Methane electronic nose system based on semiconductor material and application

Abstract

The invention belongs to the technical field of sensing devices and intelligent manufacturing, and particularly discloses a methane electronic nose system based on semiconductor materials and application. The methane electronic nose system comprises a central control device; the two or more sensing element combinations are respectively connected with the central control device; the sensing element combination comprises a Pd-SnO2 sensing element, a Pd4Au1-SnO2 sensing element, a Pd2Au1-SnO2 sensing element and a Pd1Au1-SnO2 sensing element, and the sensing element combination comprises a Pd-SnO2 sensing element and a Pd4Au1-SnO2 sensing element; each set of sensing element combination is provided with a temperature control device, so that each sensing element can work at a set temperature and obtain a first signal corresponding to gas to be detected; and the central control device is used for obtaining the concentration of methane in the gas to be detected according to the first signal acquired by each sensing element and the second model. The semiconductor material used in the invention is simpler and easier to synthesize, so that the production cost is reduced.

Description

Technical Field

[0001] The present application belongs to the field of sensor devices and intelligent manufacturing technology, and more specifically, relates to a methane electronic nose system and application based on semiconductor materials. Background Art

[0002] Methane is an important industrial energy source, widely used in various fields such as metallurgy, petrochemicals, and residential heating. However, as a colorless, odorless, and highly flammable gas, its lower explosive limit in ambient air is 4.4–5%, making it highly explosive and posing a significant safety hazard. In recent years, household gas explosions have occurred frequently, and monitoring methane leaks has received increasing attention. Therefore, the development of high-performance methane sensors for real-time and rapid detection is crucial. Current methane detection technologies mainly include catalytic combustion methane sensors, infrared methane sensors, and semiconductor methane sensors. Catalytic combustion methane sensors offer good selectivity for methane, but their catalysts are susceptible to poisoning and have a short lifespan. Infrared methane sensors offer high detection accuracy, but their high equipment cost hinders widespread application. Semiconductor methane sensors are inexpensive, but individual semiconductor methane sensors can be interfered with by common indoor gases such as acetic acid, ethanol, and formaldehyde, leading to false detections and missed detections. Furthermore, electronic nose systems constructed using semiconductor sensor arrays and intelligent recognition algorithms can effectively improve the selectivity and accuracy of target gases in complex atmospheric environments.

[0003] Currently, electronic nose systems primarily consist of sensor arrays with cross-sensitivity, signal acquisition modules, and simple pattern recognition algorithms, making them ineffective in adapting to widely varying atmospheres. Therefore, for methane gas detection in the presence of interfering gases such as acetic acid, ethanol, and formaldehyde, a rational combination of multiple sensor arrays with cross-sensitivity, signal acquisition and processing units, complex atmosphere training sets, and intelligent recognition algorithms is required to achieve real-time, online, and accurate detection.

[0004] Patent CN119064426.A discloses a "V2C / V2O5 / NiO composite gas-sensing material and its preparation method." By partially oxidizing V2C MXene, V2O5 nanoblocks are derived from the V2C MXene layered structure to obtain uniformly distributed nano-V2O5 with a "noodle-like" structure. NiO nanoparticles are then obtained by heat-treating Ni-MOF to form NiO nanoparticles, resulting in a V2C / V2O5 / NiO composite gas-sensing structure. This inventive method, using V2C / V2O5 / NiO composite thin films, produces a methane sensor with excellent reversibility, fast response / recovery speed, and selectivity. It can detect methane gas concentrations in the range of 500 ppm to 4000 ppm, effectively detecting even low concentrations at low temperatures. This effectively addresses the shortcomings of conventional methane gas sensors, such as slow response, poor sensitivity at low concentrations, and poor durability under high-temperature operating conditions. However, the material preparation method is complex and costly, making stable mass production difficult. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the purpose of this application is to select lower-cost semiconductor materials to form a sensing element combination, thereby reducing the cost of the electronic nose system while further improving the detection sensitivity of methane.

[0006] To achieve the above objectives, the present application provides a methane electronic nose system based on semiconductor materials, comprising a central control device; two or more sets of sensing element combinations respectively connected to the central control device; the sensing element combinations comprising a Pd-SnO2 sensing element, a Pd4Au1-SnO2 sensing element, a Pd2Au1-SnO2 sensing element, and a Pd1Au1-SnO2 sensing element; Each set of the sensing element assembly is provided with a temperature control device, so that each sensing element can operate at a set temperature and obtain a second signal corresponding to the gas to be measured; the second signal is eight or more parameters selected from the group consisting of baseline resistance, minimum response, response time, recovery time, integral area of ​​the response curve, integral area of ​​the recovery curve, maximum derivative of the response curve, minimum derivative of the response curve, spectrum peak, center frequency, bandwidth, spectrum entropy, and spectrum kurtosis; The central control device is used to obtain the concentration of methane in the gas to be measured based on the second signal collected by each sensing element and the second model.

[0007] Preferably, the set temperature of the temperature control device includes a first temperature and a second temperature, the first temperature is between 185°C and 195°C, and the second temperature is between 225°C and 235°C.

[0008] Preferably, the second signal is obtained by feature extraction and normalization of the first signal, and the first signal is a response curve of the sensing element to the resistance of the gas to be measured at the set temperature.

[0009] As a further preference, the first signal is obtained by cleaning and smoothing the original data.

[0010] As further preferred, the second signal is baseline resistance, minimum response, response time, recovery time, integral area of ​​response curve, integral area of ​​recovery curve, maximum derivative of response curve and minimum derivative of response curve.

[0011] Preferably, the method for obtaining the second model is: S21 corresponds to at least six different methane concentration calibration gas as the test gas, collecting the second signal corresponding to the calibration gas; S22. After normalizing the second signal in step S21, the second signal is trained with the corresponding methane concentration in the calibration gas in a support vector machine to obtain the second model.

[0012] As further preferred, in step S21, each calibration gas with a different methane concentration corresponds to at least two different humidity levels.

[0013] Preferably, the central control device is further configured to obtain the gas type of the gas to be measured based on the second signal collected by each sensing element and the first model.

[0014] As further preferred, the gas types include methane, ethanol, formaldehyde, acetic acid, a mixed gas of methane and ethanol, a mixed gas of methane and formaldehyde, and a mixed gas of methane and acetic acid.

[0015] As further preferred, the method for obtaining the first model comprises the following steps: S11. The different gas types, each gas type corresponds to at least six different concentrations of calibration gas as the test gas, collecting the second signal corresponding to the calibration gas; S12. After reducing the dimension of the second signal in step S11 through linear discriminant analysis, training is performed in a support vector classifier according to the gas type of the calibration gas corresponding to the second signal to obtain the first model.

[0016] The present application also provides the application of the above-mentioned methane electronic nose system in methane detection.

[0017] In general, compared with the prior art, the above technical solution conceived by this application can realize methane detection due to the system integration of the sensing element combination and the model constructed by the intelligent recognition algorithm, and has the following specific advantages: 1. The semiconductor material used in the sensing element is simpler and easier to synthesize, making methane sensors easier to mass-produce and reducing production costs; 2. By utilizing a more sensitive array structure, utilizing four sensing elements and two set temperatures, and taking into account humidity, gas type, and gas concentration when acquiring the first signal from the calibration gas, the methane sensor system has been proven to be capable of detecting methane even in the presence of interfering gases such as acetic acid, ethanol, and formaldehyde. 3. It has been verified that the detection limit of methane gas has been further reduced to 100 ppb. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart for preparing semiconductor gas-sensitive materials according to Example 1 of the present application; Figure 2 This is a connection diagram of the methane electronic nose system provided in Example 1 of the present application when collecting raw data; Figure 3a Response curves of different semiconductor materials to different concentrations of methane in Example 1 of the present application; Figure 3b Response curves of different semiconductor materials to mixed gases of ethanol and methane of different concentrations in Example 1 of the present application; Figure 4 is a schematic diagram of the process of creating the second model and the first model provided in Example 1 of the present application; Figure 5 is the decision boundary diagram of Example 1 of the present application; Figure 6 This is the confusion matrix diagram of Example 1 of the present application; Figure 7 This is a schematic diagram of the actual concentration and predicted concentration of Example 1 of the present application; Figure 8 These are the response values ​​of the Pd2Au1-SnO2 sensing element in Example 1 of the present application to different concentrations of methane. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0020] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0021] Additionally, references throughout this specification to "one embodiment," "one embodiment," "an example," or similar language indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, appearances of the phrase "in one embodiment," "in one embodiment," and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0022] This application provides a semiconductor-based methane electronic nose system for detecting methane concentrations. The methane electronic nose system (i.e., methane sensor system) includes a central control device; two or more sensor element combinations connected to the central control device; the sensor element combinations include Pd-SnO2 sensing elements, Pd4Au1-SnO2 sensing elements, Pd2Au1-SnO2 sensing elements, and Pd1Au1-SnO2 sensing elements. Each set of the sensing element combination is provided with a temperature control device, so that each sensing element can operate at a set temperature and obtain a first signal corresponding to the gas to be measured, wherein the first signal is a resistance signal; in one embodiment, there are two sets of the sensing element combination, and the set temperature corresponding to the temperature control devices provided thereon is any temperature between 185°C and 195°C, and any temperature between 225°C and 235°C, such as 190°C and 230°C.

[0023] The central control device is used to obtain a second signal based on the first signal collected by each sensing element through feature extraction and normalization, and then obtain the concentration of methane in the gas to be measured based on the second signal and the second model, and obtain the type of the gas to be measured based on the second signal and the first model; the first signal is the response curve of the sensing element to the resistance of the gas to be measured at the set temperature, which is obtained by cleaning and smoothing the raw data; The second signal is eight or more of baseline resistance, minimum response, response time, recovery time, integral area of ​​response curve, integral area of ​​recovery curve, maximum derivative of response curve, minimum derivative of response curve, spectrum peak, center frequency, bandwidth, spectrum entropy, and spectrum kurtosis; the gas types include methane, ethanol, formaldehyde, acetic acid, a mixture of methane and ethanol, a mixture of methane and formaldehyde, and a mixture of methane and acetic acid.

[0024] The method of obtaining the second model and the first model is as follows: S1. Collecting raw calibration gas signals, cleaning and smoothing them to obtain a first signal, i.e., a resistance response curve for each calibration gas; then performing feature extraction and normalization to obtain a second signal; the calibration gas may be a single-component gas or a two-component gas; the single-component gas may include methane, ethanol, formaldehyde, and acetic acid; the two-component gas may include a mixture of methane and ethanol, a mixture of methane and formaldehyde, and a mixture of methane and acetic acid; each calibration gas may have at least two humidity levels, for example, the humidity may be set to 0% and 50%; each component of each gas type may have at least five or more concentration gradients; for a single-component gas, taking methane as an example, six or more concentration gradients from 50 ppm to 2000 ppm may be set; for a two-component gas, taking a mixture of methane and ethanol as an example, five or more concentration gradients may be set for each component (i.e., at least 5*5=25 gas concentrations); S2. The second signal of the calibration gas is normalized and combined with the corresponding methane concentration to establish a second model using support vector classifier (SVC) and support vector regression (SVR). Simultaneously, the second signal of the calibration gas is reduced in dimension using linear discriminant analysis (LDA) and combined with the corresponding gas type to establish a first model using support vector classifier (SVC).

[0025] The present application also provides an application of the above-mentioned methane electronic nose system in actual methane detection; when used to detect the gas to be measured, a response curve of the resistance of each sensing element to the gas to be measured at the same set temperature as in the above-mentioned step S2 at at least two different set temperatures is first obtained, and then a second signal of the gas to be measured is obtained; the second signal is then input into the second model and the first model respectively, and the concentration and type of the gas to be measured can be obtained.

[0026] The following contents are examples.

[0027] Example 1 Step 1: Synthesize semiconductor materials In this example, a simple hydrothermal method was used to synthesize SnO2 nanoflowers (the synthesis process is referred to the literature "Enhancedmethane sensing performance of SnO2nanoflowers based sensors decorated withAu nanoparticles"). Taking Pd4Au1-SnO2 as an example, Figure 1As shown in step a, first, mix 1.33 g of SnCl₄·5H₂O with 30 mL of 1.26 M NaOH solution and stir for 10 minutes. Then, add 30 mL of anhydrous ethanol to the reaction system and stir to obtain a white suspension. Next, transfer the resulting white solution to a 100 mL Teflon-lined stainless steel autoclave and store at 200°C for 24 hours.

[0028] The synthesized product after heat treatment is naturally cooled to room temperature. Figure 1 As shown in step b. Then, the white precipitate was washed with deionized water and ethanol several times, and dried at 60°C for 12 h to obtain the SnO2 precursor. Finally, the SnO2 nanoflower sample was calcined in air at 350°C for 2 h.

[0029] In this embodiment, PdAu-SnO2 nanoflowers with different Pd / Au atomic ratios were prepared by in situ ascorbic acid reduction method, and differentiated sensor arrays were constructed based on them. First, 100 mg of SnO2 nanoflower precursor powder was dispersed in 30 mL of deionized water and ultrasonicated for 5 minutes. Then, 100 μl of palladium chloride (PdCl2, 50 mM) and 25 μl of tetrachlorogold (III) trihydrate (HAuCl4·xH2O, 50 mM) solution were added respectively and stirred for 5 minutes. Next, 3 mL of ascorbic acid (C6H8O6, 100 mM) solution was added. After stirring at room temperature for 2.5 h, the resultant sample was washed several times with deionized water and ethanol, and dried at 60 ° C for 12 h. The synthesized sample was calcined at 350 ° C in air at a heating rate of 5 ° C / min for 2 h, that is, Figure 1 The conversion of step c and annealing were then carried out. The annealing was then carried out in H2 / Ar (1:10) at 250 °C and a heating rate of 5 °C / min for 2 h. Figure 1 The reduction of step d, thereby obtaining a purer product, finally obtains Figure 1 The PdAu-SnO2 nanoflower shown in step e, the small image in the upper left corner of the figure represents the nanocomposite of Pd and Au.

[0030] The same method can be used to synthesize Pd-SnO2, Pd2Au1-SnO2, and Pd1Au1-SnO2. The difference is that the volumes of PdCl2 and HAuCl4·xH2O added in step 1d are adjusted according to Table 1. By adjusting the proportion of Pd, sensing elements with different sensitivities to methane are obtained.

[0031] Table 1 Different parameters for sensor element preparation

[0032] After obtaining different semiconductor samples, different samples were made into TO-type sensors, and different methane concentrations were detected in the humidity range of 0% to 90% and the operating temperature range of 150℃ to 250℃, and different response results were obtained. In order to improve the detection accuracy of the electronic nose system in actual application environments. This embodiment selected Pd-SnO2, Pd4Au1-SnO2, Pd2Au1-SnO2, and Pd1Au1-SnO2 sensors with significant differences at 190℃ and 230℃, and constructed an array of eight sensors to form a methane electronic nose system, such as Figure 2 As shown, the vertical structures on the far left represent gas cylinders containing methane (CH4), ethanol (C2H5OH), formaldehyde (HCOH), acetic acid (CH3COOH), and air, which are connected to square flow meters through solenoid valves. A computer is connected to the control end of each square flow meter to set the flow rate of each gas. At the same time, the computer is connected to a multi-channel test system. A knob for adjusting the voltage is provided below the multi-channel test system. By adjusting the voltage, the temperature control device provided on the gas chamber (sensor array) can be adjusted. The computer can control the composition of the gas in the gas chamber and the working temperature of the sensor array by controlling the flow meter and the multi-channel test system, while the humidity generating device can regulate the humidity of the gas. The entire device is used to collect original data for testing and classification research on methane, ethanol, formaldehyde, acetic acid and their mixed gases at different concentrations at different humidities.

[0033] Arrays of different semiconductor gas-sensing materials combined with different operating temperatures can amplify differences in response to different gases, resolving inaccurate identification caused by cross-interference. Signal characteristics of semiconductor gas sensors primarily include response to the target gas, integrated area, and response / recovery time. These are the primary basis for gas identification and reflect material properties.

[0034] Step 2: Collect raw data First, the raw data of the sensor is collected. This example includes the raw data of 228 gas samples. For the convenience of description, A, B, C, and D represent methane, ethanol, formaldehyde, and acetic acid, respectively. The numbers after the letters represent the concentration of the corresponding gas. At the same time, we set two humidity levels of 0% and 50%, represented by Roman numerals I and II respectively. For example, a gas containing 50ppm methane, 1ppm ethanol, and a humidity of 50% can be represented as A50B1II. Among them, the gas samples of the original data include 48 single-component gas samples, namely, methane gas with different humidity and concentrations of 50ppm, 100ppm, 200ppm, 500ppm, 1000ppm, and 2000ppm; ethanol, formaldehyde, and acetic acid gases with different humidity and concentrations of 1ppm, 2ppm, 5ppm, 10ppm, 20ppm, and 50ppm; and 180 mixed-component gas samples, taking the mixed gas of methane and ethanol as an example, namely, A50B1, A100B1, A200B1, A500B1, A1000B1, A2000B1, A50B2, A100B2, A200B2, A500B2, A1000B2, A2000B2, A50B1, A100B5, A200B5, A500B5, A1000B5 There are 60 types in total, including B5, A2000 B5, A50B10, A100 B10, A200 B10, A500 B10, A1000B10, A2000 B10, A50B20, A100 B20, A200 B20, A500 B20, A1000 B20, and A2000 B20 (since each sample has two humidity levels, the Roman numerals are omitted); the mixing method for the mixture of methane and other gases is the same, except that the ethanol gas is replaced with formaldehyde and acetic acid gas of the same concentration.

[0035] Each gas sample is sampled (sampling frequency f s =2Hz, the response curve time is about 900 seconds, the recovery curve response time is about 1800 seconds, T total =2700 s). The concentrations in the experiment must be designed to maintain an arithmetic progression. Due to factors such as long acquisition time and hardware instability, data loss may occur. Linear interpolation is used to supplement missing data.

[0036] : Missing data points The data point before the missing data point The data point after the missing data point k: data points to be smoothed N: average smoothing radius : Sample mean : Sample standard deviation The value after W: projection matrix (8) w: weight vector (9, 10, 11) b: bias coefficient ||w||: represents the L2 norm of the weight vector w : Error tolerance : slack variable for the i-th sample express x The corresponding data, x Indicates the number of points, represents the inserted data point, represents the smoothed data, represents the total sampling time of one sample, n Represents the nth data point represents the baseline resistance, Indicates the i The resistance value corresponding to each point is It means to find the minimum value. It means to find the maximum value. s represents the sampling frequency, Indicates finding the input value corresponding to the minimum value of the function, represents the derivative value at the i-th data point, represents the jth feature of the i-th sample, represents the transpose of the matrix, represents the category label of the i-th sample, As shown in formula 1:

[0037] After obtaining the complete original data, the average smoothing method is used to select the original data points before and after the The average value of the data points is used to replace the original data points to obtain smoothed data. The obtained smoothed data is shown in Figure 3, where Figure 3a The following graph shows the response curves of different semiconductor materials to methane concentrations of 50 ppm, 100 ppm, 200 ppm, 500 ppm, 1000 ppm, and 2000 ppm at 0% humidity (i.e., the gas numbers are A50I, A100I, A200I, A500I, A1000I, and A2000I, respectively). It can be seen from the graph that the greater the methane concentration, the smaller the resistance drop and the steeper the drop. Figure 3b The response curves of different semiconductor materials to mixed gases with different concentrations of methane and 1 ppm ethanol at 0% humidity (i.e., the gas numbers are A50B1I, A100B1I, A200B1I, A500B1I, A1000B1I, and A2000B1I), and the trends are shown in Figure 2. Figure 3a Same as formula 2:

[0038] Step 3: Feature extraction Next, we extract features from the smoothed data. The extracted features and their corresponding calculation methods are shown in Table 2 below.

[0039] Table 2 Extracted features and their calculation methods

[0040] After obtaining a 228*(8*8) feature matrix (228 samples, 8 sensors, 8 features), due to the different scales between features, a normalization method is used to unify the data scale, such as Equation 3 (calculating the feature mean), 4 (calculating the standard deviation), and 5 (normalization):

[0041]

[0042]

[0043] Step 4: Build the model This concludes the data preprocessing part. Next is the introduction of the machine learning algorithm. This application uses machine learning algorithms to extract the characteristics of the sensor array under different atmospheres, and uses these characteristics to establish classification and concentration regression models, such as Figure 4 As shown, the model specifically includes a classification model (i.e., the first model) and a concentration regression model (i.e., the second model).

[0044] First, this application implements the basic gas classification, which is the 7 types of organic gases mentioned above (methane, ethanol, formaldehyde, acetic acid, methane + ethanol, methane + formaldehyde, methane + acetic acid). First, the linear discriminant analysis algorithm, namely LDA dimensionality reduction, is used to reduce the 64 features corresponding to the 228 samples to 2 dimensions in a supervised manner. LDA constructs two scatter matrix: (1) between-class matrix (S B ), calculate the distance between the means of each class; (2) the intra-class matrix (S W ), returns the distance between the mean of each class and the data within the class. The calculation formulas are shown in Equations 6 and 7 respectively. Finally, the optimal linear projection matrix is ​​obtained by LDA using Equation 8:

[0045]

[0046]

[0047] where k is the number of categories, is a category The mean vector of is the mean vector of the entire data, is a category The number of samples, is a category A collection of .

[0048] The dimensionality-reduced data was then fed into a support vector classifier (SVC) for training. The SVC trained the model to recognize seven types of gases and obtained a classification model. After cross-validation, the model achieved an accuracy of 97.10% for the seven classifications. The support vector decision boundary (SVC) was plotted based on the support vectors. Figure 5 ), whose horizontal and vertical coordinates are LDA1 and LDA2, which are the two dimensions of linear discriminant analysis. Figure 5 The results show that, except for two samples that were misjudged, most of the points representing each gas sample were segmented into different regions with obvious separation; Figure 6 The confusion matrix shows that one methane sample was classified as a mixture of methane and ethanol, and one methane and ethanol mixture was classified as acetic acid (marked by the red circle). This is consistent with the results of the decision boundary diagram. This demonstrates that the PdAu-SnO2 gas sensor array has good selectivity for the seven selected gas types.

[0049] Next, we explore the effectiveness of concentration regression. In order to unify the feature scales of classification and regression, the 64 features before dimensionality reduction are directly input into the SVC and support vector regression (SVR) models for training, and classification and regression models are established. They are both based on the support vector machine (SVM) method, and both find an optimal hyperplane in the feature space. SVC separates the categories and maximizes the interval between the data points on both sides of the hyperplane; SVR minimizes the error between the predicted value and the actual value, and finds a hyperplane that makes most of the data points fall on a width of The function in the regression band is the concentration regression model. Equation 9 is the objective function, Equation 10 is the SVC constraint condition, and Equation 11 is the SVR constraint condition:

[0050]

[0051]

[0052] in, is the hyperplane normal vector, is the sample number, for Sample characteristics, is the sample label, is the hyperplane bias term, is the sample size, is the relaxation coefficient, is the hyperplane margin.

[0053] After training, we get a higher classification accuracy (92.22%) and regression coefficient (0.9434), and a scatter plot of actual concentration and predicted concentration was drawn ( Figure 7 ), as can be seen from the figure, the actual methane concentration is well distributed on both sides of the regression curve close to the predicted concentration. These results demonstrate the excellent selectivity of the PdAu-SnO2 material sensor array for detecting gases.

[0054] At 230°C and 50% relative humidity, the response to 500 ppm methane is 5.93, with a detection limit as low as 100 ppb, such as Figure 8 As shown, where x is the methane concentration, y is the response value, and R 2 is the regression coefficient of the fitting curve, and LOD is the theoretical detection limit of the fitting curve.

[0055] Comparative Example 1 The experiment was repeated using the same steps as in Example 1, with the following differences: the sensing element combination only contained a single Pd-SnO2 material, and the temperature control device was set to a fixed temperature of 230°C. No multi-temperature combination strategy was adopted, so the raw data collected corresponded to only 1 / 8 of the sensors in Example 1. Only a single humidity (50%) was used for raw data collection, and no mixed gas samples were included (only pure methane gas was tested). The results showed that in the presence of interfering gases such as ethanol and formaldehyde, the false detection rate was significantly improved, and the classification accuracy dropped to 78.125%. The R of the methane concentration regression was 0.04477 / 0.030. 2 The coefficient is only 0.712, the detection limit is 500 ppb, and the sensitivity is greatly reduced. Due to the lack of multi-temperature and multi-material synergy, the sensor has no response to low concentrations of methane (<200 ppb).

[0056] Comparative Example 2 The experiment was repeated using the same steps as in Example 1, except that in step 1, only Pd-SnO2 and unmodified SnO2 were used instead of the Pd-SnO2, Pd4Au1-SnO2, Pd2Au1-SnO2, and Pd1Au1-SnO2 sensing element combinations, without Au modification, and the temperature control device was set to a single temperature of 230°C. Therefore, the raw data collected corresponded to only one-fourth of the sensors in Example 1. The results showed that the classification accuracy for the mixed gas was only 80.6%. The confusion matrix showed that the misclassification rate for the methane and ethanol mixed gas samples was as high as 40%, and the predicted methane concentration deviated significantly from the actual value (R 2=0.621), with a detection limit of 1 ppm, failing to achieve a sensitivity of 100 ppb. The sensor exhibited poor stability during long-term testing, with significant drift in the response curve and a recovery time extended by over 30%. This demonstrates that the present embodiment enhances material selectivity by optimizing the palladium-gold modification ratio and heating temperature.

[0057] It is easy for those skilled in the art to understand that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A methane electronic nose system based on semiconductor materials, characterized in that: It includes a central control device; two or more sets of sensing element combinations respectively connected to the central control device; the sensing element combination includes a Pd-SnO2 sensing element, a Pd4Au1-SnO2 sensing element, a Pd2Au1-SnO2 sensing element and a Pd1Au1-SnO2 sensing element; Each set of the sensing element assembly is provided with a temperature control device, so that each sensing element can operate at a set temperature and obtain a second signal corresponding to the gas to be measured; the second signal is eight or more parameters selected from the group consisting of baseline resistance, minimum response, response time, recovery time, integral area of ​​the response curve, integral area of ​​the recovery curve, maximum derivative of the response curve, minimum derivative of the response curve, spectrum peak, center frequency, bandwidth, spectrum entropy, and spectrum kurtosis; The central control device is used to obtain the concentration of methane in the gas to be measured based on the second signal collected by each sensing element and the second model.

2. The methane electronic nose system according to claim 1, wherein: The set temperature of the temperature control device includes a first temperature and a second temperature, the first temperature is between 185°C and 195°C, and the second temperature is between 225°C and 235°C.

3. The methane electronic nose system according to claim 1, wherein: The second signal is obtained by extracting and normalizing the first signal, and the first signal is a response curve of the sensing element to the resistance of the gas to be measured at the set temperature.

4. The methane electronic nose system according to claim 3, wherein: The second signal is baseline resistance, minimum response, response time, recovery time, integrated area of ​​response curve, integrated area of ​​recovery curve, maximum derivative of response curve and minimum derivative of response curve.

5. The methane electronic nose system according to claim 1, wherein: The method for obtaining the second model is: S21 corresponds to at least six different methane concentration calibration gas as the test gas, collecting the second signal corresponding to the calibration gas; S22. After normalizing the second signal in step S21, the second signal is trained with the corresponding methane concentration in the calibration gas in a support vector machine to obtain the second model.

6. The methane electronic nose system according to claim 5, characterized in that: In step S21 , each calibration gas with a different methane concentration corresponds to at least two different humidity levels.

7. The methane electronic nose system according to claim 1, wherein: The central control device is further configured to obtain the gas type of the gas to be measured based on the second signal collected by each sensing element and the first model.

8. The methane electronic nose system according to claim 7, wherein: The gas types include methane, ethanol, formaldehyde, acetic acid, a mixed gas of methane and ethanol, a mixed gas of methane and formaldehyde, and a mixed gas of methane and acetic acid.

9. The methane electronic nose system according to claim 8, wherein: The method for obtaining the first model comprises the following steps: S11. The different gas types, each gas type corresponds to at least six different concentrations of calibration gas as the test gas, collecting the second signal corresponding to the calibration gas; S12. After reducing the dimension of the second signal in step S11 through linear discriminant analysis, training is performed in a support vector classifier according to the gas type of the calibration gas corresponding to the second signal to obtain the first model.

10. Use of the methane electronic nose system according to any one of claims 1 to 9 in methane detection.

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