Bionic gas sensing device and bionic sensor array preparation method

Abstract

The present disclosure provides a biomimetic gas sensing device and a method for preparing a biomimetic sensor array. The device includes: a biomimetic sensor array for detecting multiple gas samples to obtain multidimensional sensing analog signals; a signal conversion module for converting the multidimensional sensing analog signals to obtain multidimensional sensing electrical signals; and a signal processing module for processing the multidimensional sensing electrical signals using a pre-trained classification model to identify the components of the multiple gas samples. The biomimetic sensor array includes multiple sensors, each of which includes a sensing material library based on MXene and perovskite; the sensing material library has a Schottky barrier structure, and different sensors detect the same gas sample to obtain different signals. The sensing portion of the device disclosed herein has a Schottky barrier structure obtained by combining MXene and perovskite, has excellent gas sensing accuracy, and can be used for human body recognition.

Description

Technical Field

[0001] The present disclosure relates to the technical field of gas sensing and detection, and in particular to a bionic gas sensing device and a method for preparing a bionic sensor array. Background Art

[0002] Human odor, such as that emitted from human breath and pores, consists of the end products of metabolism. Because each metabolic product is unique, the electrical signals converted from them are unique, making them an ideal source of non-invasive biomarkers for human identification. The detection and identification of human odor plays a key role in many fields, including public safety, medical diagnosis, and environmental protection.

[0003] Currently, much research on human odor focuses on the detection of single gases. Research on rapidly detecting the overall odor signature for identifying individuals is limited. Most traditional gas detection technologies are cumbersome and rely on large, expensive instrumentation that requires specialized sample pretreatment steps and specialized maintenance. Despite their significant achievements, these technologies lack portability and rarely meet the requirements for point-of-care detection. Summary of the Invention

[0004] (1) Technical issues to be resolved

[0005] In response to the existing technical problems, the present disclosure provides a bionic gas sensing device and a method for preparing a bionic sensor array, which are used to at least partially solve the above technical problems.

[0006] (2) Technical solution

[0007] The present disclosure provides a biomimetic gas sensing device, comprising: a biomimetic sensor array for detecting multiple gas samples to obtain multidimensional sensing analog signals; a signal conversion module for converting the multidimensional sensing analog signals to obtain multidimensional sensing electrical signals; and a signal processing module for processing the multidimensional sensing electrical signals using a pre-trained classification model to identify the components of the multiple gas samples. The biomimetic sensor array comprises multiple sensors, each of which comprises a sensing material library based on MXene and perovskite; the sensing material library is a Schottky barrier structure, and different sensors obtain different signals when detecting the same gas sample.

[0008] Optionally, the sensing material library is MXene nanosheets modified with a second phase; wherein the second phase includes an organic metal halide perovskite AMX3.

[0009] Optionally, MXene includes: Mo2C, Ti3C2, Nb2C, Nb4C3, Ta4C3, Ti2C, V2C, V4C3 and Ti3C2T x; A in the organometallic halide perovskite AMX3 includes: MA, FA and Cs, M includes: Pb, and X includes: Cl, Br, I and combinations thereof.

[0010] Optionally, the sensing material library includes Ti3C2T x / MAPbBr3 nanocomposite material library; wherein T includes -O, -OH and -F.

[0011] Optionally, the bionic sensor array includes at least 4 sensors; wherein, among the different sensors, Ti3C2T x Different mass ratios with MAPbBr3; and Ti3C2T x The mass ratio of MAPbBr3 is in the range of 3:1 to 10:1.

[0012] Optionally, the biomimetic sensor array further includes auxiliary sensors; the auxiliary sensors are unmodified MXene nanosheets.

[0013] Optionally, the signal conversion module is an Arduino platform.

[0014] Optionally, the classification model includes: a K-nearest neighbor algorithm model, a naive Bayes model, a support vector machine, and a classification and regression tree model.

[0015] Optionally, the signal processing module further includes: a preprocessing model for performing dimensionality reduction preprocessing on the multidimensional sensor electrical signal; wherein the preprocessing model includes a principal component analysis model and a t-distributed random neighborhood embedding model.

[0016] Optionally, the multiple gas samples include a test sample and a verification sample, and the concentration gradient of each gas in the multiple gas samples varies; wherein the gas in the test sample includes: at least one of ammonia, acetone, ethanol, ether and nitrogen dioxide; the gas in the verification sample includes: at least one of carvacrol, butyl cinnamate, indole, terpinolene, nonanal, acetaldehyde, citral, farnesol, 2,3-dihydro-2,2,6-trimethylbenzaldehyde and 4-isopropylbenzyl alcohol.

[0017] Optionally, the sensing material library is used to perform multiple adsorption and desorption of multiple gas samples.

[0018] Optionally, the sensor further comprises: a substrate and electrodes stacked in sequence; wherein the sensing material library is provided on the electrodes; and the substrate comprises any one of PET, silicon, glass, ceramic and ITO, and a combination thereof.

[0019] Optionally, the bionic gas sensing device is used for human body recognition.

[0020] Another aspect of the present disclosure provides a method for preparing a biomimetic sensor array, comprising: preparing MXene nanosheets using a tuned microenvironment method; synthesizing perovskite nanoparticles using a ligand-assisted reprecipitation strategy; preparing multiple sensing material libraries based on MXene and perovskite through an in situ growth process, wherein the mass ratio of MXene to perovskite is different in different sensing material libraries; and spraying the different sensing material libraries onto an electrode array to obtain a biomimetic sensor array.

[0021] (3) Beneficial effects

[0022] Compared with the prior art, the biomimetic gas sensing device and biomimetic sensor array preparation method provided by the present disclosure have at least the following beneficial effects:

[0023] (1) The biomimetic gas sensing device disclosed herein detects analog sensing signals through a biomimetic sensor array and then processes the electrical signals converted from the sensing analog signals using a pre-trained classification model, thereby identifying the composition of gases at room temperature. The sensing portion of the sensor array comprises a Schottky barrier structure formed by a composite of MXene and perovskite, which enhances the material resistance change caused by changes in carrier concentration and improves gas sensing performance.

[0024] (2) The biomimetic gas sensing device disclosed herein uses a biomimetic sensor array and a machine learning model to detect and identify gases, which is convenient and fast. It also uses an Arduino platform for signal conversion. The device is small in size and easy to carry.

[0025] (3) The biomimetic sensor array preparation method disclosed in the present invention adopts a tuned microenvironment method, a ligand-assisted reprecipitation strategy, and an in situ growth method to prepare a sensing material library, which overcomes the problem that MXene materials are easily oxidized and have poor dispersion in non-polar phases, and can prepare gas sensors with high sensitivity and strong stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0027] Figure 1 The composition diagram of the biomimetic gas sensing device according to an embodiment of the present disclosure is schematically shown;

[0028] Figure 2 Schematically shows a structural diagram of a sensor according to an embodiment of the present disclosure;

[0029] Figure 3A Schematic diagram of Ti3C2T according to an embodiment of the present disclosure x Scanning electron microscopy images of few-layer nanosheets; Figure 3BSchematic diagram of Ti3C2T according to an embodiment of the present disclosure x Elemental mapping of few-layer nanosheets; Figure 3C Schematically shows a scanning electron microscope image of MAPbBr3 nanocubes according to an embodiment of the present disclosure; Figure 3D Schematically illustrates an elemental mapping diagram of MAPbBr3 nanocubes according to an embodiment of the present disclosure; Figure 3E Schematically shows a transmission electron microscope image of MAPbBr3 nanocubes according to an embodiment of the present disclosure; Figure 3F Schematically shows the particle size distribution of MAPbBr3 nanocubes according to an embodiment of the present disclosure;

[0030] Figure 4A Schematically shows a scanning electron microscope image of a sensing material library according to an embodiment of the present disclosure; Figure 4B Schematically shows a transmission electron microscope image of a sensing material library according to an embodiment of the present disclosure; Figure 4C Schematically shows an atomic force microscope image of a sensing material library according to an embodiment of the present disclosure; Figure 4D Schematic diagram showing the particle size distribution of MAPbBr3 nanocubes in the sensing material library according to an embodiment of the present disclosure; Figure 4E Schematically shows a high-resolution transmission electron microscope image of a sensing material library according to an embodiment of the present disclosure; Figure 4F Schematically shows a selected area electron diffraction pattern of a sensing material library according to an embodiment of the present disclosure; Figure 4G Schematically illustrates an elemental mapping diagram of a sensing material library according to an embodiment of the present disclosure;

[0031] Figure 5A Schematically shows an X-ray diffraction pattern of a sensing material library according to an embodiment of the present disclosure; Figure 5B Schematically shows a full scan spectrum of an X-ray photoelectron spectrum of a sensing material library according to an embodiment of the present disclosure; Figure 5C Schematically shows an X-ray photoelectron spectroscopy carbon element scanning spectrum of the sensing material library according to an embodiment of the present disclosure; Figure 5D Schematically shows an X-ray photoelectron spectroscopy titanium element scanning spectrum of the sensing material library according to an embodiment of the present disclosure; Figure 5E Schematically shows an X-ray photoelectron spectroscopy bromine element scanning spectrum of the sensing material library according to an embodiment of the present disclosure; Figure 5F Schematically shows an X-ray photoelectron spectroscopy lead element scanning spectrum of the sensing material library according to an embodiment of the present disclosure;

[0032] Figure 6A Schematic diagram of the original Ti3C2T according to an embodiment of the present disclosure x Gas sensing response diagram to ammonia; Figure 6BSchematic diagram of the original Ti3C2T according to an embodiment of the present disclosure x Gas sensing response graph to acetone; Figure 6C Schematic diagram of the original Ti3C2T according to an embodiment of the present disclosure x Gas sensing response graph to ethanol; Figure 6D Schematic diagram of the original Ti3C2T according to an embodiment of the present disclosure x Gas sensing response graph to ether;

[0033] Figure 7A Schematically shows a gas sensing response diagram of ammonia gas by a sensing material library according to an embodiment of the present disclosure; Figure 7B Schematically shows a gas sensing response diagram of the sensing material library to acetone according to an embodiment of the present disclosure; Figure 7C Schematically shows a gas sensing response diagram of the sensing material library to ethanol according to an embodiment of the present disclosure; Figure 7D Schematically shows a gas sensing response diagram of the sensing material library to diethyl ether according to an embodiment of the present disclosure; Figure 7E Schematic diagram of the sensing material library according to the embodiment of the present disclosure and the original Ti3C2T x Comparison of gas sensing responses to ammonia; Figure 7F Schematic diagram of the sensing material library according to the embodiment of the present disclosure and the original Ti3C2T x Comparison of gas sensing responses to acetone; Figure 7G Schematic diagram of the sensing material library according to the embodiment of the present disclosure and the original Ti3C2T x Comparison of gas sensing responses to ethanol; Figure 7H Schematic diagram of the sensing material library according to the embodiment of the present disclosure and the original Ti3C2T x Comparison of gas sensing responses to ether;

[0034] Figure 8 Schematically shows the response and recovery time of the sensing material library according to an embodiment of the present disclosure to ammonia gas with a concentration of 1 ppm;

[0035] Figure 9 Schematically showing a comparison of responses of the sensing material library to 5 ppm ammonia at different volume fractions according to an embodiment of the present disclosure;

[0036] Figure 10A A diagram schematically illustrates a linear relationship between the logarithm of the response of a sensing material library and the logarithm of the ammonia concentration according to an embodiment of the present disclosure; Figure 10B A diagram schematically illustrates a linear relationship between the logarithm of the response of the sensing material library and the logarithm of the acetone concentration according to an embodiment of the present disclosure; Figure 10C Schematically showing a linear relationship between the logarithm of the response of the sensing material library and the logarithm of the ethanol concentration according to an embodiment of the present disclosure; Figure 10D Schematically showing a linear relationship between the logarithm of the response of the sensing material library and the logarithm of the diethyl ether concentration according to an embodiment of the present disclosure;

[0037] Figure 11A Schematically shows a long-term response graph of a sensing material library to ammonia at a concentration of 5 ppm according to an embodiment of the present disclosure; Figure 11B Schematically shows a cyclic performance diagram of the long-term response of the sensing material library to ammonia at a concentration of 5 ppm according to an embodiment of the present disclosure;

[0038] Figure 12 Schematically shows a pictorial diagram of a sensor array according to an embodiment of the present disclosure;

[0039] Figure 13 Schematically illustrates an experimental operation diagram of a biomimetic sensor array according to an embodiment of the present disclosure;

[0040] Figure 14A Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to four typical gases with a concentration of 5 ppm; Figure 14B Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to ammonia gas with a concentration of 50 ppb to 5 ppm; Figure 14C Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to acetone with a concentration of 50 ppb to 5 ppm; Figure 14D Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to ethanol with a concentration of 50 ppb to 5 ppm; Figure 14E Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to ether with a concentration of 50 ppb to 5 ppm; Figure 14F Schematically shows a 2D PCA diagram of the biomimetic sensor array according to an embodiment of the present disclosure for four typical gases at 5 ppm;

[0041] Figure 15A Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to carvacrol at a concentration of 50 ppb to 5 ppm; Figure 15B Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to butyl cinnamate at a concentration of 50 ppb to 5 ppm; Figure 15C Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to indole at a concentration of 50 ppb to 5 ppm; Figure 15DSchematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to terpinolene at a concentration of 50 ppb to 5 ppm; Figure 15E Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to nonanal at a concentration of 50 ppb to 5 ppm; Figure 15F Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to acetaldehyde with a concentration of 50 ppb to 5 ppm; Figure 15G Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to citral at a concentration of 50 ppb to 5 ppm; Figure 15H Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to farnesol at a concentration of 50 ppb to 5 ppm; Figure 15I Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to 2,3-dihydro-2,2,6-trimethylbenzaldehyde at a concentration of 50 ppb to 5 ppm; Figure 15J Schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to 4-isopropylbenzyl alcohol at a concentration of 50 ppb to 5 ppm;

[0042] Figure 16 Schematically shows the average response results of each sensor in the biomimetic sensor array according to an embodiment of the present disclosure to ten odor molecules at a concentration of 5 ppm;

[0043] Figure 17 Schematically shows a 3D PCA graph of 10 odor molecules at 5 ppm for a biomimetic sensor array according to an embodiment of the present disclosure;

[0044] Figure 18 Schematically shows a 2D t-SNE diagram of the biomimetic sensor array according to an embodiment of the present disclosure for 10 odor molecules at 5 ppm;

[0045] Figure 19 Schematically shows an average response diagram of a biomimetic gas sensing device to breath odor according to an embodiment of the present disclosure;

[0046] Figure 20 Schematically shows a PCA result diagram of exhaled breath odor by a biomimetic gas sensing device according to an embodiment of the present disclosure;

[0047] Figure 21A Schematically shows a statistical histogram of breath odor data generated by a biomimetic gas sensing device according to an embodiment of the present disclosure; Figure 21BSchematically shows a statistical box plot of breath odor data by a biomimetic gas sensing device according to an embodiment of the present disclosure; Figure 21C A schematic diagram shows a scatter matrix diagram of data statistics of breath odor by a biomimetic gas sensing device according to an embodiment of the present disclosure;

[0048] Figure 22A The confusion matrix of the biomimetic gas sensing device for breath odor according to an embodiment of the present disclosure is schematically shown; Figure 22B A diagram schematically shows a comparison of the accuracy results of the biomimetic gas sensing device for exhaled breath odor according to an embodiment of the present disclosure;

[0049] Figure 23 Schematically shows a PCA result diagram of clothing odor by a biomimetic gas sensing device according to an embodiment of the present disclosure;

[0050] Figure 24A The confusion matrix of the biomimetic gas sensing device for clothing odor according to an embodiment of the present disclosure is schematically shown; Figure 24B A schematic diagram shows a comparison of the accuracy results of the bionic gas sensing device for clothing odor according to an embodiment of the present disclosure.

[0051] [Description of Reference Numerals]

[0052] 1-bionic sensor array; 11-sensor; 111-sensing material library; 112-electrode; 113-substrate; 2-signal conversion module; 3-signal processing module;

[0053] P / M-1 - sensor 1; P / M-2 - sensor 2; P / M-3 - sensor 3; P / M-4 - sensor 4; M-1 - sensor 5; M-2 - sensor 6. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0055] It should be noted that in the drawings or descriptions of the specification, similar or identical parts use the same figure numbers. The technical features in the various embodiments exemplified in the specification can be freely combined to form new solutions without conflict. In addition, each claim can be used as an embodiment alone or the technical features in each claim can be combined as a new embodiment. In the drawings, the shape or thickness of the embodiment can be expanded and simplified or conveniently indicated. Furthermore, the elements or implementations not shown or described in the drawings are forms known to ordinary technicians in the relevant technical field. In addition, although this article may provide demonstrations of parameters containing specific values, it should be understood that the parameters do not need to be exactly equal to the corresponding values, but can be approximated to the corresponding values within an acceptable error tolerance or design constraint.

[0056] Unless there are technical obstacles or contradictions, the above-mentioned various embodiments of the present disclosure can be freely combined to form additional embodiments, and these additional embodiments are all within the protection scope of the present disclosure.

[0057] Although the present disclosure is described in conjunction with the accompanying drawings, the embodiments disclosed in the drawings are intended to illustrate preferred embodiments of the present disclosure and are not to be construed as limiting the present disclosure. The dimensional ratios in the drawings are merely illustrative and are not to be construed as limiting the present disclosure.

[0058] Although some embodiments of the present general inventive concept have been shown and described, it will be appreciated by those skilled in the art that changes may be made to these embodiments without departing from the principles and spirit of the present general inventive concept, the scope of which is defined in the claims and their equivalents.

[0059] Figure 1 The figure schematically shows the composition of a biomimetic gas sensing device according to an embodiment of the present disclosure.

[0060] According to the embodiments of the present disclosure, Figure 1 As shown, the present disclosure provides a biomimetic gas sensing device, for example, including: a biomimetic sensor array 1, for detecting multiple gas samples to obtain a multidimensional sensing analog signal. A signal conversion module 2, for converting the multidimensional sensing analog signal to obtain a multidimensional sensing electrical signal. A signal processing module 3, for processing the multidimensional sensing electrical signal using a pre-trained classification model to identify the components of multiple gas samples. Among them, the biomimetic sensor array 1 includes multiple sensors 11, and the sensor 11 includes a sensing material library 111 based on MXene and perovskite. The sensing material library 111 is a Schottky barrier structure, and different sensors 11 detect the same gas sample to obtain different signals. By detecting at least one of the same gas samples in the gas samples by different sensors in the sensor array, different electrical signals can be obtained, and then these different electrical signals are processed by a machine learning model to realize the identification of the same gas sample, and then the identification of multiple gases in the gas sample can be realized.

[0061] For example, the biomimetic gas sensing device disclosed herein is an instant detection platform (IDP), consisting of a biomimetic sensor array (BSA) and a signal processing platform. It can mimic the human sense of smell, utilizing multidimensional sensor signals and machine learning algorithms to identify unique features in different body odor signals. The BSA, a core component of the IDP, is responsible for converting the characteristics of human odor into analog signals.

[0062] For example, one of the key components of BSA is the sensing material. MXene, a new two-dimensional material, holds great potential in gas sensing due to its unique microstructure, electrochemical properties, excellent surface adsorption capacity, and ability to operate at room temperature. In this field, the sensing performance of MXene materials depends on the chemical and electrical properties of their surfaces. Doping with different materials can modify the surface properties of MXene, enabling highly sensitive and selective detection of different gases. Perovskite materials have different crystal structures and chemical compositions, and highly selective detection of different gases can be achieved by controlling the type and content of the doped perovskite. Compared to materials such as metal oxides and metals, perovskites have higher surface activity and better conductivity, which can enhance the sensitivity of MXene materials. Furthermore, perovskites offer improved thermal and photostability, are less susceptible to decomposition and deactivation, and can operate at room temperature. Therefore, doped perovskites offer higher selectivity, sensitivity, and stability, as well as lower cost, compared to materials such as doped metal oxides and precious metals.

[0063] According to an embodiment of the present disclosure, the sensing material library is, for example, a MXene nanosheet modified with a second phase. The second phase includes an organometallic halide perovskite AMX3. The second phase modification refers to another atomic arrangement present in the microstructure of the solid-state material, which is closely related to the lattice structure of the material. During the solidification process of the material, the atoms self-assemble into an ordered crystal structure, in which the first phase refers to the dominant atomic arrangement in the crystal, and the second phase refers to other atomic arrangements present in the crystal. The second phase usually has different physical and chemical properties from the first phase, so the properties of the material can be adjusted by adjusting the amount and arrangement of the second phase to meet application requirements.

[0064] According to an embodiment of the present disclosure, MXene may be, for example, Mo2C, Ti3C2, Nb2C, Nb4C3, Ta4C3, Ti2C, V2C, V4C3, and Ti3C2T. x Any one of the following. The organometallic halide perovskite AMX3 has the advantages of high carrier mobility, long diffusion length, and direct band gap. A is an organic cation, for example, any one of MA, FA, and Cs; M is a divalent or trivalent metal ion, for example, Pb; and X is a halide anion, for example, Cl, Br, I, and combinations thereof.

[0065] For example, the sensing material library is Ti3C2T x / MAPbBr3 nanocomposite material library. Among them, T can be any one of -O, -OH and -F. That is, by adjusting the synthesis method of MXene, the terminal group T can be adjusted. xThe composition of MXene can be obtained by adding MAPbBr3 nanoparticles to Ti3C2O2, Ti3C2F2, Ti3C2(OH)2 and other different compositions. x It has a higher specific surface area, thus providing more active sites for gas, promoting the adsorption and desorption process of target gas molecules on the surface of the sensor material. On the other hand, due to the different work functions, the combination of perovskite and MXene can form a Schottky barrier (SB) structure. When gas is adsorbed onto MAPbBr3 and transfers electrons, it will cause a change in the Fermi level, and the electrons from the gas molecules will be further transferred to Ti3C2T x To balance the Fermi level. The electron migration caused by gas molecule adsorption will further lead to the x The hole concentration of Ti3C2T x The conductive path is narrowed. x Compared with the sensor based on Ti3C2T x The sensor with MAPbBr3 has higher resistance and thus higher response to gas.

[0066] Figure 2 The structure of a sensor according to an embodiment of the present disclosure is schematically shown.

[0067] According to the embodiments of the present disclosure, Figure 2 As shown, the sensor 11 includes, for example, a substrate 113, an electrode 112, and a sensing material reservoir 111 stacked in sequence. The substrate 113 includes, for example, polyethylene terephthalate (PET), silicon, glass, ceramic, and ITO, or any combination thereof. The electrode 112 is, for example, a gold electrode.

[0068] In order to study the surface morphology of the materials, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and atomic force microscopy (AFM) tests were performed on samples related to the sensing material library.

[0069] Figure 3A Schematic diagram of Ti3C2T according to an embodiment of the present disclosure x Scanning electron microscopy image of few-layer nanosheets. Figure 3B Schematic diagram of Ti3C2T according to an embodiment of the present disclosure x Elemental mapping of few-layer nanosheets. Figure 3C Schematically shows a scanning electron microscope image of MAPbBr3 nanocubes according to an embodiment of the present disclosure. Figure 3D Schematic diagram of the elemental mapping of MAPbBr3 nanocubes according to an embodiment of the present disclosure. Figure 3ESchematically shows a transmission electron microscope image of MAPbBr3 nanocubes according to an embodiment of the present disclosure. Figure 3F Schematic diagram showing the particle size distribution of MAPbBr3 nanocubes according to an embodiment of the present disclosure.

[0070] According to the embodiments of the present disclosure, Figure 3A As shown in the TEM image, the few-layer Ti3C2T x The nanosheets exhibit extremely low contrast, indicating their two-dimensional structure and ultrathin thickness, illustrating the x The successful preparation of nanosheets. Figure 3B As shown in Figure 2, the uniform distribution of Ti, O and C elements can be seen. Figure 3C As shown in Figure 2, the surface morphology indicates that MAPbBr3 nanocubes were successfully prepared. Figure 3D As shown, the uniform distribution of Br and Pb is shown. Figure 3E and 3F As shown, the average particle size of the homogeneous MAPbBr3 nanocubes is 7.3 nm.

[0071] Figure 4A Schematically shows a scanning electron microscope image of a sensing material library according to an embodiment of the present disclosure. Figure 4B Schematically shows a transmission electron microscope image of a sensing material library according to an embodiment of the present disclosure. Figure 4C Schematically shows an atomic force microscope image of a sensing material library according to an embodiment of the present disclosure. Figure 4D Schematic diagram showing the particle size distribution of MAPbBr3 nanocubes in the sensing material library according to an embodiment of the present disclosure. Figure 4E Schematically shows a high-resolution transmission electron microscopy image of a sensing material library according to an embodiment of the present disclosure. Figure 4F The figure schematically shows a selected area electron diffraction pattern of a sensing material library according to an embodiment of the present disclosure. Figure 4G Schematically illustrates an elemental mapping diagram of a sensing material library according to an embodiment of the present disclosure.

[0072] According to the embodiments of the present disclosure, Figure 4A As shown, in Ti3C2T x The well-grown MAPbBr3 formed an excellent nanocomposite structure. The exploration of the surface morphology showed that Ti3C2T x / MAPbBr3 nanocomposite nanomaterials were successfully synthesized. Figure 4B and 4C As shown, Ti3C2T x TEM and AFM images of the / MAPbBr3 heterostructures clearly reveal the presence of cubic MAPbBr3 nanocubes on the Ti3C2T x Deposition on the surface of nanosheets. Figure 4D As shown, in Ti3C2T x The average crystal size of the modified MAPbBr3 nanocubes is about 8.2 nm, which is almost the same as that of the original MAPbBr3 nanocubes. This similarity in particle size indicates that even though Ti3C2T was added during the preparation process, x The growth mechanism of nanosheets and MAPbBr3 nanocubes also did not change significantly. Figure 4E As shown, high-resolution transmission electron microscopy (HRTEM) was used to further study the Ti3C2T x / MAPbBr3 heterostructure. Ti3C2T x The HRTEM images of the / MAPbBr3 heterostructures show two different lattice spacings of 0.435 nm and 0.292 nm in different directions, corresponding to the Ti3C2T x The (002) plane of MAPbBr3 and the (100) plane of MAPbBr3. Figure 4F As shown in the figure, the corresponding selected area electron diffraction (SAED) pattern shows bright spots consistent with the lattice spacing, indicating that the modified MAPbBr3 nanocubes on Ti3C2T x It shows typical crystal properties. Figure 4G As shown, EDS element mapping confirmed that Ti3C2T x The distribution of C, O, Br, Pb and Ti elements in the Ti3C2T / MAPbBr3 nanocomposites indicates that MAPbBr3 is dispersed and fixed in the Ti3C2T x superior.

[0073] Figure 5A Schematically shows an X-ray diffraction pattern of a sensing material library according to an embodiment of the present disclosure. Figure 5B The full scan spectrum of the X-ray photoelectron spectrum of the sensing material library according to the embodiment of the present disclosure is schematically shown. Figure 5C The figure schematically shows an X-ray photoelectron spectroscopy carbon element spectrum of the sensing material library according to an embodiment of the present disclosure. Figure 5D The figure schematically shows an X-ray photoelectron spectroscopy titanium element scanning spectrum of the sensing material library according to an embodiment of the present disclosure. Figure 5E The figure schematically shows an X-ray photoelectron spectroscopy bromine element scanning spectrum of the sensing material library according to an embodiment of the present disclosure. Figure 5F The figure schematically shows an X-ray photoelectron spectroscopy lead element spectrum of the sensing material library according to an embodiment of the present disclosure.

[0074] According to the embodiments of the present disclosure, Figure 5A As shown, Ti3C2T with different compositions x Nanostructure and phase composition of the / MAPbBr3 composites, including pristine Ti3C2T xThe characteristic diffraction peaks of cubic phase (Pm3m) MAPbBr3 nanocubes were observed at 2θ = 15.1°, 30.3° and 46.1°, corresponding to the (100), (200) and (300) crystal planes, respectively. x The diffraction peaks of MAPbBr3 nanocubes can be mainly observed in the heterostructure of Ti3C2T3, with a distinguishable peak at around 5.1°, which can be attributed to the x (002) crystal plane. As the MAPbBr3 content gradually increases, Ti3C2T x This observation can be attributed to the fact that X-rays need to penetrate thicker MAPbBr3 nanocubes to reach the bottom Ti3C2T x layer, resulting in Ti3C2T x The intensity of MAPbBr3 decreases due to absorption, diffraction and scattering. However, no impurity peaks were detected in all samples, indicating that the MAPbBr3 nanocubes grown on MXene are of high purity. x The characteristic peaks in the XRD spectrum clearly confirm that Ti3C2T x / MAPbBr3 heterostructure was successfully formed. Figure 5B As shown, X-ray photoelectron spectroscopy (XPS) was used to analyze the Ti3C2T x / MAPbBr3 nanocomposites. The full scan spectrum reveals the chemical composition and bonding state of Ti3C2T x Ti, C, Pb, Br, O, F and N elements are present in / MAPbBr3 nanocomposites. Figure 5C As shown, the high-resolution C1s spectrum shows four singlets at 281.13, 284.54, 286.50 and 288.30 eV, corresponding to C-Ti-Tx, CC, CO and -COO, respectively. Figure 5D As shown, the high-resolution Ti 2p spectrum exhibits three double peaks (Ti 2p 1 / 2 、Ti 2p 3 / 2 ), with an area ratio of 1:2. Ti2p was observed at 454.74, 455.97, and 458.37 eV. 3 / 2 peaks, corresponding to Ti-C, Ti-Ti and TiO2, respectively. These results indicate that Ti-C and Ti-Ti bonds are the main components of Ti3C2T x The main chemical state of Ti in the Ti3C2T x Slight oxidation does not affect its structure and physical and chemical properties. Figure 5E and 5F As shown, Br 3d5 / 2 of 68.22 eV and Br 3d 3 / 2 The 69.21eV peak of Pb 4f 7 / 2 and Pb 4f 5 / 2 The strong peaks at 138.26eV and 143.13eV confirm the existence of MAPbBr3. Overall, XPS analysis shows that Ti3C2T3 is successfully formed. x / MAPbBr3 heterostructures provide evidence and reveal key information about the chemical composition and bonding state of the nanocomposite.

[0075] The dynamic response to the target gas is one of the important indicators for evaluating the response performance of a gas sensor. The dynamic response performance of a gas sensor includes, for example, sensor sensitivity, detection limit, and response / recovery time. The gas response is defined as:

[0076]

[0077] Among them, R a is the resistance of the sensor in air, R g is the resistance of the sensor when exposed to the target gas.

[0078] According to an embodiment of the present disclosure, the plurality of gas samples may include, for example, a test sample and a verification sample, wherein the concentration gradient of each gas in the plurality of gas samples varies. The gas in the test sample may include, for example, at least one of ammonia, acetone, ethanol, ether, and nitrogen dioxide.

[0079] For example, the dynamic sensing response of the sensor is examined by exposing it to target gas samples with continuously varying concentrations. The entire device consists of a vapor detection chamber, a data transmission platform, and a computer. The gas concentration is controlled by adding varying volumes of a solution containing target gas molecules, guided by saturated vapor pressure theory. The sensor generates an analog signal, which is converted into an electrical signal via the data transmission platform, allowing the computer to monitor the sensing process in real time. The proof-of-concept design was tested using typical gases present in the human body or with daily contact: ammonia, acetone, ethanol, and ether.

[0080] Figure 6A Schematic diagram of the original Ti3C2T according to an embodiment of the present disclosure x Gas sensing response diagram to ammonia. Figure 6B Schematic diagram of the original Ti3C2T according to an embodiment of the present disclosure x Gas sensing response graph to acetone. Figure 6C Schematic diagram of the original Ti3C2T according to an embodiment of the present disclosure x Gas sensing response diagram for ethanol. Figure 6D Schematic diagram of the original Ti3C2T according to an embodiment of the present disclosurex Gas sensing response graph to ether.

[0081] According to the embodiments of the present disclosure, four typical gases were tested in the concentration gradient range of 50 ppb to 5 ppm for the original Ti3C2T x The gas response sensitivity of the sensor. Figures 6A to 6D As shown, based on the original Ti3C2T x The real-time response values of the sensor to ammonia, acetone, ethanol and ether are 2.74%, 2.78%, 2.24% and 1.82% respectively at a concentration of 5 ppm. This can be attributed to the Ti3C2T x The adsorption of surface groups on gas molecules causes electron transfer. However, gas sensitivity still needs to be improved.

[0082] Figure 7A The gas sensing response diagram of the sensing material library to ammonia according to an embodiment of the present disclosure is schematically shown. Figure 7B The gas sensing response diagram of the sensing material library to acetone according to an embodiment of the present disclosure is schematically shown. Figure 7C The diagram schematically shows the gas sensing response of the sensing material library to ethanol according to an embodiment of the present disclosure. Figure 7D The gas sensing response diagram of the sensing material library to diethyl ether according to an embodiment of the present disclosure is schematically shown. Figure 7E Schematic diagram of the sensing material library according to the embodiment of the present disclosure and the original Ti3C2T x Comparison of gas sensing responses to ammonia. Figure 7F Schematic diagram of the sensing material library according to the embodiment of the present disclosure and the original Ti3C2T x Comparison of gas sensing responses to acetone. Figure 7G Schematic diagram of the sensing material library according to the embodiment of the present disclosure and the original Ti3C2T x Comparison of gas sensing responses to ethanol. Figure 7H Schematic diagram of the sensing material library according to the embodiment of the present disclosure and the original Ti3C2T x Comparison of gas sensing responses to ether. Figure 8 The diagram schematically shows the response and recovery time of the sensing material library according to an embodiment of the present disclosure to ammonia gas with a concentration of 1 ppm.

[0083] According to the embodiment of the present disclosure, in order to compare the Ti3C2T x / MAPbBr3 sensor and Ti3C2T based x The gas response sensitivity difference of the original sensor was still tested by four typical gases with the same concentration gradient (ranging from 50ppb to 5ppm). Figures 7A to 7D As shown, based on Ti3C2Tx The sensor response value of the / MAPbBr3 composite material increases with the increase of gas concentration. Figures 7E-7H As shown, Ti3C2T x The response values of the / MAPbBr3 sensor to ammonia, acetone, ethanol, and ether at a concentration of 5 ppm were 3.75%, 3.51%, 3.46%, and 2.47%, respectively, which were those of the original Ti3C2T x The gas response of the sensor is greatly improved by 1.37, 1.26, 1.54 and 1.35 times of the sensor response. This phenomenon can be attributed to the change in electron transfer between the target gas and the sensing material due to the adsorption of gas molecules on the surface of the material. x The formation of SB structure at the interface of Ti3C2T x The gas-sensing performance is improved by transferring charge carriers in the sensor. As gas concentration increases, gas adsorption reduces the number of charge carriers and increases channel resistance, resulting in a larger change in sensor resistance and higher sensitivity. Table 1 shows a comparison of the sensing performance of relevant sensing materials for target gases.

[0084] Table 1 Sensitivity of different sensing materials

[0085]

[0086] From Table 1 and Figure 8 It can be seen that MAPbBr3 / Ti3C2T x The detection limit for ammonia reached 1ppm, and the response time to low-concentration target gas was 39s and the recovery time was 34s, showing significant response and excellent gas-sensing performance.

[0087] Figure 9 A schematic diagram shows a comparison of responses of the sensing material library according to an embodiment of the present disclosure to 5 ppm ammonia at different volume fractions.

[0088] According to the embodiments of the present disclosure, Figure 9 As shown, based on Ti3C2T x The response of the sensor based on Ti3C2T3 / MAPbBr3 shows a convex function characteristic with the increase of MAPbBr3 volume fraction (V). x / MAPbBr3 sensor to each gas response first increased and then decreased. x The maximum response was achieved when the mass ratio of MAPbBr3 was 1:2 (corresponding to a mass ratio of 5:1). This phenomenon was mainly attributed to the increase in the size and number of MAPbBr3 nanoparticles with the increase in MAPbBr3 concentration.x The modification on the surface of the nanocomposite leads to an increase in active sites and enhanced gas adsorption. However, with further increase in MAPbBr3 concentration, agglomeration begins to occur, which may reduce the effective active sites of the sensing material.

[0089] Figure 10A A diagram schematically illustrates the linear relationship between the logarithm of the response of the sensing material library and the logarithm of the ammonia concentration according to an embodiment of the present disclosure. Figure 10B A linear relationship diagram between the logarithm of the response of the sensing material library and the logarithm of the acetone concentration according to an embodiment of the present disclosure is schematically shown. Figure 10C A linear relationship diagram between the logarithm of the response of the sensing material library and the logarithm of the ethanol concentration according to an embodiment of the present disclosure is schematically shown. Figure 10D A linear relationship diagram between the logarithm of the response of the sensing material library and the logarithm of the diethyl ether concentration according to an embodiment of the present disclosure is schematically shown.

[0090] According to an embodiment of the present disclosure, in order to visualize the relationship between the target gas concentration and the sensor response, a Ti3C2T x The natural logarithm of the sensor response value of / MAPbBr3 is linearly fitted as a function of the natural logarithm of the gas concentration. Figures 10A to 10D As shown in Figure 2, the slopes of the fitted linear lines for ether and ammonia responses decrease with increasing gas concentration, which can be attributed to the saturation of surface active sites. x The sensor of Ti3C2T / MAPbBr3 exhibits good linearity for ethanol and acetone vapors in the range of 50 ppb to 5 ppm, which indicates that the Ti3C2T x / MAPbBr3 surface active sites are still in an unsaturated state. x The sensor based on MAPbBr3 can be used for further practical applications over a 1000-fold (50 ppb to 5 ppm) concentration range for real-time monitoring and concentration measurement of ethanol and acetone.

[0091] Figure 11A The long-term response graph of the sensing material library to ammonia at a concentration of 5 ppm according to an embodiment of the present disclosure is schematically shown. Figure 11B Schematic diagram showing the cyclic performance of the long-term response of the sensing material library to ammonia at a concentration of 5 ppm according to an embodiment of the present disclosure.

[0092] According to the embodiments of the present disclosure, the sensing material library can be used to perform multiple adsorption and desorption of multiple gas samples. The stability of a gas sensor is also an important indicator for measuring its sensing performance, representing the potential of the gas sensor in practical applications. It is completed through multiple independent identical tests. Figure 11A As shown, based on Ti3C2Tx The sensor of Ti3C2T3 / MAPbBr3 was repeatedly measured in an ethanol atmosphere with a concentration of 5 ppm for 10 days, and 85.6% of the initial response value was maintained after 10 days. x / MAPbBr3 sensors have long-term stability. Figure 11B As shown in the figure, the Ti3C2T based x The stability of the sensor response of MAPbBr3 / MAPbBr3 was demonstrated. Over five consecutive test cycles, it exhibited a stable and repeatable dynamic response to ammonia, with no significant change in sensitivity. The results demonstrate that the adsorption and desorption of the target gas on the sensing material surface is repeatable.

[0093] According to the embodiment of the present disclosure, a simple and reliable Ti3C2T based dynamic modulation strategy is developed. x / MAPbBr3 material library, effectively reducing the manufacturing cost of BSA, while still maintaining good performance. x / MAPbBr3 and Ti3C2T x The material has excellent gas sensing performance and distinguishable sensing response. The material library is an excellent component for manufacturing BSA. The biomimetic sensor array (BSA) includes at least 4 sensors. Among them, Ti3C2T x Different mass ratios with MAPbBr3 and Ti3C2T x The mass ratio of MAPbBr3 is in the range of 3:1 to 10:1.

[0094] For example, the structure of the sensor used in one embodiment of the present disclosure is a stacked PET plate, a silicon substrate, a gold electrode and a sensing material library. x The mass ratios of MAPbBr3 to MXene are different, for example, 3:1, 5:1, 7:1, and 9:1. To increase the number of detectable gases and sensitivity of the sensor array, at least one auxiliary sensor can be added to the biomimetic sensor array. For example, the auxiliary sensor is an unmodified MXene nanosheet.

[0095] Figure 12 A schematic diagram of a sensor array according to an embodiment of the present disclosure is shown.

[0096] For example, Figure 12 As shown, the BSA consists of 6 sensors, including sensor 1: P / M-1, sensor 2: P / M-2, sensor 3: P / M-3, sensor 4: P / M-4, sensor 5: M-1 and sensor 6: M-2. P / M is Ti3C2T x / MAPbBr3 composite material, the suffix represents MAPbBr3 and Ti3C2T x For example, P / M-1 represents the volume fraction ratio of MAPbBr3 to Ti3C2T x The volume ratio of Ti3C2T is 1:1, and P / M-2 means that the ratio is 2:1. M-1 and M-2 represent the original Ti3C2T with a concentration of 1 mg / mL and 0.1 mg / mL, respectively. x .

[0097] For example, the signal conversion module is an Arduino platform. Because BSA has excellent selective sensing performance for odor molecules, its application scenarios can be greatly expanded. For example, an instant gas detection platform (IDP) is constructed using the Arduino platform and BSA. Compared to traditional gas sensor platforms, it measures only 16cm*15cm*5cm, for example, offering excellent portability and enabling real-time gas-specific detection, which is crucial for human recognition.

[0098] For example, the present disclosure uses an instant detection platform (IDP) to perform dynamic gas sensing measurements at ambient temperature. A 1.2-liter container serves as the vapor sensing chamber. During the experiment, solutions of gas molecules of varying volumes are introduced directly into the container through an aperture located on its upper surface. After injection, the container is sealed with transparent tape. The concentration of the test gas is calculated using the following formula:

[0099]

[0100] Where ρ (in g / mL) is the density of gas molecules, V (in μL) is the volume of gas molecule solution, ω is the mass fraction of gas molecule solution, M (in g / mol) is the molar mass of gas molecules, and V m (unit: L / mol) is the molar volume of the ideal gas, V c (in L) is the volume of the vapor sensing chamber. An Arduino-based six-channel gas sensing system records steady-state resistance values after exposure to pure air and air containing the target gas. The Arduino board facilitates data acquisition and storage for dynamic gas detection. A host computer program collects data from all six sensors at a constant rate. A DC voltage of 5 V was applied, and the measurement error was determined to be less than 0.1%.

[0101] Figure 13 The figure schematically shows an experimental operation diagram of a biomimetic sensor array according to an embodiment of the present disclosure.

[0102] For example, Figure 13As shown in Figure 2, the sensor array's sensing performance was tested for the four typical gases mentioned above: ammonia, acetone, ethanol, and diethyl ether. The dynamic sensing response of the BSA was examined by exposing it to continuously varying concentrations of the target gas, the same gas concentrations as in the previous examples. A classification algorithm was introduced to help distinguish between the collected signals.

[0103] Figure 14A The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to four typical gases with a concentration of 5 ppm. Figure 14B The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to ammonia gas with a concentration of 50 ppb to 5 ppm. Figure 14C The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to acetone with a concentration of 50 ppb to 5 ppm. Figure 14D The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to ethanol with a concentration of 50 ppb to 5 ppm. Figure 14E The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to diethyl ether with a concentration of 50 ppb to 5 ppm. Figure 14F The 2D PCA diagram of the biomimetic sensor array according to an embodiment of the present disclosure for four typical gases at 5 ppm is schematically shown.

[0104] According to the embodiments of the present disclosure, Figures 14A to 14E As shown, the gas-sensitive response of each sensor increases with the increase of the target gas concentration. It can be seen that the six individual sensors have different responses to the target gas due to their different compositions. For example, for the response of BSA to ethanol gas at 50 ppb, the sensor 5 with the largest response is 3.12 times that of the corresponding smallest sensor 1. This difference can be used to demonstrate the selectivity of BSA. Seven consecutive experiments were conducted at 5 ppm for each of the four gases mentioned above, and the sensor data were analyzed using principal component analysis (PCA). Figure 14F As shown, the BSA is able to distinguish gases at a concentration of 5 ppm, thus having excellent selectivity for gases. It turns out that the BSA of the embodiment of the present disclosure is successfully manufactured, which proves that the BSA constructed by a single sensor of different components has a significant effect on the selective recognition of target gases.

[0105] According to an embodiment of the present disclosure, the gas in the verification sample includes, for example, at least one of carvacrol, butyl cinnamate, indole, terpinolene, nonanal, acetaldehyde, citral, farnesol, 2,3-dihydro-2,2,6-trimethylbenzaldehyde, and 4-isopropylbenzyl alcohol.

[0106] To further verify the gas-sensing properties of BSA in practice, 10 odor molecules listed in Table 2 were used for testing. These odor molecules are representative due to their widespread presence in animals, plants, and daily life.

[0107] Table 2 Typical odor molecules in 10 kinds of cattle life

[0108]

[0109] Figure 15A The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to carvacrol at a concentration of 50 ppb to 5 ppm. Figure 15B The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to butyl cinnamate with a concentration of 50 ppb to 5 ppm. Figure 15C The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to indole at a concentration of 50 ppb to 5 ppm. Figure 15D The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to terpinolene at a concentration of 50 ppb to 5 ppm. Figure 15E The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to nonanal at a concentration of 50 ppb to 5 ppm. Figure 15F The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to acetaldehyde with a concentration of 50 ppb to 5 ppm. Figure 15G The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to citral at a concentration of 50 ppb to 5 ppm. Figure 15H The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to farnesol at a concentration of 50 ppb to 5 ppm. Figure 15I The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to 2,3-dihydro-2,2,6-trimethylbenzaldehyde at a concentration of 50 ppb to 5 ppm. Figure 15J The diagram schematically shows the sensing response results of the biomimetic sensor array according to an embodiment of the present disclosure when exposed to 4-isopropylbenzyl alcohol with a concentration of 50 ppb to 5 ppm. Figure 16 The figure schematically shows the average response results of each sensor in the biomimetic sensor array according to an embodiment of the present disclosure to ten odor molecules at a concentration of 5 ppm.

[0110] According to the embodiments of the present disclosure, Figures 15A to 15JAs shown in Figure 2, the dynamic sensing response of BSA was tested by exposing BSA to 10 target gases with concentrations continuously varying from 50 ppb to 5 ppm. Figure 16 As shown, the average responses of each sensor to the ten odor molecules differed to some extent, indicating the potential selectivity of BSA.

[0111] According to an embodiment of the present disclosure, the signal processing module may further include a preprocessing model for performing dimensionality reduction preprocessing on the multidimensional sensor electrical signal. The preprocessing model may include, for example, a principal component analysis model and a t-distributed stochastic neighbor embedding model. Using t-SNE and PCA to preprocess data can bring many benefits, particularly when processing high-dimensional data, as they can improve data processing efficiency and accuracy.

[0112] Figure 17 The 3D PCA diagram of the biomimetic sensor array according to an embodiment of the present disclosure for 10 odor molecules at 5 ppm is schematically shown. Figure 18 The 2D t-SNE diagram of the biomimetic sensor array according to an embodiment of the present disclosure for 10 odor molecules at 5 ppm is schematically shown.

[0113] According to the embodiments of the present disclosure, Figure 17 As shown in the figure, seven consecutive experiments were conducted on a gas with a concentration of 5 ppm, and PCA was used to analyze and process the data. It can be seen that since the gas-sensitive properties of the ten odor molecules are relatively similar, data processing using only PCA cannot effectively distinguish them. Compared with PCA, t-distributed stochastic neighbor embedding (t-SNE) as a nonlinear data dimensionality reduction method can ensure that the distribution of low-dimensional data is highly similar to the distribution of the original feature space, and generally has good effects in feature dimensionality reduction and visualization. Figure 18 As shown, by extracting features and performing dimensionality reduction from the data of each sensor, a corresponding t-SNE plot is generated. The values on the horizontal and vertical axes represent the relative differences between data points. After processing using the t-SNE algorithm, the data points are clearly clustered together and separated from other groups. The results show that the t-SNE algorithm performs better than the PCA algorithm in identifying 10 gas molecules, demonstrating the more accurate pattern recognition capabilities of t-SNE in BSA applications. After testing BSA with 10 odor molecule samples, this strategy has been proven to be simple and effective, and has the potential for practical application.

[0114] According to an embodiment of the present disclosure, a bionic gas sensing device can be used, for example, for human body identification. Human body identification has a wide range of application scenarios in public safety, medical diagnosis, environmental protection, etc., such as escape tracking, early detection of diseases, monitoring of environmental pollution impacts, etc. The smell of exhaled breath covers the characteristics of the human body. At the same time, the smell of human clothes reflects the characteristics emitted by human pores. These two smells represent the characteristics of the human body. Since human body odor is composed of so many components, a theory proposes that it can be simplified into a "complex gas molecule". Since each person's "gas molecules" are different, the corresponding electrical signals are also unique, which can be detected by sensors and distinguished with the help of machine learning (ML).

[0115] For example, the composition of odor in exhaled breath is complex, and the odor composition of each person is different, which is the basis of human recognition. The present disclosure selectively detects the odor molecules in the exhaled breath and clothing of 16 volunteers through IDP, and accurately identifies the corresponding odors and people through ML, demonstrating the high performance of BSA-based IDP in gas sensing and its potential in the field of human recognition. The present disclosure uses ML to assist in processing the different electrical signals of the exhaled breath of different volunteers, and the IDP successfully identifies the exhaled odor with the corresponding person. Participants were instructed to exhale steadily onto the BSA at a fixed distance of 10 cm for 2 seconds. The scale of the test data is, for example, 16 volunteers * 100 exhalations, which means that 1,600 sets of data were tested using IDP.

[0116] Figure 19 The figure schematically shows the average response of the biomimetic gas sensing device to breath odor according to an embodiment of the present disclosure. Figure 20 The PCA result diagram of the biomimetic gas sensing device for exhaled breath odor according to an embodiment of the present disclosure is schematically shown.

[0117] According to the embodiments of the present disclosure, Figure 19 As shown in the figure, IDP has a significant response to everyone's exhaled breath. This proves that it has a high sensitivity and can respond sensitively to low concentrations of human exhaled gas. In terms of the selectivity of IDP, based on the differences in the responses of different volunteers to exhaled gas, it has significant specificity for each volunteer's exhaled gas. Figure 20 As shown, the PCA results showed that most of the measured samples had relatively good clustering, which can be attributed to the differences in the composition and content of the exhaled gas of each volunteer.

[0118] Figure 21A The figure schematically shows a statistical histogram of breath odor data generated by the biomimetic gas sensing device according to an embodiment of the present disclosure. Figure 21B The figure schematically shows a statistical box plot of breath odor data obtained by the biomimetic gas sensing device according to an embodiment of the present disclosure. Figure 21CThe figure schematically shows a scatter matrix diagram of data statistics of breath odor by the biomimetic gas sensing device according to an embodiment of the present disclosure.

[0119] According to the embodiment of the present disclosure, in order to further realize odor detection and identify the corresponding person through IDP, ML algorithm is introduced for data processing. First, the data obtained by IDP is statistically reviewed. Figure 21A As shown in the histogram, the response distribution of each sensor in BSA to all samples can be seen. Figure 21B As shown in Figure 2, the response of each sensor can be seen in the box plot, showing the differences in the signal among different people. Figure 21C As shown in the figure, the scatter matrix diagram shows the statistical relationship between the sensors, indicating that the data is distinguishable. Then, the data collected by the sensors is processed by machine learning algorithms.

[0120] According to an embodiment of the present disclosure, the classification model includes, for example: a K-nearest neighbor algorithm model, a naive Bayes model, a support vector machine, and a classification and regression tree model.

[0121] For example, the present disclosure introduces 6 ML algorithms, namely logistic regression (LR), linear discriminant analysis (LDA), K-nearest neighbor algorithm (KNN), classification and regression tree (CART), naive Bayes model (NB) and support vector machine (SVM). LR and LDA are linear models that detect linear relationships in data. KNN is an instance-based learning algorithm that classifies according to the labels of the k nearest data points. CART constructs a tree structure by recursively splitting the data, with each leaf node corresponding to a class. NB assumes that all features are independent and uses Bayes' theorem to calculate the posterior probability. SVM is a binary classification algorithm used to find the optimal hyperplane for classification. The training samples account for 90% of the total sample size, and the remaining 10% are test samples.

[0122] Figure 22A The confusion matrix of the biomimetic gas sensing device for breath odor according to an embodiment of the present disclosure is schematically shown. Figure 22B A diagram schematically shows a comparison of the algorithm accuracy results of the bionic gas sensing device for exhaled breath odor according to an embodiment of the present disclosure.

[0123] According to the embodiments of the present disclosure, Figure 22A As shown in Figure 2, among the 16 volunteers, except for a few participants with lower accuracy, the accuracy rates of the rest were above 70%. Figure 22B As shown in the figure, after 10 cross-validation tests, the CART algorithm has the highest accuracy of 0.682, which can well identify human breath samples, while the accuracy of the LR algorithm is only 0.183, proving that there is no obvious linear relationship between the samples.

[0124] According to embodiments of the present disclosure, in addition to detecting and identifying odors in exhaled breath, the present disclosure also studies the odor of clothing, which reflects unique skin signals. Clothes are initially placed in a sealed bag and sealed at the top to promote the diffusion of clothing odor within the bag. The BSA is then quickly exposed to the atmosphere to react.

[0125] Figure 23 The PCA result diagram of the biomimetic gas sensing device for clothing odor according to an embodiment of the present disclosure is schematically shown. Figure 24A The confusion matrix of the bionic gas sensing device for clothing odor according to an embodiment of the present disclosure is schematically shown. Figure 24B A diagram schematically shows a comparison of algorithm accuracy results for clothing odor using a bionic gas sensing device according to an embodiment of the present disclosure.

[0126] For example, the scale of the test data is 16 volunteers * 70 clothing features, which means that 1120 sets of data were obtained using IDP. In contrast to the exhaled breath odor measurement, the BSA response time is extended by 30 seconds relative to the exhaled breath due to the lower concentration of clothing odor molecules. In terms of sensitivity, the average response data shows that although the concentration of clothing odor is low, resulting in a lower response relative to the exhaled breath, a significant response is still observed, indicating that the IDP has excellent sensitivity. This may be because although the concentration of gas molecules is low, the surface groups of BSA have excellent adsorption capacity and still have a high response at low concentrations. Figure 23 As shown in , PCA of the data revealed clustering of clothing response data for certain volunteers, indicating that BSA exhibited high selectivity for low concentration odor sources. Figure 24A As shown in Figure 2, six algorithms were applied to clothing odor detection and were able to successfully detect and identify some volunteers. Figure 24B As shown in Figure 3, by comparing all algorithms, the CART algorithm still has the highest accuracy of 51.1%.

[0127] Another aspect of the present disclosure provides a method for preparing a biomimetic sensor array, for example comprising:

[0128] S110, preparation of MXene nanosheets using the tuned microenvironment method.

[0129] According to the embodiment of the present disclosure, a multilayer Ti3C2T x , and then prepare few-layer Ti3C2T x Nanosheets.

[0130] For example, 2 g of LiF was dissolved in 40 mL of 9 M HCl solution in a polytetrafluoroethylene (PTFE) reactor and stirred at 35 ° C for 30 minutes. Then 2 g of Ti3AlC2 (MAX phase) was slowly added and stirred at a constant temperature of 35 ° C and a speed of 600 rpm for 24 h. The resulting solution was washed with deionized water at 3500 rpm until the pH of the supernatant reached 6. Finally, the collected multilayer Ti3C2T x MXene was freeze-dried using a vacuum freeze dryer for 36 h.

[0131] It should be noted that the number of MXene layers can be determined using experimental techniques such as transmission electron microscopy (TEM) and atomic force microscopy (AFM). Multilayer MXenes generally refer to MXenes with more than five layers. These materials have thicker layers, so their electrical and thermal properties may be similar to those of traditional layered materials. Few-layer MXenes generally refer to MXenes with fewer than or equal to five layers. These materials have thinner layers, so they may have better electrical and thermal properties and exhibit unique performance in certain applications.

[0132] It should be noted that the boundaries between multilayer and few-layer structures may be different for different types of MXenes.

[0133] Because MXene's surface active groups, -OH, -O, and -F, are polar solvent-loving, MXene is readily soluble in water but poorly dispersed in organic polar solvents such as DMF. Perovskites, on the other hand, are active in organic solvents but inactive in water. Furthermore, their synthesis requires a solvent that changes from polar to nonpolar. Synthesis of these materials requires the use of organic solvents, and the polarity of the solvent can vary, making the synthesis of MXene / perovskite materials challenging.

[0134] This paper uses a tuned microenvironmental approach to prepare MXene nanosheets. This method involves using surface modifiers to adjust the surface environment of two-dimensional materials, enabling their dispersion in organic solvents. This method ensures the formation of larger flakes with fewer defects. Because it eliminates the need for harsh ultrasonic treatment, these materials exhibit greater oxidative stability.

[0135] For example, the tuned microenvironment method (TMM) was introduced to prepare few-layer Ti3C2T dispersed in N,N-dimethylformamide (DMF). x Nanosheets. 0.5g multilayer Ti3C2T xThe powder was added to 12 mL of a 25% tetrabutylammonium hydroxide (TBAOH) solution and stirred at 500 rpm for 6 h at room temperature for intercalation. Ethanol was then used to wash away excess TBAOH, and all precipitates were collected in a 50 mL centrifuge tube. About 30 mL of DMF was added to the centrifuge tube and shaken until the precipitate was completely dispersed. To collect the upper liquid, centrifuge at 12,000 rpm for 5 minutes. After separating the solution and precipitate, DMF was added again and centrifuged repeatedly until 50 mL of solution was collected.

[0136] S120, synthesis of perovskite nanoparticles using a ligand-assisted reprecipitation strategy.

[0137] For example, pure MAPbBr3 nanocrystals were prepared using a ligand-assisted reprecipitation (LARP) strategy. 0.2 mmol of CH3NH3Br and 0.2 mmol of PbBr2 were dissolved in 5 mL of DMF, and then 20 μL of n-octylamine (OTA) and 0.5 mL of oleic acid (OA) were added to form a precursor solution. Then, 20 μL of the precursor solution was added dropwise to 3 mL of toluene and shaken by hand for 5 seconds. The color of the solution changed from transparent to light green, indicating the crystallization of MAPbBr3 nanocrystals.

[0138] It should be noted that the ligand-assisted reprecipitation strategy is a perovskite crystal synthesis strategy that involves dissolving the perovskite precursor in a polar organic solvent, then adding a fixed amount of the perovskite precursor solution dropwise to a non-polar solvent with vigorous stirring. This method offers mild synthesis conditions and a simple operating procedure, while avoiding the use of inert gases and high temperatures.

[0139] S130, preparing multiple sensing material libraries based on Mxene and perovskite through an in-situ growth process, wherein the mass ratio of Mxene to perovskite is different in different sensing material libraries.

[0140] For example, to prepare Ti3C2T x / MAPbBr3 nanocomposites, 0.2mmol of CH3NH3Br and 0.2mmol of PbBr2 were dissolved in 5mL of DMF, and then 20μL of OTA and 0.5mL of OA were added to form a parent ion solution. The above 20μL parent ion solution was mixed with 20μL of Ti3C2T x The DMF suspension (~5 mg / mL) was mixed and shaken. Then 3 mL of toluene was added. A dark green solution was formed, indicating that the target composite material was successfully synthesized. After centrifugation at 10000 rpm for 10 minutes, the precipitate was collected and washed three times with toluene for further characterization. This composite material was recorded as P / M-1. xWhen the volume of DMF suspension was constant, the added volume of parent ion solution was adjusted to 40 μL, 60 μL and 80 μL respectively, and the products synthesized according to this step were recorded as P / M-2, P / M-3 and P / M-4. x DMF suspension was used to prepare Ti3C2T2 at two different concentrations, 1 mg / mL and 0.1 mg / mL. x The DMF suspensions were labeled as M-1 and M-2, respectively.

[0141] It's important to note that in situ growth involves growing the target material on the sample surface without disturbing the sample's dynamics or moving it. This contrasts with ex situ growth, where the sample is removed from its growth environment for manipulation during the experiment, then returned to its original location for the next measurement. In ex situ experiments, the sample may have undergone some changes. In situ growth is simpler and provides more reliable data.

[0142] S140, spraying different sensing material libraries onto the electrode array respectively to obtain a bionic sensor array.

[0143] For example, 10 mg of the composite materials P / M-1, P / M-2, P / M-3, and P / M-4 were dispersed in 10 mL of toluene to form a 1 mg / mL solution. Six interdigitated electrodes were systematically fixed on a polyethylene terephthalate (PET) substrate, serving as the backbone of the BSA. 2 mL of each of the six materials was evenly sprayed onto the interdigitated electrodes using a spray gun and then dried in a 60°C vacuum oven for 4 hours to form a gas-sensitive film. The resulting BSA sensors were designated as sensor 1: P / M-1, sensor 2: P / M-2, sensor 3: P / M-3, sensor 4: P / M-4, sensor 5: M-1, and sensor 6: M-2.

[0144] It is understood that a ±10% range can be introduced for physical quantities such as time and volume in the experimental portion, and this is considered a reasonable range. For example, the range for M-1 can be 0.08 mg / mL to 0.12 mg / mL, and the range for M-2 can be 0.8 mg / mL to 1.2 mg / mL.

[0145] In summary, the disclosed embodiments provide a biomimetic gas sensing device. By dynamically modulating a library of tunable sensing materials based on MXene and perovskite, combined with a special small sensing chamber, an integrated modular biomimetic gas sensing device was successfully fabricated. The gas sensing performance of the sensing material library was tested, and the results showed a 30%-70% improvement compared to the original MXene material. With the help of an ML algorithm, the biomimetic gas sensing device achieved a success rate of 68.2% and 51.1% in identifying human breath and clothing odors, respectively, demonstrating excellent performance in human recognition.

[0146] Any details not included in the method embodiment section are similar to those in the device embodiment section. Please refer to the device embodiment section and will not be repeated here.

[0147] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to a specific order or hierarchy.

[0148] It should also be noted that directional terms such as "upper," "lower," "front," "back," "left," and "right" mentioned in the embodiments are merely references to the directions in the accompanying drawings and are not intended to limit the scope of protection of this disclosure. Throughout the drawings, identical elements are represented by identical or similar reference numerals. Conventional structures or configurations that may cause confusion in understanding this disclosure will be omitted. Furthermore, the shapes, sizes, and positional relationships of the components in the drawings do not reflect their actual sizes, proportions, or actual positional relationships.

[0149] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the disclosure comprises less than all features of any individual disclosed embodiment. The appended claims are therefore hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the disclosure.

[0150] In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present disclosure, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. With respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", as explained in terms of "including" used as a transitional word in the claims. Any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

[0151] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present disclosure. It should be understood that the above are only specific embodiments of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A biomimetic gas sensing device, characterized in that: include: Bionic sensor array, used to detect multiple gas samples and obtain multi-dimensional sensing analog signals; A signal conversion module, configured to convert the multi-dimensional sensing analog signal to obtain a multi-dimensional sensing electrical signal; a signal processing module, configured to process the multidimensional sensing electrical signal using a pre-trained classification model to identify components of the plurality of gas samples; Wherein, the biomimetic sensor array includes a plurality of sensors, and the sensors include a library of sensing materials based on MXene and perovskite; The sensing material library is a Schottky barrier structure, and different sensors detect the same gas sample to obtain different signals.

2. The biomimetic gas sensing device according to claim 1, characterized in that: The sensing material library is MXene nanosheets modified with a second phase; Wherein, the second phase includes organic metal halide perovskite AMX3.

3. The biomimetic gas sensing device according to claim 2, characterized in that: The MXenes include: Mo2C, Ti3C2, Nb2C, Nb4C3, Ta4C3, Ti2C, V2C, V4C3 and Ti3C2T x ; In the organometallic halide perovskite AMX3, A includes MA, FA and Cs, M includes Pb, and X includes Cl, Br, I and combinations thereof.

4. The biomimetic gas sensing device according to claim 3, characterized in that: The sensing material library includes Ti3C2T x / MAPbBr3 nanocomposite library; Wherein, T includes -O, -OH and -F.

5. The biomimetic gas sensing device according to claim 4, characterized in that: The bionic sensor array includes at least 4 sensors; Among them, different sensors, Ti3C2T x A different mass ratio than MAPbBr3; and Ti3C2T x The mass ratio of MAPbBr3 is in the range of 3:1 to 10:

1.

6. The biomimetic gas sensing device according to claim 2, characterized in that: The bionic sensor array further includes an auxiliary sensor; The auxiliary sensor is the unmodified MXene nanosheet.

7. The biomimetic gas sensing device according to claim 1, characterized in that: The signal conversion module is an Arduino platform.

8. The biomimetic gas sensing device according to claim 1, characterized in that: The classification models include: K-nearest neighbor algorithm model, naive Bayes model, support vector machine and classification and regression tree model.

9. The biomimetic gas sensing device according to claim 8, characterized in that: The signal processing module further includes: A preprocessing model, used for performing dimensionality reduction preprocessing on the multidimensional sensor electrical signal; The preprocessing model includes a principal component analysis model and a t-distributed random neighbor embedding model.

10. The biomimetic gas sensing device according to claim 1, characterized in that: The plurality of gas samples include a test sample and a verification sample, and the concentration gradient of each gas in the plurality of gas samples varies; Wherein, the gas in the test sample includes at least one of ammonia, acetone, ethanol, ether and nitrogen dioxide; The gas in the verification sample includes at least one of carvacrol, butyl cinnamate, indole, terpinolene, nonanal, acetaldehyde, citral, farnesol, 2,3-dihydro-2,2,6-trimethylbenzaldehyde and 4-isopropylbenzyl alcohol.

11. The biomimetic gas sensing device according to claim 1, characterized in that: The sensing material library is used to perform multiple adsorption and desorption on the multiple gas samples.

12. The biomimetic gas sensing device according to claim 1, characterized in that: The sensor further comprises: A substrate and electrodes stacked sequentially; Wherein, the sensing material library is arranged on the electrode; The substrate includes any one of PET, silicon, glass, ceramic and ITO, and combinations thereof.

13. The biomimetic gas sensing device according to any one of claims 1 to 12, characterized in that: The bionic gas sensing device is used for human body recognition.

14. A method for preparing a biomimetic sensor array, characterized in that: include: MXene nanosheets were prepared using a tuned microenvironment method; The perovskite nanoparticles were synthesized using a ligand-assisted reprecipitation strategy; preparing a plurality of MXene- and perovskite-based sensing material libraries through an in situ growth process, wherein different sensing material libraries have different mass ratios of the MXene to the perovskite; The different sensing material libraries are sprayed onto the electrode array respectively to obtain the bionic sensor array.

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