dot IDE capacitive nose was successfully employed for continuous, real-time monitoring of bacterial growth. Importantly, the recorded characteristic capacitance signals enabled the differentiation between different Gram-positive and Gram-negative bacteria. Overall, the novel capacitance C-dot-based artificial nose can be easily implemented as a portable vapor sensor for continuous, non-invasive monitoring and identification of bacterial growth in a variety of applications, including medical diagnostics, food processing, and environmental monitoring.

[0230] Virus recognition in vapor The COVID-19 pandemic caused immense damage to society and led to widespread panic. As a result, the severity of SARS-CoV-2, the causative coronavirus infection, was greatly underestimated by society at the start of the COVID-19 outbreak. Based on this, the inventors conducted tests for virus detection using an exemplary sensor of the present invention containing red C dots with high surface polarity.

[0231] Two airborne viruses were tested: lentivirus (LV) and human coronavirus HCoV-OC43.

[0232] LV Lentiviruses are a genus of retroviruses that cause chronic and fatal diseases in humans and other mammalian species, characterized by long incubation periods.

[0233] Lentivirus culture: 7.5 × 10⁶ HEK293T cells were placed on a poly-K coated 10cm plate (Sigma) in DMEM medium containing 10% FBS, pen-strep, HEPES, L-glutamine, non-essential amino acids, and sodium pyruvate (all from Biological Industries). 10 μg of pHAGE2 lenti vector and 3 μg of packaging plasmids tat, rev, hgpm2, and vsvg were transfected in a 1:1:1:2 ratio using JetPrime® reagent (Polyplus) according to the company's protocol. The medium was replaced with fresh, complete DMEM 4 hours after transfection. Fresh medium was added on days 2 and 3 post-transfection and collected on day 4. The medium containing LV was filtered through a 0.45 μm PVDF membrane and divided equally as supernatant, or transferred to an ultracentrifuge tube (Beckman Coulter) and centrifuged at 17,000 RPM at 4°C for 90 minutes. The LV pellets were suspended, divided into equal portions, and stored at -20°C.

[0234] HCoV-OC43 Human coronaviruses are members of the betacoronavirus species that infect humans and cattle. Infectious coronaviruses are enveloped, positive-sense single-stranded RNA viruses that enter host cells by binding to N-acetyl-9-O-acetylneuraminate receptors. OC43 is one of seven coronaviruses known to infect humans. It is one of the viruses that cause the common cold and functions as an analogue of the COVID-19 virus.

[0235] HCoV-OC43 culture: The HCoV-OC43 viral genome was extracted from 500 μL of patient samples using the NucliSENS easyMAG kit (BioMerieux, Marcy-l'Etoile, France). To determine HCoV-OC43, all samples were subjected to qRT-PCR. As previously described, HCT8 cells were cultured with HCoV-OC43 in complete RPMI medium containing 10% FBS, pen-strep, HEPES, L-glutamine, non-essential amino acids, and sodium pyruvate (all from Biological Indutries, Beit Haemek, Israel) in a humidified 5% CO-2 370C incubator. After 5 days of incubation, the supernatant (sup) was collected and cell debris was discarded by centrifugation. The supernatant was divided equally into 0.5 ml low-bind tubes and kept at -80°C. The stock was tested on HCT8 cells.

[0236] Figure 10 shows the experimental setup (Figure 10A) containing aerosolized virus generated by a pressurized airflow pipe connected to a jet sprayer containing a diluted virus solution. All electrodes were drop-cast with 10 μl of solution containing 2 mg / ml of C dots (polar C dots as described herein). After addition, the samples were allowed to dry overnight. Initial capacitance values ​​were obtained for each electrode. 10 ml of solution was packed into the aerosol system. Aerosol vapor was generated for 2 minutes using an airflow of 10 L / min. Final capacitance values ​​were recorded after drying at room temperature for 2 hours. As an important control, the capacitance of a DMEM solution control was measured after the same treatment.

[0237] Figure 10B shows the capacitance ratio to the initial capacitance value between electrodes after virus recognition. The DMEM control without the virus showed a clear decrease in capacitance value compared to the recognition of both viruses, which mainly revealed an increase in capacitance after recognition.

[0238] In addition, one electrode from each group was correlated to different virus concentrations and tested for 5-minute and 10-minute aerosol exposures (Figure 10C). Aerosol vapor was generated for an additional 3-minute and 5-minute periods using an airflow of 10 L / min. Final capacitance values ​​were recorded after drying at room temperature for 2 hours. Notably, after 5 minutes, an increase in the capacitance signal was observed for the air sample containing virus particles, while the capacitance signal of the control sample remained largely unchanged.

[0239] These results confirm the significant potential of using C-dot IDE electrodes for virus detection. More research is needed to optimize the approach and test a variety of viruses.

[0240] While the present invention has been described in conjunction with its specific embodiments, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. Therefore, it is intended to encompass all such alternatives, modifications, and variations that fall within the spirit and broad scope of the appended claims.

[0241] All publications, patents, and patent applications referenced herein are incorporated herein by reference in full to the same extent as each individual publication, patent, or patent application is specifically and individually indicated as being incorporated herein by reference. Furthermore, any citation or identification of any reference in this application should not be construed as an admission that such reference is available as prior art of the present invention. Section headings, to the extent that they are used, should not necessarily be construed as restrictive.

Claims

1. It is a sensor array, The aforementioned sensor array comprises a plurality of capacitive sensors, Each capacitive sensor is electrically connected to two electrodes and includes a sensing element containing a carbon dot. The plurality of capacitance sensors comprises at least a first sensor, a second sensor, and a third sensor. The carbon dots of the first sensor, the second sensor, and the third sensor each independently contain hydrophilic surface groups, and each independently contains nitrogen and oxygen atoms. The hydrophilic surface groups of the carbon dots are predetermined to have (i) different surface polarities and (ii) different sensitivities of the first sensor, the second sensor, and the third sensor to volatile compounds (VC). The nitrogen content of the carbon dots in the first sensor, the second sensor, and the third sensor is 13-20 at%, 10-12.9 at%, and 1-9.9 at%, respectively. Sensor array.

2. The sensor array according to claim 1, wherein the hydrophilic surface group comprises one of an amine group, an imine group, a carbonyl group, a carboxyl group, and a hydroxyl group, or any combination thereof.

3. The sensor array according to claim 1 or 2, wherein (i) the carbon content and (ii) the atomic ratios between the total nitrogen and oxygen content of the carbon dots of the first sensor, the second sensor, and the third sensor are 0.2 to 1.9, 2 to 2.9, and 3 to 8, respectively.

4. The sensor array according to claim 1, wherein the carbon dots lack metal.

5. The sensor array according to claim 2, wherein the w / w ratios between the amine group and the imine group of the carbon dots of the first sensor, the second sensor, and the third sensor are 1.5 to 3, 0.8 to 1.4, and 0.2 to 0.8, respectively.

6. The sensor array according to claim 1, wherein the oxygen content of the carbon dots of the first sensor, the second sensor, and the third sensor is 20-30 at%, 10-20 at%, and 1-9.9 at%, respectively.

7. The sensor array according to claim 1, wherein the carbon content of the carbon dots of the first sensor, the second sensor, and the third sensor is 40-65 at%, 66-79 at%, and 80-95 at%, respectively.

8. The sensor array according to claim 1, wherein the sensor array is configured to selectively detect a target VC originating from a microorganism.

9. The sensor array according to claim 1, wherein the VC includes a polar VC, a non-polar VC, or both.

10. The sensor array according to claim 9, wherein the first sensor is characterized by its sensitivity to the polar VC, and the polar VC optionally comprises an amine, hydroxyl, carboxyl, carbonyl, or any combination thereof.

11. The sensor array according to claim 9, wherein the third sensor is characterized by its sensitivity to the nonpolar VC, the nonpolar VC optionally comprises an aromatic ring, an alkyl chain, or both.

12. The sensor array according to claim 1, wherein the electrodes are configured to receive electricity.

13. The sensor array according to claim 1, wherein each of the carbon dots is characterized by a different water contact angle.

14. The sensor array according to claim 1, wherein the different polarities of each of the carbon dots are predetermined to enable each of the plurality of capacitance sensors to generate a different capacitance signal in response to VC.

15. A method for predicting the presence of at least one target microorganism, which can be executed by at least one processor, the method being: The steps include receiving multiple capacitance signals generated by multiple capacitance sensors in response to exposure of a sample to multiple capacitance sensors, A step of introducing the plurality of capacitance signals into a machine learning (ML) model, wherein the ML model is trained to predict the presence and concentration of a target microorganism in the sample based on the capacitance signals; and a step of generating an index of the presence or concentration of one or more of the target microorganisms in the sample based on the predictions. The plurality of capacitance sensors are the sensor array described in claim 1. method.

16. The method according to claim 15, wherein the introduction includes extracting at least one capacitance feature from the plurality of capacitance signals, and the ML model is further trained to predict the presence of the target microorganism based on the capacitance feature.

17. The method according to claim 16, wherein the extraction of capacitance features includes sampling the capacitance signal at predetermined time intervals and generating capacitance features representing a vector of the sampled capacitance signals.

18. The method according to claim 17, wherein the capacitance feature is associated with a specific capacitance sensor.

19. The method according to claim 16, wherein the extraction of capacitance features includes calculating the maximum change in capacitance and generating a capacitance feature representing the calculated maximum capacitance.

20. Training the aforementioned ML model is The method according to claim 15, comprising receiving a plurality of training samples, each of which is labeled by (i) the presence of the microorganism of interest in the sample and optionally by (ii) its concentration, or both (i) and (ii); and using the labeled training samples as supervisory data for training the ML model to predict the presence or concentration of the microorganism of interest in the samples.

21. The method according to claim 15, wherein the sample is obtained from an indoor or outdoor location.

22. The method according to claim 15, wherein the sample is a gaseous sample suspected to contain VC derived from the target microorganism.

23. The method according to claim 15, wherein the sensor array is operably communicating with a computing device having at least one hardware processor.

24. The method according to claim 23, wherein the computing device comprises a non-temporary computer-readable storage medium storing program instructions executable by the at least one hardware processor.

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