D-DNA and MXene composite material, preparation method thereof, gas sensor and array sensor

Abstract

The invention relates to the technical field of gas sensors, and provides a D-DNA and MXene composite material, a preparation method thereof, a gas sensor and an array sensor. The composite material is prepared from MXene and D-DNA (Deoxyribose Nucleic Acid). According to the composite material, stable compounding can be achieved through the electrostatic interaction between negative charges of a DNA phosphoric acid framework and MXene surface groups, the interface electron transmission efficiency is enhanced through the pi-pi stacking effect and the hydrogen-bond interaction between basic groups (such as guanine G and thymine T) and MXene transition metal layers, and the sensing activity of DNA native conformation is maintained. Therefore, the composite material disclosed by the invention has relatively good stability, meanwhile, the baseline recovery capability of the prepared gas sensor can be remarkably improved, and the sensitivity is improved by 1.2-1.6 times compared with that of a single MXene gas sensor.

Description

Technical Field

[0001] This invention relates to the field of gas sensor technology, specifically to a composite material of D-DNA and MXene, its preparation method, a gas sensor, and an array sensor. Background Technology

[0002] Gas sensors have significant applications in industrial production and medical diagnostics. In recent years, two-dimensional transition metal carbides / nitrides (MXenes) have shown great potential in gas sensing due to their excellent conductivity, large specific surface area, and abundant surface functional groups. However, standalone MXene sensors generally suffer from bottlenecks such as poor selectivity and low sensitivity. While existing composite modification strategies can improve sensitivity, the improvement in selectivity remains insignificant.

[0003] Currently, the main composite materials for MXene include MXene / Gr, MXene / MOS, MXene / TMDs, MXene / MOF, and MXene / polymer. Although sensors based on these composite materials have improved sensitivity to some extent, their limited selectivity restricts their practical applications. Some studies have used biomacromolecules (peptides) as a second phase in MXene composites, but the selectivity remains limited. Compared to proteins, DNA is more stable and possesses properties such as adjustable length, programmable sequence, and environmental friendliness, making it a potential candidate for improving MXene selectivity. Summary of the Invention

[0004] This invention aims to at least partially solve at least one of the technical problems existing in the prior art. Through extensive experimentation, the inventors discovered that natural DNA...

[0005] When (D-DNA) is combined with MXene, the baseline recovery capability of the prepared gas sensor is significantly improved, and the sensitivity is increased by 1.2-1.6 times compared with the MXene gas sensor alone, exhibiting good gas response.

[0006] This invention is based on the following discoveries of the inventors:

[0007] As an emerging two-dimensional material, MXene has significantly expanded its application potential in gas sensing by combining it with various functional materials. Currently researched MXene composite systems include MXene / Gr, MXene / MOS, MXene / transition metal diacids (TMDs), MXene / metal-organic frameworks (MOFs), and MXene / polymers. These composites can compensate for the performance shortcomings of single materials through synergistic effects, but each still faces unique challenges.

[0008] MXene / Gr composite material with Ti3C2T x Composite systems with reduced graphene oxide (rGO) or chemical vapor deposition graphene, for example, exhibit high electrical conductivity and good mechanical properties. However, due to the easy stacking of their two-dimensional sheet structures, they suffer from a loss of specific surface area and exhibit poor selectivity for nonpolar gases (such as CH4) or structurally similar VOCs (such as benzene and toluene). MXene / MOS composites (such as Ti3C2T) x / SnO2、Ti3C2T x MXene / ZnO is mostly prepared using hydrothermal synthesis or atomic layer deposition techniques. It utilizes the oxygen vacancies of MOS to catalyze the dissociation of gas molecules, and combines this with the high conductivity of MXene to reduce the sensor's operating temperature and shorten the response time. However, its hydrophilic surface easily adsorbs water molecules, resulting in high signal drift errors under high humidity. It also exhibits cross-sensitivity to broad-spectrum reducing gases (such as CO and ethanol), and poor specificity in target gas recognition. MXene / TMDs composites can improve NO detection sensitivity and response speed, but they have potential problems such as complex preparation processes or high costs. MXene / MOF composites can effectively improve the specific surface area and gas adsorption capacity of composite materials, but the intrinsic insulation of MOF restricts charge transport, and the weak interfacial bonding between MOF crystals and MXene leads to poor mechanical properties. MXene / polymer composites have good flexibility, and hydrophobic polymer coating can suppress humidity interference. However, the poor thermal stability of polymers limits their application in high-temperature scenarios, and non-specific adsorption results in a lack of selectivity.

[0009] Despite significant advancements in sensitivity, response speed, and environmental adaptability of the aforementioned composite materials, insufficient selectivity remains a core bottleneck limiting their practical application. While some studies have used biomolecules (peptides) as a second phase in MXene composites, the selectivity remains limited. In 2025, Yang et al. first revealed the selectivity of MXene for single-stranded DNA.

[0010] ssDNA exhibits superior adsorption properties and, compared to proteins, greater stability, along with advantages such as adjustable length and chemical modifiability. Therefore, programming the DNA base sequence into the MXene sensing interface will achieve high specificity and programmability for gases, thereby propelling MXene composite materials from the laboratory to real-world applications such as medical diagnostics and environmental monitoring.

[0011] The inventors discovered that natural DNA (D-DNA) can achieve efficient interfacial binding with MXene through the synergistic effect of its phosphate backbone and base sequence, providing a universal regulatory basis for optimizing sensing performance. The complexation of the two is mainly based on their physical and chemical interactions. From a physical perspective, there is an electrostatic interaction between the charge distribution on the MXene surface and the negative charge of the DNA phosphate backbone, allowing DNA to adsorb onto the MXene surface. Furthermore, the high specific surface area of ​​MXene provides numerous adsorption sites for DNA, increasing the likelihood of complexation. From a chemical perspective, functional groups on the MXene surface (such as -OH, -O, -F, etc.) can undergo hydrogen bonding, coordination, and other chemical reactions with the phosphate groups and bases of DNA, further enhancing the binding force between the two.

[0012] Based on this, in a first aspect, the present invention proposes a composite material. According to an embodiment of the present invention, the composite material comprises MXene and D-DNA. This composite material achieves stable recombination through the electrostatic interaction between the negative charge of the DNA phosphate backbone and the surface groups of MXene. The interfacial electron transport efficiency is enhanced through the π-π stacking effect and hydrogen bonding between bases (such as guanine G and thymine T) and the MXene transition metal layer, maintaining the sensing activity of the native conformation of DNA. Therefore, the composite material of the present invention exhibits good stability and significantly improves the baseline recovery capability of the prepared gas sensor, increasing the sensitivity by 1.2-1.6 times compared to a standalone MXene gas sensor.

[0013] According to embodiments of the present invention, the composite material may further include at least one of the following additional technical features:

[0014] According to an embodiment of the present invention, the length of the D-DNA is 5nt-20nt. For example, it can be 5nt, 6nt, 7nt, 8nt, 9nt, 10nt, 11nt, 12nt, 13nt, 14nt, 15nt, 16nt, 17nt, 18nt, 19nt, 20nt, etc., or a range of any of the above values. According to an embodiment of the present invention, the length of the D-DNA is 10nt. This improves the sensitivity of the gas sensor to gases.

[0015] In this invention, "D-DNA" refers to right-handed deoxyribonucleic acid, the main form of naturally occurring deoxyribonucleic acid (DNA) in living organisms. Its molecular structure exhibits a right-handed helix morphology.

[0016] According to embodiments of the present invention, the sequence of the D-DNA consists of one or two bases. It should be noted that when the sequence of the D-DNA consists of one base, the base is selected from any one of A (adenine), T (thymine), C (cytosine), and G (guanine); when the sequence of the D-DNA consists of two bases, the base is selected from any two of A (adenine), T (thymine), C (cytosine), and G (guanine).

[0017] In this invention, "DNA-A" refers to a DNA sequence consisting entirely of the base A; "DNA-C" refers to a DNA sequence consisting entirely of the base C; "DNA-G" refers to a DNA sequence consisting entirely of the base G; "DNA-T" refers to a DNA sequence consisting entirely of the base T; and "DNA-AT" refers to a DNA sequence consisting of both the base A and the base T.

[0018] According to an embodiment of the present invention, the D-DNA has any one of the sequences shown in SEQ ID NO: 1 to 28.

[0019] According to an embodiment of the present invention, the MXene is Ti3C2T. x .

[0020] According to an embodiment of the present invention, the molar ratio of MXene to D-DNA is (25-1000):1. For example, it can be 25:1, 50:1, 100:1, 200:1, 300:1, 400:1, 500:1, 600:1, 700:1, 800:1, 900:1, 1000:1, etc., or a range of any of the above values. Therefore, by keeping the molar ratio of MXene to D-DNA within the above range, the surface functional groups of D-DNA can interact with the surface functional groups of MXene, thereby providing more active sites and improving the sensitivity of the gas sensor when detecting gases.

[0021] According to an embodiment of the present invention, the 5′ or 3′ end of the D-DNA has a modifying group. Therefore, by chemically modifying the D-DNA, it is possible to directionally introduce specific functional groups while strengthening the DNA-MXene interface binding energy, constructing active sites for the specific adsorption of gas molecules, thereby improving the sensitivity of the gas sensor.

[0022] According to embodiments of the present invention, the modifying group includes at least one of amino, thiol, phosphate, biotin, cholesterol ester, and fluorescein. This improves the sensitivity of the gas sensor.

[0023] In a second aspect, the present invention provides a method for preparing the composite material described in the first aspect. According to an embodiment of the present invention, the method includes: mixing MXene and D-DNA to obtain the composite material. Due to the electrostatic interaction between the charge distribution on the MXene surface and the negative charge of the DNA phosphate backbone, DNA can be adsorbed onto the MXene surface through simple physical mixing, thereby efficiently obtaining the composite material of the present invention.

[0024] According to embodiments of the present invention, the method for preparing the composite material may further include at least one of the following additional technical features:

[0025] According to an embodiment of the present invention, the concentration of MXene is 0.01 mg / mL to 0.1 mg / mL based on the total mass of the mixed treatment products. For example, it can be 0.01 mg / mL, 0.02 mg / mL, 0.03 mg / mL, 0.04 mg / mL, 0.05 mg / mL, 0.06 mg / mL, 0.07 mg / mL, 0.08 mg / mL, 0.09 mg / mL, 0.1 mg / mL, etc., or a range of any of the above values. According to an embodiment of the present invention, the concentration of MXene is 0.05 mg / mL based on the total mass of the mixed treatment products. According to an embodiment of the present invention, the molar concentration of D-DNA is 0.1 μM to 10 μM based on the total mass of the mixed treatment products. For example, it can be 0.1 μM, 1 μM, 2 μM, 3 μM, 4 μM, 5 μM, 6 μM, 7 μM, 8 μM, 9 μM, 10 μM, etc., or a range of any of the above values. According to an embodiment of the present invention, the molar concentration of the D-DNA is 5 μM based on the total mass of the mixed treatment products. This allows the surface functional groups of the D-DNA to interact with the surface functional groups of MXene, thereby providing more active sites and improving the sensitivity of the gas sensor in detecting gases.

[0026] In a third aspect, the present invention proposes the use of the composite material described in the first aspect or the composite material prepared by the method described in the second aspect in the fabrication of a gas sensor. The intervention of DNA provides active sites for gas adsorption, enhancing the gas adsorption process on the material surface and thus affecting the interfacial charge transfer efficiency. Consequently, the gas sensor prepared using the composite material of the present invention exhibits better baseline stability and recognition accuracy, as well as excellent sensitivity.

[0027] In a fourth aspect, the present invention provides a gas sensor. According to embodiments of the invention, the gas sensor comprises the composite material described in the first aspect or a composite material prepared using the method described in the second aspect. The intervention of DNA provides active sites for gas adsorption, enhancing the gas adsorption process on the material surface and thus affecting the interfacial charge transfer efficiency. Consequently, the gas sensor of the present invention exhibits good baseline stability and recognition accuracy, as well as excellent sensitivity.

[0028] According to an embodiment of the present invention, the composite material is coated on the surface of the interdigital electrode sheet.

[0029] In a fifth aspect, the present invention provides a method for preparing a gas sensor. According to an embodiment of the invention, the method comprises: coating the composite material described in the first aspect or the composite material prepared by the method described in the second aspect onto the surface of an interdigitated electrode sheet, and drying to obtain the gas sensor. As previously stated, the intervention of DNA provides active sites for gas adsorption, enhancing the gas adsorption process on the material surface, thereby affecting the interfacial charge transfer efficiency. Therefore, the gas sensor prepared by the method of the present invention exhibits better baseline stability and recognition accuracy, as well as excellent sensitivity.

[0030] In a sixth aspect of the invention, the invention proposes the application of the gas sensor described in the fourth aspect in the detection of gases.

[0031] According to an embodiment of the present invention, the gas is derived from at least one of ammonia, ethanol, diethyl ether, and acetone.

[0032] In a seventh aspect, the present invention provides an array sensor. According to an embodiment of the invention, the array sensor includes a plurality of gas sensors as described in the fourth aspect. Thus, by combining multiple gas sensors, the shortcomings of a single gas sensor can be overcome, improving the selectivity and accuracy of gas detection. Simultaneously, different gases can be distinguished, achieving specific detection of different odor molecules.

[0033] It should be noted that the number of gas sensors in the array sensor and the composite material used for each gas sensor can be flexibly selected. Specifically, the composite materials in multiple gas sensors can be completely identical, partially identical, or even completely different to meet the detection needs of different application scenarios. At the same time, there is no fixed limit to the number of gas sensors; it can be adjusted according to actual needs, for example, it can be set to any number such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15. Those skilled in the art can comprehensively determine the number of gas sensors and the type of composite material based on specific detection targets, environmental conditions, and cost factors to achieve the best gas detection effect. For example, the composite material in the array sensor can be entirely made of unmodified D-DNA and MXene composite material, or it can be entirely made of modified D-DNA and MXene composite material, or it can simultaneously contain both unmodified D-DNA and MXene composite material and modified D-DNA and MXene composite material.

[0034] According to embodiments of the present invention, the composite materials in the plurality of gas sensors may be the same or different.

[0035] According to embodiments of the present invention, the composite materials in the plurality of gas sensors are all different. Therefore, different sensors have different response characteristics to different gases. By combining multiple sensors into an array, the response differences of each sensor can be utilized through data processing and analysis algorithms to achieve simultaneous detection and accurate identification of multiple gases. By comprehensively analyzing the response signals of each sensor, different gases can be effectively distinguished and their concentrations accurately measured, obtaining reliable detection results even in complex environments containing interfering gases.

[0036] According to an embodiment of the present invention, the array sensor may include an MXene gas sensor. The MXene gas sensor refers to a gas sensor composed solely of MXene.

[0037] In an eighth aspect of the invention, the present invention proposes applications of the array sensor described in the seventh aspect, said applications including at least one of the following: gas detection; identification of fruit odor molecules; identification of cancer patients; and identification of fermentation odor molecules. According to embodiments of the present invention, the array sensor can successfully achieve accurate classification of more than ten gases. With the help of machine learning algorithms, the array sensor has been practically applied to scenarios such as fruit odor molecules, fermentation gases, and the exhaled breath of cancer patients. The accuracy of the array sensor in identifying different gases has been significantly improved, demonstrating excellent sensing performance. Therefore, the DNA interface regulation strategy proposed in this invention not only significantly improves the selectivity and sensitivity of the MXene sensor, but the constructed array sensor also shows important application prospects in fields such as food quality monitoring and non-invasive disease diagnosis.

[0038] According to embodiments of the present invention, the gas originates from at least one of ammonia, acids, alcohols, aldehydes, ketones, ethers, and esters.

[0039] According to embodiments of the present invention, the gas is derived from at least one of ammonia, ethanol, diethyl ether, acetone, formic acid, n-heptanoic acid, farnesol, 4-isopropylbenzyl alcohol, dextrorotatory carvone, dihydrojasmone, ethyl acetate, γ-caprolactone, citral, and benzaldehyde.

[0040] It should be noted that when using array sensors to identify fruit odor molecules, cancer patients, and fermentation odor molecules, neural network algorithms are required. This involves combining relevant signal processing and machine learning algorithms to form an intelligent gas sensing system. By employing algorithms such as t-Distributed Stochastic Neighborhood Embedding (t-SEN), Principal Component Analysis (PCA), Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), and Support Vector Machine (SVM), feature information from the sensor array can be effectively extracted, enabling accurate identification and prediction of gas types and greatly enhancing the feasibility of multi-gas differentiation.

[0041] In a ninth aspect, the present invention proposes a gas sensing system. According to an embodiment of the present invention, the gas sensing system includes: a sensing generation module, the sensing generation module including the array sensor described in the seventh aspect, for acquiring chemical signals of a gas; a data conversion and transmission module for converting the chemical signals into electrical signals; and a data visualization processing module for inputting the electrical signals into a trained machine learning model to obtain the source of the gas. Thus, the gas sensing system of the present invention can successfully achieve accurate classification of more than ten gases. With the help of machine learning algorithms, the gas sensing system has been practically applied to scenarios such as fruit odor molecules, fermentation gases, and the exhalation of cancer patients. The accuracy of the gas sensing system in identifying different gases has been significantly improved, demonstrating excellent sensing performance. Therefore, the DNA interface regulation strategy proposed in this invention not only significantly improves the selectivity and sensitivity of the MXene sensor, but the constructed gas sensing system also shows important application prospects in fields such as food quality monitoring and non-invasive disease diagnosis.

[0042] According to embodiments of the present invention, the algorithm includes, but is not limited to, at least one of t-Distributed Stochastic Neighborhood Embedding (t-SEN), Principal Component Analysis (PCA), Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), and Support Vector Machine (SVM).

[0043] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0044] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0045] Figure 1 This is a synthesis pathway diagram of MXene nanosheets according to an embodiment of the present invention;

[0046] Figure 2 It is a natural DNA / Ti3C2T according to an embodiment of the present invention.x Synthesis path diagram;

[0047] Figure 3 This is a modified DNA / Ti3C2T according to an embodiment of the present invention. x Synthesis path diagram;

[0048] Figure 4 This refers to the surface morphology of a material according to an embodiment of the present invention; wherein,

[0049] a is Ti3C2T x TEM image; b is Ti3C2T x The corresponding AFM image; c is the thickness corresponding to the AFM image; d is the D-DNA / Ti3C2T x TEM image; e represents D-DNA / Ti3C2T x AFM image; f is related to D-DNA / Ti3C2T x The thickness corresponding to the AFM image;

[0050] Figure 5 This refers to the surface morphology of a material according to an embodiment of the present invention; wherein,

[0051] a represents modified DNA / Ti3C2T x AFM image; b is modified DNA / Ti3C2T x AFM images and corresponding thicknesses; c represents modified DNA / Ti3C2T x SEM image; d represents modified DNA / Ti3C2T x SEM images;

[0052] Figure 6 It is the crystal structure of the material according to an embodiment of the present invention; wherein,

[0053] a is Ti3C2T x XRD pattern; b is D-DNA / Ti3C2T x HRTEM image; c is Ti3C2T x HRTEM image; d represents Ti3C2T x SAED images;

[0054] Figure 7 It is the crystal structure of the material according to an embodiment of the present invention; wherein,

[0055] a represents modified DNA / Ti3C2T x XRD images; b is modified DNA / Ti3C2T x HRTEM image; c represents modified DNA / Ti3C2T xSAED images;

[0056] Figure 8 This is an elemental analysis of materials according to embodiments of the present invention; wherein,

[0057] a is D-DNA / Ti3C2T x XPS spectra; b represents D-DNA / Ti3C2T x TEM images and EDS element distribution images;

[0058] Figure 9 This is an elemental analysis of materials according to embodiments of the present invention; wherein,

[0059] a represents modified DNA / Ti3C2T x XPS spectra; b represents modified DNA / Ti3C2T x SEM images and EDS element distribution images;

[0060] Figure 10 DNA-A / Ti3C2T according to an embodiment of the present invention x Sensing properties of biocomposite materials; among which,

[0061] a is Ti3C2T x And 10nt, low concentration of DNA-A / Ti3C2T x Real-time response to acetone; b is Ti3C2T x And 10nt, low concentration of DNA-A / Ti3C2T x Response value analysis to acetone; c represents Ti3C2T x And 10nt, low concentration of DNA-A / Ti3C2T x A linear relationship exists between the logarithm of the response and the logarithm of the acetone concentration; d represents Ti3C2T x And 10nt, high concentration of DNA-A / Ti3C2T x Real-time response to acetone; e represents Ti3C2T x And 10nt, high concentration of DNA-A / Ti3C2T x Response value analysis to acetone; f represents Ti3C2T x And 10nt, high concentration of DNA-A / Ti3C2T x The linear relationship between the logarithm of the response and the logarithm of the acetone concentration;

[0062] Figure 11 This is the DNA / Ti3C2T according to an embodiment of the present invention. x Sensing properties of biocomposite materials; among which,

[0063] a is Ti3C2T xand low concentration DNA / Ti3C2T x Comparison of sensor response values ​​to ammonia; b represents Ti3C2T x and high concentration of DNA / Ti3C2T x Comparison of sensor response values ​​to ammonia; c represents Ti3C2T x and 16 low-concentration DNA / Ti3C2T x Analysis of the sensor's response to four gases; d represents Ti3C2T x and 16 high-concentration DNA / Ti3C2T x Analysis of sensor response to four gases; e and g represent low concentrations of DNA / Ti3C2T. x Sensitivity analysis of the sensor to four gases; f and h represent high concentrations of DNA / Ti3C2T. x Sensitivity analysis of the sensor to four gases; sensor1 is sensor 1 (Ti3C2T) x Sensor2 is sensor 2 (L-DNA-A / Ti3C2T). x Sensor3 is sensor 3 (L-DNA-T / Ti3C2T). x Sensor4 is sensor 4 (L-DNA-C / Ti3C2T). x Sensor5 is sensor 5 (L-DNA-G / Ti3C2T). x Ammonia, Ethanol, and Ether are all names of acetones.

[0064] Figure 12 The sensing characteristics of a five-channel sensor array according to an embodiment of the present invention; wherein,

[0065] a represents the array's sensing response to four typical gases at a concentration of 20 ppm; b represents the response value of each sensor in the array to the gas; c represents the percentage of response of each sensor in the array to a single gas; d represents the sensitivity analysis of each sensor in the array to the four typical gases; e represents the PCA image of the sensor array to the four typical gases at a concentration of 20 ppm.

[0066] Figure 13 This refers to 12 sets of dibasic DNA-AT / Ti3C2T according to embodiments of the present invention. x The sensing characteristics; among which,

[0067] a is a ball-and-stick model of 12 sets of dibasic DNA-AT sequences; b is a ball-and-stick model of 12 sets of dibasic DNA-AT / Ti3C2T sequences. x Response modes to four gases; c represents 12 groups of dinucleotide DNA-AT / Ti3C2T. xAnalysis of the average response values ​​for c1) acetone, c2) ammonia, c3) ethanol, c4) diethyl ether and c5);

[0068] Figure 14 This invention relates to an Arduino-based sensor array for identifying gaseous substances in fruits; wherein,

[0069] a) Statistical analysis of sensor array response; b) Box plot; c) Comparison of different algorithms; d) Average response of sensor array to fruit; e) Correlation between two sensors in the sensor array; f) Confusion matrix of sensor system for recognizing gaseous substances in fruit.

[0070] Figure 15 This is a correlation analysis of the exhalation of different populations by a sensor according to an embodiment of the present invention; wherein,

[0071] a) Correlation analysis of the sensor with the exhalation of gastric cancer patients; b) Correlation analysis of the sensor with the exhalation of lung cancer patients; c) Correlation analysis of the sensor with the exhalation of colorectal cancer patients; d) Correlation analysis of the sensor with the exhalation of healthy individuals.

[0072] Figure 16 This invention relates to an Arduino-based sensor array for human gas identification.

[0073] a represents the response patterns of the sensor array to a1) gastric cancer patients, a2) lung cancer patients, a3) colorectal cancer patients, and a4) healthy individuals; b represents a comparison of different algorithms; c represents histograms of different sensors; d represents box plots of different human exhalations; e represents PCA principal component analysis plots; and f represents confusion matrices of different human exhalations.

[0074] Figure 17 This is a modified DNA / Ti3C2T according to an embodiment of the present invention. x The sensing properties of the composite; among which,

[0075] a is Ti3C2T x and modified DNA / Ti3C2T x The response of the complex to acetone; b is Ti3C2T x and modified DNA / Ti3C2T x Sensitivity of the complex to acetone; c is Ti3C2T x DNA-A / Ti3C2T x Modified DNA / Ti3C2T x The response of the complex to four gases;

[0076] Figure 18 This is a modified DNA / Ti3C2T according to an embodiment of the present invention. x The sensing properties of the composite; among which,

[0077] a represents modified DNA / Ti3C2T x The complex's response to four gases; b is a modified DNA / Ti3C2T x The sensitivity of the complex to four gases;

[0078] Figure 19 This is a modified DNA / Ti3C2T according to an embodiment of the present invention. x The sensing performance of the sensor array; among which,

[0079] a represents modified DNA / Ti3C2T x The sensor array's response to ten gases; b is a modified DNA / Ti3C2T x t-SEN images of ten gases from a sensor array; c represents modified DNA / Ti3C2T x PCA images of ten gases from a sensor array;

[0080] Figure 20 This invention relates to the identification of fermentation gases from a bacterial strain using an Arduino-based sensor array according to an embodiment of the present invention; wherein,

[0081] a represents the response patterns of the sensor array to strains a1), a2), b3), c4), D4), and e5), where strains AE represent different mutant strains of *E. coli*; b represents a comparison of different algorithms; c represents a scatter plot of different sensors; d represents t-SEN images of five fermentation gases at 12 h; e represents the confusion matrix of the sensor array for the fermentation gases; and f represents the scatter plot of the sensors for the fermentation gases.

[0082] Figure 21 This invention relates to the identification of exhalations from different groups of people using an Arduino-based sensor array according to an embodiment of the present invention; wherein,

[0083] a represents the response patterns of the sensor array to a1) colorectal cancer, a2) gastric cancer, a3) pancreatic cancer, and a4) healthy individuals; b represents a comparison of different algorithms; c represents a scatter plot of different sensors; and d represents the confusion matrix of the sensor array for the exhalations of different populations. Detailed Implementation

[0084] The embodiments of the technical solution of the present invention are described in detail below. These embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore merely examples and should not be used to limit the scope of protection of the present invention.

[0085] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0086] For the sake of brevity, this article only discloses some specific numerical ranges. However, any lower limit can be combined with any upper limit to form an unspecified range; and any lower limit can be combined with other lower limits to form an unspecified range, just as any upper limit can be combined with any other upper limit to form an unspecified range. Furthermore, each individually disclosed point or single value can itself serve as a lower or upper limit and be combined with any other point or single value or with other lower or upper limits to form an unspecified range.

[0087] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the invention, are intended to cover non-exclusive inclusion.

[0088] In this document, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the present invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0089] Unless otherwise specified, all embodiments and optional embodiments of the present invention can be combined with each other to form new technical solutions.

[0090] The embodiments of the present invention are described in detail below. These embodiments are exemplary and are only used to explain the present invention, and should not be construed as limiting the invention. Where specific techniques or conditions are not specified in the embodiments, they are performed according to the techniques or conditions described in the literature in the art or according to the product instructions. Reagents or instruments used, unless otherwise specified, are all commercially available conventional products.

[0091] The materials used in this embodiment and their sources:

[0092] Ti3AlC2 (MAX) powder (98%, 200 mesh) was purchased from Maclean's Ltd., hydrochloric acid (HCl, AR) from Tongguang Co., Ltd., lithium fluoride (LiF, 99.99%, 1-3 mm) from Zhongke Yinuo Co., Ltd. (all ground into fine powder before use), tetrabutylammonium hydroxide (TBAOH, 40%) from Aladdin Reagent (Shanghai) Co., Ltd., and N,N-dimethylformamide (DMF, SafeDry) from Adamas. Various DNA oligonucleotides were purchased from Suzhou Genewiz Biotechnology Co., Ltd.

[0093] Detection System: The real-time detection platform independently built in this invention can be divided into three modules: a sensing generation module, a data conversion and transmission module, and a data visualization and processing module. The sensing module is a gas receiving reaction chamber. During the experiment, the sensor is placed in the detection chamber where the concentration of the target gas continuously changes to achieve dynamic sensing and detection of the gas. Based on the law of conservation of mass, the number of gas molecules is controlled by adjusting the volume of the gas solution added. When the sensing material interacts with the gas molecules, electron transfer occurs. This chemical change affects the resistance of the relevant channel circuits, and the process of converting the chemical signal into an electrical signal is completed by the data conversion and transmission platform. This platform transmits the real-time resistance change signal to the data visualization and processing port (computer), and uses machine learning to complete the entire process of dynamic gas detection and recording.

[0094] The gas response value is defined as: (R0-Ri) / R0*100%, where R0 is the resistance value of the sensor in air, and Ri is the resistance value of the sensor in the gas environment to be measured.

[0095] Example 1: D-DNA / Ti3C2T x and modified DNA / Ti3C2T x Synthesis

[0096] 1. Single-layer / few-layer Ti3C2T x (MXene) Synthesis

[0097] This study used monolayer / few-layer MXenes in the experiments, and the synthesis route is as follows: Figure 1As shown. First, multilayer MXene was synthesized. In a polytetrafluoroethylene (PTFE) reactor, 40 mL of 9M hydrochloric acid solution was added, followed by the addition of 2 g of LiF for dissolution. The mixture was stirred at 35°C and 600 rpm for 30 minutes. Then, 2 g of Ti3AlC2 (MAX phase) was added, and the reaction was maintained at 35°C and 600 rpm for 24 hours until etching was complete. The resulting solution was washed with deionized water until the pH of the supernatant was 6, at which point multilayer MXene was obtained. The multilayer MXene was then freeze-dried under vacuum for 36 hours to obtain multilayer MXene powder (also known as multilayer Ti3C2T). x (Powder), store at low temperature (4℃).

[0098] Highly dispersed mono / few-layer MXenes in DMF were prepared using the microenvironmental tuning (TMM) method. 0.5 g of the obtained multilayer Ti3C2T was taken... x The powder was added to 12 mL of 25% TBAOH solution as an intercalating agent and surface modifier, and the mixture was stirred at 25°C and 500 rpm for 6 hours to perform intercalation, thereby intercalating Ti3C2T. x Transformed into Ti3C2T x -TBA powder. Excess TBAOH solution was washed out with ethanol, and the resulting precipitate was Ti3C2T. x -TBA was collected in a 50 mL centrifuge tube. 30 mL of DMF was added to the centrifuge tube, and the mixture was gently stirred or shaken to completely disperse the precipitate in the organic reagent. The 50 mL centrifuge tube was then centrifuged at 25°C and 12000 rpm for 5 minutes to collect the supernatant. After separating the solution and precipitate, an appropriate amount of DMF was added again, and the mixture was repeatedly shaken and centrifuged until the precipitate was completely dissolved and collected in a 50 mL solution. This yielded 10 mg / mL low / monolayer Ti3C2T. x MXene stock solution, stored at low temperature (4°C).

[0099] 2. D-DNA / Ti3C2T x Synthesis of (D-DNA / MXene)

[0100] This study used adsorbent solution self-assembly technology to prepare D-DNA / Ti3C2T x Complexes, such as Figure 2 As shown. The initial DNA concentration used in the experiment was 10 μM for DNA-A, DNA-T, DNA-C, and DNA-G stock solutions. Taking DNA-A as an example, 10 μL of the stock solution was added to a 1.5 mL centrifuge tube containing 90 mL of DMF and diluted 10-fold to obtain a 1 μM DNA daughter solution. 10 mg / mL of low / monolayer Ti3C2T was used... x10 μL of MXene solution was added to a 1.5 mL centrifuge tube, and 990 μL of DMF solution was added for a 100% dilution to obtain 0.1 mg / mL low / monolayer Ti3C2T. x MXene solution. Then, 0.1 mg / mL of low / monolayer Ti3C2T was taken. x 40 μL of MXene solution was added to two separate 0.5 mL centrifuge tubes, followed by 40 μL, 1 μM, and 10 μM DNA solutions, respectively. The mixtures were thoroughly mixed by pipetting repeatedly to obtain oligo / monolayer Ti3C2T solutions. x Composite solutions with a final concentration of 0.05 mg / mL and final DNA concentrations of 0.5 μM and 5 μM were prepared and labeled as low-concentration and high-concentration groups, respectively. The absence of a reagent reaction upon pipetting indicated successful synthesis of the target composite material. The resulting product was designated DNA-A / Ti3C2T. x

[0101] (DNA-A / MXene). DNA-T / Ti3C2T was synthesized using the same method. x (DNA-T / MXene), DNA-C / Ti3C2T x (DNA-C / MXene) and DNA-G / Ti3C2T x

[0102] (DNA-G / MXene) composite material.

[0103] 3. Modified DNA / Ti3C2T x synthesis

[0104] This study used adsorption self-assembly technology to prepare modified DNA / Ti3C2T x Complexes, such as Figure 3 As shown. DNA was specifically functionalized through chemical modification groups (such as amino C6, phosphorylation, thiol C6, biotin, fluorescein, and esterification). The initial concentration of the modified DNA was 10 μM solution. 0.1 mg / mL of low / monolayer Ti3C2T was used. x Add 40 μL of the solution to a 0.5 mL centrifuge tube, then add 40 μL of 10 μM DNA solution. Use a pipette to repeatedly mix the solution until it is completely mixed. No obvious reagent reaction indicates that the combining was successful.

[0105] Example 2: D-DNA / Ti3C2T x and modified DNA / Ti3C2T x The representation

[0106] 1. Surface morphology

[0107] To study the preparation of monolayer / few-layer Ti3C2T xand D-DNA / Ti3C2T x The surface morphology of the material was observed using TEM and AFM. In the TEM images, the prepared material was identified as a few-layer Ti3C2T. x Nanosheets, which are relatively thin ( Figure 4 a and Figure 4 b). The prepared Ti3C2T x The nanosheet thickness is 0-4 nm, meaning the prepared nanosheet has a single or few layers. Figure 4 c). Due to the presence of water molecules and other surface adsorbates in the MXene nanosheets, the thickness is slightly increased. D-DNA / Ti3C2T x Morphological characterization of the composite material, taking DNA-A (SEQ ID NO: 5) as an example, shows that its surface remains relatively thin. Figure 4 d and Figure 4 e), the nanosheet thickness is less than 10 nm ( Figure 4 f), namely the synthesized D-DNA / Ti3C2T x The number of layers is also small. In summary, this indicates the successful synthesis of D-DNA / Ti3C2T. x Nanocomposite material, with DNA as the second relative Ti3C2T x The morphology of the nanosheets was not significantly affected.

[0108] To investigate modified DNA / Ti3C2T x The surface morphology of the composite material was characterized using TEM, AFM, and SEM. In the SEM images ( Figure 5 a) It can be observed that the material has a relatively uniform layered structure. In the TEM image ( Figure 5 b) The prepared material was observed to be relatively thin, indicating that the two-dimensional structure of the material consists of several layers of Ti3C2T. x Nanosheets. The AFM results show modified DNA / Ti3C2T. x The thickness of the nanosheets is 0-6 nm. Figure 5 d), namely, the prepared Ti3C2T x The nanosheets have approximately 4-5 layers. The thickness varies slightly due to the presence of other solvent molecules within the composite nanosheets. Modified DNA / Ti3C2T x The surface morphology results provide important information for understanding the structure and properties of composite materials.

[0109] 2. Crystal Structure

[0110] To investigate the nanostructure on the material surface, XRD and HRTEM analyses were performed. It is known that Ti3AlC2 crystals exhibit a diffraction peak (002) at 9.517° in the XRD image, while D-DNA / Ti3C2T...x In the material, the Ti3AlC2 peak disappears ( Figure 6 a) The (002) peak undergoes a negative shift, i.e., there is a distinct sharp peak at approximately 4.830°. This indicates that after selective etching, the Al layer is partially stripped away, and the material is transformed into Ti3C2T. x This peak is Ti3C2T x The crystal plane of Ti3C2T. This characteristic peak indicates that Ti3C2T x The Ti3C2T was successfully prepared and possesses excellent crystal properties. Further research was conducted using HRTEM. x and D-DNA / Ti3C2T x The crystal structure of Ti3C2T (taking DNA-A (SEQ ID NO: 5) as an example). HRTEM images show Ti3C2T x There is a lattice spacing of 0.267 nm. Figure 6 c) Select the appropriate region for electron diffraction ( Figure 6 d) shows crystal bright spots consistent with the lattice spacing, reflecting Ti3C2T x Typical lattice properties and crystal structure. And D-DNA / Ti3C2T x The lattice spacing is 0.268 nm. Figure 6 b) This indicates that D-DNA acts as the second relative to Ti3C2T. x The lattice of the nanosheets was not significantly affected.

[0111] The modified DNA / Ti3C2T was observed using XRD curve analysis and HRTEM images. x Surface nanostructures (taking DNA-A (SEQ ID NO: 5) as an example). XRD results show that the sample nanosheets have a distinct peak at around 6.73°. Figure 7 a). And Ti3C2T x The disappearance of the (002) peak in the diffraction pattern at 9.517° indicates that the modified DNA / Ti3C2T x The (002) peak of the nanosheets exhibits a negative shift. This indicates that the Al layer in the material has been exfoliated to some extent, and the material has transformed into Ti3C2T. x This peak is Ti3C2T x The crystal plane (002). This characteristic peak indicates that the modified DNA / Ti3C2T x The composite nanosheets were successfully synthesized and possessed excellent crystal properties. HRTEM was used to further investigate the crystal structure of the composite nanosheets. HRTEM images showed that the composite nanosheets had a lattice spacing of 0.27 nm (…). Figure 7 b) Select the appropriate region for electron diffraction ( Figure 7c) shows crystal bright spots consistent with the lattice spacing, indicating that the intervention of DNA does not affect the typical lattice properties and crystal structure of MXene.

[0112] 3. Elemental Analysis

[0113] To study the nanocomposite material D-DNA / Ti3C2T x The chemical composition and bonding state of DNA-A (SEQ ID NO: 5) were analyzed using XPS. Spectroscopy (...) Figure 8 a) Displays D-DNA / Ti3C2T x The nanocomposite material contains Ti, C, N, O, and P elements. The high-resolution spectrum shows four characteristic peaks for C1s at 281.15, 284.95, 286.15, and 288.35 eV, corresponding to C-Ti-T elements, respectively. x CC, CO, and COO. The high-resolution spectrum of O1s shows two characteristic peaks at 529.95 eV and 531.95 eV, corresponding to O-Ti and O=C, respectively. The high-resolution spectrum of Ti 2p shows Ti 2p... 1 / 2 and Ti 2p 3 / 2 The two components, where Ti 2p 3 / 2 455.25 eV and 458.45 eV correspond to Ti-N and TiO2 (Ti) respectively. 4+ ), while Ti 2p 1 / 2 The two components, 460.95 eV and 464.15 eV, correspond to Ti-N and Ti-O, respectively. High-resolution spectra of N1s show two components corresponding to NC and NH at 399.80 and 401.95 eV, respectively, confirming the presence of base groups. High-resolution spectra of P 2p show components corresponding to P 2p at 136.05 eV and 134.10 eV, respectively. 1 / 2 and P 2p 3 / 2 The two components indicate the presence of phosphorus (P) found in DNA on the material surface. A single peak centered at 132.90 eV is present in the P 2p spectrum, which can be attributed to the PO phosphate groups of the DNA. XPS analysis reveals key information about the chemical composition and bonding state of the composite material.

[0114] The surface elements of the composite material were observed using SEM and its built-in EDS, confirming the composition of the sample. Elemental distribution maps were then used. Figure 8 b) It can be observed that Ti, C, O, N and P elements are uniformly dispersed in D-DNA / Ti3C2T x The surface of the nanosheets indicates the successful preparation of D-DNA / Ti3C2T. x Nanosheets. EDS results show D-DNA / Ti3C2Tx The successful synthesis of biocomposite materials provides strong evidence.

[0115] XPS analysis of modified DNA / Ti3C2T x Chemical composition and bonding state of the composite nanosheets. Spectroscopy reveals DNA / Ti3C2T x The nanocomposite material contains Ti, C, N, and P elements ( Figure 9 a) The spectrum shows four characteristic peaks at C 1282.15, 284.8, 286, and 289.1 eV, corresponding to C-Ti-T, respectively. x The high-resolution spectra of O1s show two characteristic peaks at 530.05 eV and 532.05 eV, corresponding to O-Ti and O=C, respectively. The high-resolution spectra of Ti 2p show three characteristic peaks at 455.7 eV, 458.75 eV, and 461.85 eV, corresponding to Ti 2p... 1 / 2 Ti=O and Ti 2p 3 / 2 Among them, Ti 2p 3 / 2 The two components (455.5 eV and 456.4 eV) correspond to Ti-Ti and Ti-N, respectively. The Ti=O observed in the spectrum indicates Ti3C2T x Slight oxidation occurs, but it does not affect its structure and physicochemical properties. The high-resolution N1s spectrum shows two components, corresponding to NC and NH at 400.3 and 401.9 eV respectively, indicating the presence of bases. Characteristic peaks for P 2p are present at 134.3 eV and 130.9 eV, indicating the presence of phosphorus (P) found in DNA.

[0116] Observation of the sample surface elements using SEM and its built-in EDS confirmed the presence of modified DNA / Ti3C2T. x Sample composition. EDS spectrum ( Figure 9 (b) It can be observed that Ti, C, O, N and P elements are uniformly dispersed on the surface of the composite nanosheets, indicating that the modified DNA / Ti3C2T nanosheets have been successfully prepared. x Nanosheets. XPS and EDS results indicate modified DNA / Ti3C2T. x The successful synthesis of biocomposite materials provides strong evidence and reveals key information about the chemical composition and bonding state of the materials.

[0117] Example 3: D-DNA / Ti3C2T x Gas sensing performance analysis

[0118] D-DNA / Ti3C2T x Fabrication of the gas sensor: The D-DNA / Ti3C2T gas sensor prepared in Example 1 was used...x The composite material was drop-coated onto the interdigitated electrode sheet, ensuring complete coverage of the electrode surface. The interdigitated electrode containing the composite material was then carefully transferred to a 55°C oven and dried for 30 minutes to form a gas-sensitive membrane, yielding D-DNA / Ti3C2T. x Gas sensor.

[0119] The inventors used an Arduino circuit to achieve the sensor's dynamic response performance to gases. The gas response performance is evaluated by calculating the sensor's resistance change in the target gas environment. Specifically, this is expressed as: (R0-Ri) / R0*100%, where R0 is the sensor's resistance in air, and Ri is the sensor's resistance in the target gas environment. Within the gas detection chamber, the dynamic concentration change of the gas is adjusted by calculating and controlling the volume of the target gas solution.

[0120] The method for determining the concentration of odor molecules is as follows:

[0121]

[0122] Where ρ (g / mL) is the gas molecule density; V (μL) is the gas molecule solution volume; ω (%) is the gas molecule solution mass fraction; M (g / mol) is the gas molecule molar mass; V m (L / mol) is the molar volume of an ideal gas; V c (L) is the volume of the gas detection chamber.

[0123] The specific operating procedure for detecting gas molecules in a solution is as follows: Several electrodes are sequentially fixed above a 1.5L test box. The corresponding volumes of different concentrations of the gas solution to be tested are calculated according to Formula 1 and injected into the box. The box is left to stand for two minutes to allow the odor molecules to fully evaporate. During this time, a glass plate is used to completely cover the detection hole to achieve a sealing effect. During detection, the sensor is placed inside the test box containing the target gas molecules and held for 2 minutes to ensure full contact between the gas molecules and the material surface. The computer terminal reads the resistance change during this period. The resistance value obtained by the sensor during the gas evaporation period is used as a baseline, and the sensor's response value to each gas molecule is calculated.

[0124] 1. Sensing performance of single-base sensors

[0125] To investigate the effect of D-DNA on Ti3C2T x To investigate whether the sensing characteristics are affected and whether this effect is related to the length, concentration, and base types of DNA molecules, this experiment uses pure base DNA (DNA-A, DNA-T, DNA-C, DNA-G) as the research object.

[0126] First, a single-base DNA-A (SEQ ID NO: 5) was selected for proof-of-concept testing, compared with pure Ti3C2T. x and DNA-A / Ti3C2T x Sensing characteristics of the sensors for gases. Based on existing reports of gas detection concentrations, concentration gradients of 50 ppb, 100 ppb, 250 ppb, 500 ppb, 1 ppm, 5 ppm, and 20 ppm were set within the range of 50 ppb-20 ppm, and the sensing characteristics of these two types of sensors for gases were tested respectively. Analysis of pure Ti3C2T... x DNA-A / Ti3C2T with different concentrations of DNA x The resistance values ​​of the sensors responding to ammonia, ethanol, diethyl ether, and acetone showed that when both types of sensors were placed in the environment of the gas being measured, the resistance values ​​increased rapidly, reaching a certain level before balancing out. Figure 10 a and Figure 10 d). This may be because when the target gas is exposed to the sensor surface, the gas molecules are affected by Ti3C2T. x Adsorption leads to Ti3C2T x The conductive channels within the sensor are blocked, thus increasing the sensor's resistance. It was also found that within the range of 50 ppb-20 ppm, resistance was significantly reduced based on pure Ti3C2T. x When the sensor continuously detects different concentrations of a single gas, baseline drift occurs and is difficult to recover. However, DNA-A / Ti3C2T x The sensor's baseline recovery capability is significantly enhanced, which may be due to the interaction between the surface functional groups of base A and Ti3C2T. x The interactions of surface functional groups facilitate the adsorption and desorption of gas molecules. At a gas concentration of 20 ppm, low concentrations of DNA-A / Ti3C2T... x The sensor's response values ​​to ammonia, ethanol, diethyl ether, and acetone were 14.68%, 10.78%, 7.51%, and 9.50%, respectively, corresponding to Ti3C2T x 2.03, 2.91, 1.26 and 2.21 times ( Figure 10 b), while high concentrations of DNA-A / Ti3C2T x The sensor's response values ​​to ammonia, ethanol, diethyl ether, and acetone were 14.27%, 10.51%, 8.10%, and 13.40%, respectively, corresponding to Ti3C2T x 1.11, 1.10, 1.13 and 1.23 times ( Figure 10 e). The above results indicate that DNA, as the second phase of the composite material, does indeed contribute to the composite material's performance in Ti3C2T. x The sensing characteristics are affected, and this effect is related to the concentration of DNA.

[0127] To investigate the relationship between target gas concentration and sensing response, the response values ​​of both sensors were linearly fitted to the natural logarithm of the gas concentration. Low-concentration DNA DNA-A / Ti3C2T x The sensors are all smaller than pure Ti3C2T x The slope of the sensor's response to ammonia and acetone is greater than that to ethanol, while the slope of its response to ether is roughly the same. Figure 10 c). However, high concentrations of DNA in DNA-A / Ti3C2T x The sensor's slope for all four gases is greater than that for pure Ti3C2T. x Sensor. Among them, compared to Ti3C2T x Sensor, DNA-A / Ti3C2T with high concentration of DNA x The sensor showed the greatest increase in the slope of acetone gas detection. Figure 10 f). This indicates that, compared to Ti3C2T x Sensor, DNA-A / Ti3C2T with added low concentration of DNA x The sensor is only sensitive to the detection of certain gases, while DNA-A / Ti3C2T with added high concentrations of DNA is more sensitive. x The sensor is generally sensitive to the detection of various gases, and the sensitivity to a particular gas may increase significantly with increasing DNA concentration. This is likely due to the interaction between high concentrations of DNA-A surface functional groups and Ti3C2T. x Surface functional group interactions provide more active sites. It was also found that DNA-A / Ti3C2T... x The sensor exhibits good linearity for the four gases in the range of 50 ppb to 20 ppm, indicating that the sensor can be further applied to the detection of higher gas concentration ranges.

[0128] 2. Sensing performance of multi-base sensors

[0129] To investigate the gas sensing properties of composite materials containing DNA with different bases, three other pure DNA bases (DNA-T (SEQ ID NO: 6), DNA-C (SEQ ID NO: 7), and DNA-G (SEQ ID NO: 8)) were also synthesized with Ti3C2T. x The composite was used to detect ammonia, ethanol, diethyl ether, and acetone. Detection of four standard gases revealed that DNA / Ti3C2T... x The sensor's baseline recovery capability has been improved. The response values ​​of the aforementioned sensors to four different gases with concentrations ranging from 50 ppb to 20 ppm were calculated according to Formula 1. The results showed that when the gas concentration was greater than 1 ppm, the response of each sensor to the four gases was significantly improved, particularly for DNA / Ti3C2T. xThe response to gases is higher than that of pure Ti3C2T. x ( Figure 11 a and Figure 11 b). Furthermore, the responses of different sensors also differ. This indicates that DNA / Ti3C2T x Not only improved Ti3C2T x It has good baseline recovery capabilities and a lower detection limit for gases.

[0130] The inventors further investigated the effect of DNA base length on Ti3C2T x Due to the influence of sensing characteristics, the inventors prepared 16 different lengths of pure base DNA / Ti3C2T x A bio-composite sensor was developed, in which the DNA sequence is shown in Table 1. Four standard gases—acetone, ammonia, ethanol, and diethyl ether—were detected in the range of 1 ppm to 100 ppm. The results showed that the DNA / Ti3C2T... x The bio-composite sensor generally achieved good gas response and baseline stability. To comprehensively analyze four different lengths and two different concentrations of pure DNA / Ti3C2T... x The overall response characteristics of the bio-composite sensor are presented by taking the average value of each response value and presenting the results in the form of a heatmap. Figure 11 c shows pure Ti3C2T x and low concentration of 16 pure base DNA / Ti3C2T x The sensor's response to four gases, Figure 11 d shows pure Ti3C2T x and high concentration of 16 pure base DNA / Ti3C2T x The biosensor responded to four gases. Results showed that high concentrations of DNA / Ti3C2T... x The bio-composite sensor exhibits superior overall response to multiple gases compared to low-concentration DNA / Ti3C2T. x Biocomposite sensor. By comparing DNA / Ti3C2T of different lengths... x A bio-composite sensor was discovered at two DNA concentrations in DNA / Ti3C2T. x Among sensors, those with a DNA length of 10 nt exhibit relatively high sensitivity to gases. Figure 11 e and Figure 11 f). Simultaneously, low concentrations of DNA / Ti3C2T x DNA-G / Ti3C2T x The sensor is highly sensitive to four gases, while high concentrations of DNA / Ti3C2T... x The sensitivity of the bio-composite sensor to four gases is generally ( Figure 11 g and Figure 11h). This may be due to the high concentration of DNA with Ti3C2T. x The interaction of functional groups on the surface leads to the exposure of more active sites in the nanocomposite material. Studies have shown that for certain bases, DNA / Ti3C2T x The sensitivity of the composite sensor can be improved by increasing the DNA concentration. In summary, the study indicates that high concentrations of DNA are effective as a Ti3C2T... x The second phase of the sensor has broad application prospects and potential in the practical detection of low-concentration target gases. Furthermore, when fabricating sensor arrays that mimic the animal olfactory system, DNA / Ti3C2T with a length of 10 nt should be considered first. x Bio-composite sensor.

[0131] Table 1

[0132] SEQ ID NO: DNA Base Sequence 1 AAAAA 2 TTTTT 3 CCCCC 4 GGGGG 5 AAAAAAAAAA 6 TTTTTTTTTT 7 CCCCCCCCCC 8 GGGGGGGGGG 9 AAAAAAAAAAAAAAA 10 TTTTTTTTTTTTT 11 CCCCCCCCCCCCCCC 12 GGGGGGGGGGGGGGG 13 AAAAAAAAAAAAAAAAAAAA 14 TTTTTTTTTTTTTTTTTT 15 CCCCCCCCCCCCCCCCCCCC 16 GGGGGGGGGGGGGGGGGGGG

[0133] 3. Sensing performance of single-base sensor array

[0134] The inventors simulated the olfactory or gustatory systems of animals and used neural network algorithms to construct sensor arrays to improve sensor selectivity. The above research results indicate that, although DNA / Ti3C2T x Bio-composite sensors offer low detection limits and high sensitivity for gas detection; however, these individual sensors have limitations and do not demonstrate specificity for detecting a particular gas. Gas sensor arrays, utilizing neural network algorithms, overcome the limitations of single gas sensors in gas detection, significantly enhancing the feasibility of multi-gas differentiation and identification.

[0135] The above-mentioned DNA / Ti3C2T with excellent sensing performance x Used for building a five-channel sensor array. Compared to other biomolecules such as proteins, DNA molecules have higher stability and, due to the presence of active sites that specifically bind to gases, can achieve gas adsorption. High-concentration DNA / Ti3C2T... x With a low detection limit and high sensitivity, high concentrations of 10 nt DNA / Ti3C2T should be given special consideration. x The bio-composite sensor serves as the main component of the sensor array. The array used here consists of five sensors, namely sensor 1: Ti3C2T. x Sensor 2: DNA-A / Ti3C2T x Sensor 3: DNA-T / Ti3C2T x Sensor 4: DNA-C / Ti3C2T x Sensor 5: DNA-G / Ti3C2T x .

[0136] Sensors 1-5 were attached to interdigitated electrodes to construct a sensor array. The interdigitated electrode substrate was alumina ceramic with external dimensions of 10mm × 10mm. The electrode line spacing was 50μm, the line width was 100μm, and the finger length of the 15 interdigitated pairs was 7.7mm. The electrodes were composed of Ti / Cu / Ni / Aud surface metal layers with thicknesses of 0.1μm, 5μm, 4μm, and 1μm, respectively. During sensor array construction, 20μl of the above-mentioned synthetic material solution was first pipetted and evenly added to the electrodes. The electrodes were then dried in a 55℃ oven for 30 minutes to form a gas-sensitive film. Finally, the electrodes carrying the gas-sensitive film were fixed onto the polyethylene terephthalate (PET) substrate of the test kit as the sensor array framework. At this point, the sensor array was completed, and the thin film attached to the interdigitated electrodes was DNA / Ti3C2T. x Composite materials.

[0137] The aforementioned 5-channel sensor array was used with an Arduino platform to detect four typical gases: acetone, ammonia, ethanol, and diethyl ether. First, the array was placed inside a detection chamber. When the gas concentration inside the chamber reached 20 ppm, the gas response signal of the sensor was detected. The overall response of the sensor array to acetone and ethanol was significantly better than that to ammonia and diethyl ether, indicating that the sensor array has good selectivity for acetone and ethanol. Figure 12 a). This could be due to the presence of the biological composite material DNA / Ti3C2T in the array. x The resulting surface structure and functional groups are more conducive to the adsorption of acetone and ethanol gas molecules.

[0138] The sensing performance of different sensors in the array varied for four typical gases at a concentration of 20 ppm. Sensors 2-5 in the array all showed better gas responses than sensor 1. However, the sensing performance differed significantly when detecting acetone, ethanol, and diethyl ether, while the sensing performance remained almost unchanged when detecting ammonia. Figure 12 b). This indicates that the sensor plays a major role in detecting acetone, ethanol, and diethyl ether, meaning that the addition of DNA significantly improves the sensing performance of the sensor array. Furthermore, in the sensor array, sensors 2 and 3 show significantly better responses to single gases than the other sensors. Figure 12 b, Figure 12 c). Analysis of the sensitivity of the five sensors in the array shows that sensors 2 and 3 generally have high sensitivity to the four gases. Figure 12 d). In summary, DNA / Ti3C2T xThe addition of sensors enhances the selectivity of the array and improves its sensing performance. Furthermore, DNA-A and DNA-T bases play crucial roles in the array, dominating the enhancement of the sensor array's selectivity.

[0139] With the assistance of algorithms, based on DNA / Ti3C2T x The array of bio-composite sensors can distinguish different gases. Four typical gases showed good aggregation, indicating that the array can distinguish these four gases at a concentration of 20 ppm. Figure 12 e). This also demonstrates the successful preparation of the array and proves that it is composed of different DNA / Ti3C2T x The array constructed from composite biosensors demonstrates remarkable performance in gas identification, enabling the specific detection of different odor molecules.

[0140] 4. Sensing performance of dual-base sensors

[0141] Based on the above experimental results, the composite material DNA-A / Ti3C2T x and DNA-T / Ti3C2T x This contributes significantly to the high sensitivity and selectivity of the array, exhibiting superior sensing performance compared to biocomposite materials containing other bases. Here, we further explore the interaction between DNA and Ti3C2T based on the DNA-A and DNA-T dibase DNA-AT. x Impact on sensing performance. Considering the differences in the number of bases and their positional arrangement in dibasic DNA sequences, the inventors generated 12 sets of dibasic DNA-AT sequences using SPSS (as shown in Table 2), and then compared them with Ti3C2T... x The composite material DNA-AT / Ti3C2T was synthesized and prepared. x Investigating the dual-base DNA-AT / Ti3C2T x The sensing characteristics of the sensor.

[0142] Table 2

[0143] Number of DNAs DNA Base Sequence (5' to 3') SEQ ID NO: DNA-AT1 TTTAATATTA 17 DNA-AT2 ATATTAATTT 18 DNA-AT3 TAAATTAATT 19 DNA-AT4 TATATATTAT 20 DNA-AT5 AATTTTTATA 21 DNA-AT6 TAATAATTTA 22 DNA-AT7 AAAAAAAAAA 23 DNA-AT8 ATAATTTTAA 24 DNA-AT9 ATTAAATATT 25 DNA-AT10 TTATATTAAT 26 DNA-AT11 AATTATATAT 27 DNA-AT12 TTTTTAAAAA 28

[0144] 12 sets of DAN-AT / Ti3C2T x Due to their unique molecular structure, the detection of gases varies considerably. To more intuitively observe the molecular conformation of DNA-AT sequences, the two-dimensional and three-dimensional structures of 12 DNA-AT sequences with the lowest energy were calculated and plotted using ChemDraw and Chem3D. The 12 DNA-AT structures are shown below. Figure 13 As shown in a. Figure 13 b showcased 12 sets of DAN-AT / Ti3C2T xThe sensor's overall sensing modes for four gases were analyzed. By comparing and analyzing the DNA-AT sequences and their corresponding sensor response modes, it was found that when the DNA-AT sequence is in a relatively stable conformation, although some base sequences have a certain degree of structural similarity, they exhibit significant differences in gas detection. For example, sequences DNA-AT3 and DNA-AT4, and DNA-AT9 and DNA-AT10 are very similar in base composition, arrangement, and two-dimensional structure; however, the DNA-AT / Ti3C2T sensor synthesized from these sequences shows different results. x The composite biosensor exhibits the opposite effect in gas detection. Figure 13 b3-b4, Figure 13 (b9-b10). This may be attributed to the dibase DAN-AT / Ti3C2T. x The unique molecular structure affects the adsorption and desorption of gases by the composite biosensor.

[0145] To facilitate comparison with pure Ti3C2T x Sensor with 12 DNA-AT / Ti3C2T x The sensing characteristics of a composite biosensor when detecting a certain gas were analyzed. Bar charts of the response values ​​for four different gases were plotted, and the average values ​​of each gas were analyzed. Figure 13 c). The results showed that 12 groups of DNA-AT / Ti3C2T x The composite sensor performs better than pure Ti3C2T in gas detection. x Sensor, and DNA-AT1 / Ti3C2T x DNA-AT3 / Ti3C2T x DNA-AT7 / Ti3C2T x The composite biosensor exhibits high stability. Furthermore, the sensor DNA-AT3 / Ti3C2T... x In other words, the overall detection performance of the dibase DNA-AT3 sequence is superior to that of other DNA-AT sequences for the four typical gases. Figure 13 c5). This indicates that the composite biosensor DNA-AT3 / Ti3C2T x As a core component, it can be used to construct multidimensional sensor arrays for the detection of real gases.

[0146] Example 4: D-DNA / Ti3C2T x Applications of sensor arrays

[0147] 1. Application in fruit odor molecule recognition

[0148] In the experiment, DNA-AT3 / Ti3C2T was used. xA five-channel sensor array was introduced to construct a six-channel sensor array. The sensors in the array are designated as Sensor 1: Ti3C2T. x Sensor 2: DNA-A / Ti3C2T x Sensor 3: DNA-T / Ti3C2T x Sensor 4: DNA-C / Ti3C2T x Sensor 5: DNA-G / Ti3C2T x Sensor 6: DNA-AT3 / Ti3C2T x Among them, sensors 2, 3, 4, and 5 are all high-concentration, 10-nt pure DNA / Ti3C2T. x Bio-composite sensor.

[0149] Random sampling was used to collect samples from fruits to ensure an equal probability of selecting each part. The collected samples were pulped, and the fruit pulp was sealed in plastic bags. A sensor was placed inside the bag, and fruit odor molecules were steadily exhaled onto the sensor for 2 seconds at a fixed distance of 5 cm. The test data consisted of 40 tests on 5 different fruits, generating 200 sets of data. During the experiment, when fruit odor molecules came into contact with the sensor, DNA / Ti3C2T... x Electron transfer occurs on the surface, leading to an increase in resistance. The resulting chemical signal is converted into an electrical signal, which is received by the computer. With the assistance of the computer, the gas detection platform processes the electrical signals of different fruits and successfully detects the corresponding fruit odors.

[0150] Statistical analysis of the acquired data revealed differences in the responses of each sensor. The raw data for each fruit's response were preprocessed and the results displayed as a heatmap. The sensor array exhibited different response patterns to odor molecules from different fruits. This is likely due to variations in the types and amounts of odor molecules present in different fruits. Furthermore, the array was found to possess a degree of selectivity, capable of distinguishing between different fruit odor molecules to some extent. Figure 14 d). Statistical analysis of the obtained data reveals the correlation between sensors from the scatter matrix plot. Figure 14 e). The histogram shows the distribution of the responses of each sensor in the array, and the box plot shows the responses of each sensor, revealing the differences in signals from different sensors. Figure 14 ab).

[0151] To identify different varieties of fruit, six computer algorithms were introduced to process the data. These six algorithms are LR (Logistic Regression), LDA, etc.

[0152] (Linear Discriminant Analysis), KNN (K-Nearest Neighbors), CART (Classification and Regression Tree), NB (Naive Bayes), and SVM (Support Vector Machine). First, the data is preprocessed, and the obtained raw data is labeled into 20*5 groups, with 90% of the data set as the training group and the remaining 10% as the test group. Then, the processed data is used for algorithm training. Taking the KNN algorithm as an example, the processed training group data is recorded, and the spatial distribution of each data type is displayed. After training the above model, the test group data is input, and the distance of each data input is calculated according to the algorithm. The distance of each point is calculated, and classification and recognition are achieved according to the nearest distance principle. From the confusion matrix, it can be seen that the detection accuracy of the gas detection platform based on the sensor array for the five fruit odor molecules is 62.5%, 71.4%, 100%, 85.7%, and 100%, respectively. Figure 14 f). Through 10 cross-validation tests, the KNN algorithm was found to have the highest accuracy, reaching 0.747, and could effectively identify fruit odor samples, while the LR algorithm had an accuracy of only 0.176. Figure 14 c) This proves that there is no obvious linear relationship between the samples.

[0153] The six-channel sensor array exhibits good selectivity and high recognition accuracy in detecting fruit odor molecules. This indicates that the gas detection platform built using the sensor array is suitable for detecting fruit odor molecules.

[0154] 2. Application in cancer population identification

[0155] The aforementioned six-channel sensor array was used to detect and identify odor molecules in the exhaled breath of individuals with gastric, lung, or colorectal cancer, as well as healthy individuals. Before the experiment, exhaled breath samples were collected from cancer patients using a 1L aluminum gas collection bag. Patients were required to rinse their mouths and avoid eating for two hours prior to gas collection. The medical-grade aluminum foil composite gas collection bag possesses core characteristics such as ultra-high sealing, light protection, chemical inertness, and anti-adsorption. During the experiment, the piston was opened and the gas collection bag was squeezed, allowing the gas to flow evenly and steadily to the sensor at a distance of two centimeters. This was maintained for 20 seconds to allow the gas to fully contact the sensing material, and the change in resistance was recorded in real-time by the computer. During this period, a 20-second interval was allowed between each test to allow the sensor resistance to return to baseline. The test data scale included 20 tests on 43 individuals, meaning the real-time detection platform tested 860 sets of data. When human odor molecules come into contact with the sensor, DNA / Ti3C2T... x Electron transfer occurs on the surface, converting chemical signals into electrical signals, which are then received by a computer. With the help of the computer, the gas detection platform processes the electrical signals from different human bodies and successfully detects the corresponding human odors.

[0156] First, statistical analysis is performed on the obtained data, starting with the scatter matrix plot ( Figure 15The correlation between each pair of sensors in the sensor array for breath detection in different populations can be observed. Specifically, for the breaths of healthy individuals and lung cancer patients, the correlation coefficients between sensors 2 and 3, and between sensors 4 and 6, are 0.54 and 0.44, respectively, indicating a moderate positive correlation. For the breaths of colorectal cancer patients and gastric cancer patients, the coefficients between sensors 1 and 2, and between sensors 4 and 5, are 0.75 and 0.91, respectively, indicating a strong correlation. Figure 15 (cd). This indicates that the individual sensors in the sensor array have a significant impact on the exhalation detection of different populations. That is, when the sensor array is used for human exhalation detection, the correlation between the individual sensors is strong, indicating that there are separable features in the multidimensional data.

[0157] By introducing the aforementioned six computer algorithms (LR, LDA, KNN, CART, NB, and SVM) to process the data, the identification of different population groups was achieved. Histogram ( Figure 16 c) and box plot ( Figure 16 d) The displayed response differences indicate that the sensor generates different electrical signals in response to the exhaled breath of different individuals, demonstrating that differences in odor molecules between individuals can be detected. PCA processing of the response results showed significant clustering in the exhaled breath tests of healthy individuals and individuals with three types of cancer. Figure 16 (e) Among them, the three types of cancer patients showed very similar clustering, indicating that the exhalation data of cancer patients were very similar and different from those of the healthy group, reflecting the specificity of cancer and healthy groups. This shows that the sensor array can distinguish and identify the exhalation of cancer patients and healthy people. The results of the responses of different groups are presented in the form of heatmaps. The heatmaps show that the exhalation responses of each sensor in the array to healthy people are significantly different from the exhalation responses of cancer patients. Figure 16 a) This may be due to the interaction and competition for active sites between complex components in the patient's exhalation, which is one of the characteristics that distinguishes healthy people from cancer patients. When introducing machine learning algorithms for population classification and identification, the data is first labeled into 200*4 groups, with 90% of the data set as the training group and the remaining 10% as the test group. The model is trained using algorithms such as KNN, and classification is achieved by calculating the spatial distance between the test data and the training data. Finally, the above six machine learning algorithms are introduced for identification. The confusion matrix shows that the gas detection platform has an overall accuracy rate of over 60% for all four human odor molecules. Among them, the accuracy rate for odor molecules from healthy individuals is the highest, reaching 81.4% (…). Figure 16 f). After 10 cross-validations, the algorithm accuracy was compared, and it was found that the KNN algorithm performed best with an accuracy of 0.807, while LR only achieved 0.451, proving that the samples lacked a linear relationship. Figure 16 b). In summary, histogram ( Figure 16 c) and box plot ( Figure 16d) The difference in response indicates that differences in odor molecules between individuals can be detected. PCA image ( Figure 16 e) and confusion matrix diagram ( Figure 16 f) This demonstrates that the 6-channel sensor array can distinguish between the exhaled breath of cancer patients and the exhaled breath of healthy individuals. Figure 16 f indicates that the 6-channel sensor array can achieve a maximum accuracy of 81.4% in recognizing human exhalation.

[0158] Example 5: Modified DNA / Ti3C2T x Gas sensing performance analysis

[0159] The inventors used six types of modifying groups: C6 amino (AminolinkerC6), C6 thiol (Thiol-C6 SS), phosphorylation (Phosphate), biotin (Biotin), cholesterol ester (Cholesteryl), and fluorescein (FAM, 6-Carboxyfluorescein). They modified DNA-A and DNA-AT3 at different sites at the 5′ and 3′ sites, respectively. The various modification methods and sites are shown in Table 3.

[0160] Table 3 DNA sequence modification groups and sites

[0161]

[0162]

[0163] First, a comparison was made between the Ti3C2T and the Ti3C2T. x and DNA-A-Thiol-C6 / Ti3C2T x The sensor's response to gases was analyzed. Typical gas concentrations of 1 ppm, 3 ppm, 5 ppm, 10 ppm, and 20 ppm were selected. Acetone was used as an example to analyze the modified DNA / Ti3C2T... x Response values, sensitivity, and selectivity to gases. Although DNA-A-Thiol-C6 / Ti3C2T x The initial response was lower than that of pure Ti3C2T. x sensor( Figure 17 a) However, with increasing gas concentration, DNA-A-Thiol-C6 / Ti3C2T x The increase in resistance of the sensor in the target gas environment is much greater than that of pure Ti3C2T. x At 10 ppm, DNA-A-Thiol-C6 / Ti3C2T x The sensor's response to acetone is 5.905%, which is pure Ti3C2T. x 1.36 times the sensor response ( Figure 17a). By using DNA-A-Thiol-C6 / Ti3C2T x A linear fit was performed between the natural logarithm of the sensor response value and the natural logarithm of the gas concentration. Figure 17 b) It was observed that its slope towards acetone increased significantly, indicating that it was pure Ti3C2T. x This is 1.72 times that of pure Ti3C2T. This indicates that after C6 thiol modification with DNA-A, the composite sensor becomes more sensitive to acetone gas. (Compared to pure Ti3C2T...) x DNA-A / Ti3C2T x and DNA-A-Thiol-C6 / Ti3C2T x The average response to typical gases validated the gas selectivity of DNA. The results showed ( Figure 17 c) Based on Ti3C2T x The sensor's average response to the four gases was 4.38%, with a variance of 0.36; based on DNA-A / Ti3C2T x The sensor's average response to the four gases was 4.72%, with a variance of 0.53%; while the DNA-A-Thiol-C6 / Ti3C2T sensor... x The sensor's average response to the four gases was 5.85%, with a variance of 1.54%. This indicates that, relative to pure Ti3C2T... x DNA-A / Ti3C2T x and DNA-A-Thiol-C6 / Ti3C2T x The response values ​​to the four gases differed considerably. DNA-A / Ti3C2T x The response to ethanol and Ti3C2T x The effect was the same, while the effect on other gases was improved to varying degrees, namely Ti3C2T. x 1.42 and 1.085 times; while DNA-A-Thiol-C6 / Ti3C2T x The responses to other gases are Ti3C2T x The ratio was 1.134-1.89 times higher. This indicates that the modified DNA / Ti3C2T... x The sensor's selectivity in responding to gases has been improved.

[0164] To investigate the relationship between the modification sites and sequences, the modified DNA / Ti3C2T shown in Table 3 was synthesized. x Sensor. Using unmodified DNA-A / Ti3C2T x and DNA-AT / Ti3C2T x Using the sensor as a reference, the responses of four gases were detected at 1 ppm to 20 ppm to obtain DNA / Ti3C2T modified at different sites. x Average response value ( Figure 18 a). Just DNA-A / Ti3C2T x In general, the 5′ site modification showed a better response to acetone, ammonia, and ethanol than the 3′ site modification. However, both the 5′ and 3′ site modifications showed lower response values ​​to diethyl ether than DNA-A / Ti3C2T. x This indicates that modification of the 5′ site of DNA-A is beneficial for the adsorption and desorption of ketones, ammonia, and alcohols, while modification at the 5′ and 3′ sites has little effect on the adsorption and desorption of ethers. Regarding DNA-AT3 / Ti3C2T... x In contrast, the 5′ and 3′ modifications exhibited varying degrees of enhanced response to the four gases due to their different modification groups. This indicates that the choice of DNA-AT modification site does not significantly affect the adsorption of the gases.

[0165] Next, the modified DNA / Ti3C2T was investigated. x The sensitivity of the composite sensor to gas response. A linear fit was performed between the natural logarithm of the sensor's response to gas values ​​from 1 ppm to 20 ppm and the natural logarithm of the gas concentration; the slope represents the sensitivity. Figure 18 (b) The results showed that DNA-A modified at the 5′ site was more sensitive to acetone and ammonia than DNA modified at the 3′ site. DNA-AT showed varying degrees of increased sensitivity to gases depending on the modifying group at different sites. For example, AT5-12 (DNA-AT3) with a 5′ cholesterol ester modification achieved sensitivities of 0.542 and 0.308 for ammonia and diethyl ether, respectively, while its sensitivities to ethanol and acetone were only 0.1042 and 0.027. This may be because the modified DNA has different active sites for different gases, thereby enhancing the electron exchange frequency between gas molecules and the composite material surface.

[0166] The average sensitivity of the modified sensor to four gases was analyzed. Although the sensitivity of individual modified DNAs to gases remained in balance with DNA-A and DNA-AT or even showed a negative increase, most modified DNAs still had the property of improving sensitivity.

[0167] 2. Sensor array sensing performance

[0168] Modified DNA / Ti3C2T enhances the molecular adsorption properties of DNA molecules by binding specific modifying groups to them. x Composite materials offer specific advantages in the fabrication of sensor arrays. As demonstrated by the experiments above, modification with specific functional groups can improve sensitivity and gas selectivity. Based on these experiments, a modified DNA / Ti3C2T composite material was selected to enhance sensing properties. x A sensor array was fabricated. This array consists of six sensors, including sensor 1: DNA-A5-3 / Ti3C2T.x Sensor 2: DNA-A5-6 / Ti3C2T x Sensor 3: DNA-AT5-7 / Ti3C2T x Sensor 4: DNA-AT5-10 / Ti3C2T x Sensor 5: DNA-A3-15 / Ti3C2T x Sensor 6: DNA-A3-18 / Ti3C2T x .

[0169] To test the sensing performance of the modified sensor array, ten odor molecules (Table 4) were used as experimental subjects. These ten odor molecules can be classified into five categories: acids, alcohols, aldehydes, ketones, and esters. These gases are widely present in industry and are representative. In the experiment, the gas concentration was set to 5 ppm, and the modified sensor array was tested five times repeatedly. The detection results are presented in the form of a heatmap. Figure 19 a) It can be seen that each sensor responds differently to each gas, demonstrating the array's gas selectivity. The results are then dimensionality-reduced using PCA, and the five gas types are projected onto the x, y, and z axes respectively. Figure 19 c) shows the aggregation effect of each type of gas molecule in different directions, exhibiting good clustering characteristics, indicating that the array has a strong classification ability for these 5 types of gas molecules. Furthermore, the t-SEN algorithm was used to distinguish these 10 gases ( Figure 19 b). After data processing, t-SEN plots were generated. The results show that the modified sensor array has the ability to recognize these 10 gas molecules. In summary, after verification with these 10 gas molecules, the modified DNA / Ti3C2T array constructed through functional group modification... x The sensor array has a good ability to identify and distinguish gases, and can be applied to practical classification.

[0170] Table 4

[0171] serial number English name Chinese name CSA purity Manufacturers A Formic Acid Formic acid 64-18-6 AR McLean B Heptanoic Acid n-heptanoic acid 111-14-8 AR McLean C Farnesol Farnesol 4602-84-0 AR McLean D Cumic alcohol 4-Isopropylbenzyl alcohol 536-60-7 AR McLean E D-Carvone Dextrorotatory carvone 2244-16-8 AR McLean F Isojasmone B11 dihydrojasmine 95-41-0 AR McLean G Ethyl Acetate Ethyl acetate 141-78-6 AR McLean H γ-Caprolactone γ-caprolactone 695-06-7 AR McLean I Citral citral 5392-40-5 AR McLean J Benzaldehyde benzaldehyde 100-52-7 AR McLean

[0172] Example 6: Modified DNA / Ti3C2T x Applications of sensor arrays

[0173] 1. Application in the recognition of fermentation odor molecules

[0174] Odor molecules produced during fermentation (such as aldehydes and alcohols in VOCs) are key indicators reflecting microbial metabolic activities and fermentation status.

[0175] With the help of machine learning algorithms, the modified DNA / Ti3C2T from Example 5 was used.x The sensor array detection platform successfully detected and identified the fermentation gases from the bacterial strains. The strains used in the experiment were single colonies of different sizes and morphologies from the same streak plate containing isobutyraldehyde production pathways, and were labeled A, B, C, D, and E. First, fermentation gas samples were collected at different time points using a 1L aluminum gas collection bag. Then, the gas collection bag was squeezed to allow the gas to flow evenly and steadily towards the sensor array at a distance of 2 cm for 20 seconds, ensuring complete contact between the gas and the sensing material. Changes in resistance were recorded in real-time by a computer. During this process, each test was paused for 20 seconds to allow the array resistance to return to baseline. The test data consisted of 30 tests for five strains at 0h, 12h, 24h, and 36h, meaning the array tested 600 sets of data. When the fermentation gases came into contact with the sensor surface, DNA / Ti3C2T... x Electron transfer occurs on the surface, converting chemical signals into electrical signals, which are then transmitted to the terminal. With the help of algorithms, fermentation gases from different bacterial strains are processed and differentiated.

[0176] The results of the gas detection platform's response to the strain's gas production are presented in the form of a heatmap. The heatmap shows ( Figure 20 a) The detection platform showed significant responses to the gas production of the five strains at different time periods. Specifically, the gas at 0h was primarily air, resulting in the lowest response signal from the detection platform. As fermentation progressed, the gas production of the five strains varied at different time periods, and the platform's response to each strain's gas also differed, demonstrating the platform's selectivity. Statistical analysis of the data revealed differences in response shown by histograms and box plots, indicating that differences in odor molecules between individuals could be detected. Furthermore, t-SEN analysis of the data revealed overlap in the time intervals of the five strains at different time periods. Figure 20 c) indicates that points that are close together in the high-dimensional data space remain close together when projected into the low-dimensional space, resulting in overlap during clustering and classification. This may be due to the similarity in gas production and species among strains within the same time period. t-SEN processing was performed on the responses of different strains at four different time periods, revealing that the classification performance of the five strains was better at 12 hours. Figure 20 d) indicates that when the detection platform is used for fermentation gas identification, fermentation gas of 12 hours should be selected first.

[0177] Gas samples are classified and identified by incorporating machine learning into the data processing. (Scatter matrix plot) Figure 20f) shows the correlation between the sensors in the array for detecting gas production from different strains. For example, for gases from strains b and c, the correlation coefficients between sensors 3 and 4, and between sensors 1 and 2, are 0.718 and 0.741, respectively, indicating a strong positive correlation. This shows that the sensors in the array have a significant impact on the detection of gas production from different strains, and there are significant differences in the responses of different strains. When introducing machine learning algorithms (LR, LDA, CART, KNN, NB, and SVM) for gas classification and recognition, the data is first labeled into 130*5 groups, with 90% of the data set as the training group and the remaining 10% as the test group. The confusion matrix shows (… Figure 20 e) When detecting gases from five bacterial strains, the detection platform achieved an accuracy rate of 77% for strains C and D, 75% and 64% for strains B and E respectively, and the lowest accuracy rate for strain A was only 54%. After 10 cross-validation tests, the results showed that ( Figure 20 b) CART achieved the best accuracy of 0.719, while LR had an accuracy of 0.231, demonstrating its unsuitability for inter-sample identification. In summary, the detection platform demonstrated good identification capability for 12-hour fermentation gases with a prediction accuracy of 77%.

[0178] 2. Application in cancer population identification

[0179] The modified DNA / Ti3C2T from Example 5 was used. x A sensor array was used to detect and identify human exhaled breath. Gas samples were collected from individuals with colorectal cancer (A), stomach cancer (B), pancreatic cancer (C), and healthy individuals (D). Before the experiment, exhaled breaths from different groups were collected in 1L gas collection bags and then exhaled onto the sensor array at a fixed distance of 2cm, maintaining contact with the sensing material for 20 seconds. Ten measurements were taken from each individual's breath, generating 400 sets of data. With the help of machine learning, the detection platform processed the responses from different groups' exhaled breaths and successfully identified the corresponding human odors.

[0180] By introducing machine learning algorithms (LR, LDA, CART, KNN, NB, and SVM) to process the data, the detection platform was able to identify different population groups. Modified DNA / Ti3C2T x The sensor array's response to different population groups is presented in the form of a heatmap. The heatmap shows that the array's expiratory response varies among different population groups, and also differs among individuals within the same category. Figure 21 a). This may be due to differences in the composition of exhaled breath between individuals, which is one of the characteristics that distinguishes healthy people from cancer patients. From the scatter plot ( Figure 21c) It can be seen that there is a correlation between each pair of sensors in the sensor array for breath detection in different populations. For example, for the breaths of patients A and B, the coefficients between sensors 1 and 2, and between sensors 2 and 3, reach 0.775 and 0.629, respectively, indicating a strong correlation between them. This shows that each sensor in the sensor array has a significant impact on breath detection in different populations. When introducing machine learning algorithms for population classification and identification, the data is first labeled into 100*4 groups, with 90% of the data set as the training group and the remaining 10% as the test group. The confusion matrix shows ( Figure 21 d) The detection platform achieved accuracy rates of 90%, 70.8%, 78.3%, and 92.3% for the four human odor molecules, respectively. After 10 cross-validations, we compared the algorithm accuracy and found that both KNN and CART algorithms had an accuracy of 0.853, while LR only had an accuracy of 0.365. Figure 21 b) indicates that it is not suitable for identification of this sample.

[0181] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0182] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A composite material, characterized in that, include: MXene and D-DNA.

2. The composite material according to claim 1, characterized in that, The length of the D-DNA is 5nt-20nt; Optionally, the sequence of the D-DNA consists of one or two bases; Optionally, the D-DNA has any one of the sequences shown in SEQ ID NO: 1 to 28; Optionally, the MXene is Ti3C2T x ; Optionally, the molar ratio of MXene to D-DNA is (25-1000):

1.

3. The composite material according to claim 2, characterized in that, The D-DNA has a modifying group at its 5′ or 3′ end; Optionally, the modifying group includes at least one of amino, thiol, phosphate, biotin, cholesterol ester, and fluorescein.

4. A method for preparing the composite material according to any one of claims 1-3, characterized in that, include: The composite material was obtained by mixing MXene and D-DNA. Optionally, the concentration of MXene is 0.01 mg / mL to 0.1 mg / mL based on the total mass of the mixed treatment products; preferably, it is 0.05 mg / mL. Optionally, the molar concentration of the D-DNA is 0.1 μM to 10 μM based on the total mass of the mixed treatment products; preferably, it is 5 μM.

5. Use of the composite material according to any one of claims 1-3 or the composite material prepared by the method of claim 4 in the preparation of a gas sensor.

6. A gas sensor, characterized in that, Includes the composite material according to any one of claims 1-3 or the composite material prepared by the method according to claim 4; Optionally, the composite material is coated on the surface of the interdigitated electrode sheet.

7. The application of the gas sensor according to claim 6 in gas detection.

8. An array sensor, characterized in that, Includes several gas sensors as described in claim 6.

9. The array sensor according to claim 8, characterized in that, The composite materials used in the multiple gas sensors may be the same or different.

10. An application of the array sensor according to any one of claims 8-9, wherein the application includes at least one of the following: Detecting gases; Identify fruit odor molecules; Identifying cancer patients; Identify fermentation odor molecules.

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