Methanol battery platinum-based catalyst screening method based on multi-feature fusion

Abstract

The invention discloses a methanol cell platinum-based catalyst screening method based on multi-feature fusion, and relates to the technical field of graphene and carbon-based electro-catalytic materials.According to the method, under the working condition of a methanol fuel cell anode, structural interface features, electrochemical behavior features and stability tolerance feature data of a platinum-based catalyst are synchronously collected; a corresponding data set is constructed after preprocessing; layered screening of catalyst interface electron transmission capability, intermediate reaction regulation capability and structural stability is realized by calculating an interface electron cooperation coefficient, an intermediate regulation conflict coefficient and a structural evolution tolerance coefficient and comparing with a preset threshold value or a threshold value interval; and in combination with a multi-dimensional evaluation result, targeted mechanism analysis is performed on unqualified samples, and feedback optimization design is performed, so that the scientificity, accuracy and engineering applicability of methanol battery platinum-based catalyst screening are improved.

Description

Technical Field

[0001] This invention relates to the field of graphene and carbon-based electrocatalytic materials technology, specifically a method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion. Background Technology

[0002] Direct methanol fuel cells (DMFCs) have broad application prospects in portable power sources and distributed energy fields due to their advantages such as high energy density, low operating temperature, and convenient fuel storage and transportation. Among these, the platinum-based anode catalyst is a key material determining the efficiency of the methanol electro-oxidation reaction and the overall performance of the battery. Its catalytic activity, resistance to poisoning, and long-term operational stability directly affect the output performance and service life of the fuel cell.

[0003] In existing technologies, platinum-based catalysts typically use graphene, carbon black, or other carbon materials as supports, and their electrocatalytic performance is improved by controlling the platinum particle size, loading, and support structure. However, the following prominent problems still exist in the actual research and screening process: On the one hand, existing catalyst screening methods mostly rely on a single or a few performance indicators, such as initial peak current density or specific surface area, which are difficult to fully reflect the comprehensive performance of the platinum-carbon support system in terms of interfacial electron transport, intermediate regulation and long-term structural stability. This can easily lead to catalysts that perform well in the early stages but degrade rapidly during service being misjudged as preferred samples.

[0004] On the other hand, nonlinear coupling and mutual constraints are prevalent among various key performance indicators of platinum-based catalysts. For example, improving catalytic activity is often accompanied by enhanced intermediate adsorption, thereby causing poisoning of active sites; while reducing platinum particle size is beneficial for increasing specific surface area, it may exacerbate particle migration and sintering risks; and it is also difficult to balance the conductivity and structural stability of carbonaceous supports. Existing technologies typically address these issues in a fragmented manner, lacking a unified comprehensive evaluation mechanism, making it difficult to effectively identify potential conflict risks during the screening stage.

[0005] In addition, the experimental data sources in the existing screening process are scattered, involving a variety of testing methods such as physical structure characterization, electrochemical performance testing and stability assessment. The different testing conditions, time scales and data dimensions vary greatly, resulting in insufficient comparability between data, making it difficult to form a unified decision-making basis, and increasing the uncertainty of manual judgment and screening costs.

[0006] Meanwhile, in the development of high-throughput platinum-based catalysts, existing technologies generally lack a phased, early-term termination mechanism for rapid elimination. Often, it is necessary to complete all testing procedures before the quality of samples can be judged, which not only reduces screening efficiency but also wastes a lot of testing resources, making it difficult to meet the needs of engineering applications for rapid iteration and accurate screening. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a screening method for platinum-based catalysts for methanol batteries based on multi-feature fusion, thereby solving the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a screening method for platinum-based catalysts for methanol batteries based on multi-feature fusion, comprising the following steps: Step 1: Simultaneously test platinum-based catalyst candidate samples with graphene and carbon materials as carbon supports under methanol fuel cell anode test conditions, and collect support and interface basic parameter data, electrochemical behavior parameter data, and stability tolerance parameter data; after preprocessing, form structural interface feature dataset, electrochemical behavior feature dataset, and stability tolerance feature dataset. Step 2: Based on the structural interface feature dataset and the electrochemical behavior feature dataset, calculate and obtain the interface electronic synergy coefficient JDX, and compare it with the interface synergy threshold Jth to determine whether the interface electronic synergy is qualified. If qualified, the platinum-based catalyst sample is marked to generate a qualified sample set of interface electronic synergy; if unqualified, the current sample is marked as an unqualified sample of interface electronic synergy. Step 3: Based on the electrochemical behavior characteristic dataset and the stability tolerance characteristic dataset, calculate and obtain the intermediate regulation conflict coefficient ZTCX, and compare it with the intermediate conflict threshold range. The comparison is performed to determine whether the intermediate regulation ability is qualified; if qualified, the platinum-based catalyst sample is marked to generate a qualified intermediate regulation sample set; if unqualified, the current sample is marked as an unqualified intermediate regulation conflict sample. Step 4: Based on the structural interface feature dataset and the stability tolerance feature dataset, calculate and obtain the structural evolution tolerance coefficient YNX, and compare it with the structural tolerance threshold Yth to determine whether the current sample's structural evolution tolerance is qualified. If qualified, the platinum-based catalyst sample is marked to generate a candidate catalyst set for methanol battery engineering applications; if unqualified, the current sample is marked as a sample with unqualified structural evolution tolerance. Step 5: Based on the catalyst samples marked as unqualified in the evaluation results, targeted analysis is carried out from three dimensions: interfacial electron transport, intermediate reaction regulation, and structural evolution tolerance. By correlating key electrical parameters, electrochemical performance indicators, and structural evolution characteristics, the dominant influencing factors that limit performance are identified, and the analysis results are fed back to the catalyst support structure, platinum loading mode, and composition design and screening stages for optimization and adjustment.

[0009] Preferably, step one includes: S11. By real-time monitoring of the anode test conditions of methanol fuel cells, multi-source basic data collection is performed on platinum-based catalyst candidate samples with graphene and carbon materials as carbon supports; by simultaneously monitoring the support structural state, interfacial electronic behavior, and service stability of platinum-based catalysts during the methanol electrocatalytic reaction, basic support and interface parameter data, electrochemical behavior parameter data, and stability tolerance parameter data are collected, specifically through the following steps: S111. The specific surface area (Sc) of the carbonaceous support was collected by performing nitrogen adsorption-desorption tests on the platinum-based catalyst sample in the sample testing chamber; the conductivity of the carbonaceous support was collected by setting up a four-probe testing device on the carbonaceous support tablet sample in the sample testing chamber. The average particle size Dpt of platinum particles on the carbonaceous support was collected by imaging the platinum-based catalyst sample using a transmission electron microscope in the sample testing chamber; the dispersion Vpt of the platinum particle size distribution was collected by statistically analyzing the size of platinum particles in the transmission electron microscope images. S112. Cyclic voltammetry tests were performed on the platinum-based catalyst electrode on a methanol fuel cell electrochemical testing platform to collect the peak current density Im of the methanol oxidation reaction; the position of the oxidation peak in the cyclic voltammetry test curve was recorded to collect the offset of the methanol oxidation peak potential relative to the reference potential. The charge transfer resistance Rct at the electrode interface was collected by performing electrochemical impedance spectroscopy on the platinum-based catalyst electrode on the same testing platform. S113. By conducting continuous operation tests on the platinum-based catalyst electrode under constant potential conditions, the current retention rate during the test cycle is collected. The onset potential (ECO) of the CO oxidation reaction was acquired by performing CO stripping voltammetry on the platinum-based catalyst electrode on the test platform. The average particle size change rate of platinum particles was compared by performing microstructural imaging on the platinum-based catalyst sample before and after accelerated aging tests. .

[0010] Preferably, step one further includes: S12, based on the specific surface area Sc and conductivity of the carbonaceous carrier. The average particle size (Dpt) and dispersion (Vpt) of platinum particles were processed using a dimensionless normalization method to unify the scale of structural and interface parameters with different physical dimensions. Furthermore, the time consistency correction method was used to align and correct repeated test data using timestamp information from multiple test results, eliminating the influence of test batch differences on parameter values. Outlier suppression and outlier removal methods were employed to process data that significantly deviated from the statistical range, obtaining a set of basic standard parameters for the carrier and interface, and establishing a structural interface feature dataset. S13, based on the acquired peak current density Im and offset In addition to charge transfer resistance (Rct) data, a multi-parameter normalization mapping method was used to perform dimensionless transformation of electrochemical behavior parameters under different test dimensions; and based on the time series information of cyclic voltammetry and electrochemical impedance spectroscopy, a test stage consistency correction method was used to eliminate the influence of different test stages and environmental fluctuations on parameter comparability; and a statistical interval constraint method was used to suppress abnormal fluctuation data, obtain a standard parameter set of electrochemical behavior, and establish an electrochemical behavior feature dataset. S14, Current holding rate based on data acquisition Initiation potential (ECO) and average particle size change rate of platinum particles The data were processed using normalization and scaling methods to unify the dimensions of stability parameters obtained under long-term operation and accelerated aging conditions. Time series data from continuous operation tests were processed using time consistency and trend smoothing methods to reduce the impact of instantaneous fluctuations on stability parameters. Stability interval screening methods were used to constrain abnormal decay or mutation data to obtain a standard parameter set for structural evolution and tolerance, and a stability tolerance feature dataset was established.

[0011] Preferably, step two includes: S21. Extract the processed conductivity using the structural interface feature dataset and the electrochemical behavior feature dataset. The interface electronic cooperation coefficient JDX was calculated using data on dispersion Vpt and charge transfer resistance Rct.

[0012] Preferably, step two further includes: S22. By setting a preset interface collaboration threshold Jth, and comparing and analyzing the interface electronic collaboration coefficient JDX with the interface collaboration threshold Jth, the first evaluation results are obtained, including: When the interfacial electronic synergy coefficient JDX ≥ interfacial synergy threshold Jth, it indicates that the interfacial electronic synergy is qualified, and the first qualified label is generated. The platinum-based catalyst sample is then marked to generate a set of qualified samples for interfacial electronic synergy. When the interfacial electronic synergy coefficient JDX < the interfacial synergy threshold Jth, it indicates that the interfacial electronic synergy is unqualified. The platinum-based catalyst sample has problems with discontinuous electron transport or abnormal interfacial impedance at the interface between platinum and carbonaceous support, which leads to the risk of reduced electron migration efficiency and limited electron supply in the methanol oxidation reaction. This triggers the first warning instruction and generates the first strategy: mark the current sample as a sample with unqualified interfacial electronic synergy and terminate the screening process.

[0013] Preferably, step three includes: S31. Based on the qualified sample set of interface electronic collaboration, extract the corresponding peak current density Im and offset from the electrochemical behavior feature dataset. By combining the initial potential ECO in the stability tolerance feature dataset, the intermediate regulation conflict coefficient ZTCX is calculated and obtained.

[0014] Preferably, step three further includes: S32, via a preset intermediate conflict threshold range And the intermediate regulation conflict coefficient ZTCX and the intermediate conflict threshold range are used. A comparative analysis was conducted to obtain the second evaluation results, including: When the intermediate regulation conflict coefficient ZTCX ∈ the intermediate conflict threshold range When the time is right, it indicates that the catalytic activity and anti-poisoning performance of the current platinum-based catalyst sample are in a controllable and coordinated state, and the intermediate regulation ability is qualified; generate a second qualified label to mark the platinum-based catalyst sample and generate a qualified sample set of intermediate regulation; When the intermediate modulates the conflict coefficient ZTCX Intermediate conflict threshold range When the signal is triggered, it indicates that the catalytic activity and anti-poisoning performance of the current platinum-based catalyst sample are in an uncontrolled conflict state, the intermediate regulation ability is unqualified, and there is a risk of active site poisoning, reaction kinetic limitation or performance degradation. This triggers a second warning instruction and generates a second strategy: mark the current sample as an unqualified sample with intermediate regulation conflict and terminate the screening process.

[0015] Preferably, step four includes: S41. Based on the qualified sample set regulated by intermediates, combined with the specific surface area Sc of the corresponding carbonaceous support in the structural interface feature dataset, and the current retention rate in the stability tolerance feature dataset. Average particle size variation rate of platinum particles The structural evolution tolerance coefficient YNX is calculated and obtained.

[0016] Preferably, step four further includes: S42. By setting a pre-defined structural tolerance threshold Yth, and comparing the structural evolution tolerance coefficient YNX with the structural tolerance threshold Yth, the third evaluation results are obtained, including: When the structural evolution tolerance coefficient YNX ≥ the structural tolerance threshold Yth, it means that the current sample meets the engineering application requirements in terms of activity retention, structural stability and support tolerance, and is judged as a qualified sample in terms of structural evolution tolerance. A third qualified label is generated to mark the platinum-based catalyst sample and generate a set of candidate catalysts for methanol battery engineering applications. When the structural evolution tolerance coefficient YNX < structural tolerance threshold Yth, it indicates that the current sample does not meet the engineering application requirements in terms of activity retention, structural stability, or carrier tolerance. It is judged as a sample with unqualified structural evolution tolerance, triggering the third warning instruction and generating the third strategy: marking the current sample as a sample with unqualified structural evolution tolerance and terminating the screening process.

[0017] Preferably, step five includes: S51. Based on samples marked as unqualified for interfacial electronic synergy, the overall conductivity and charge transfer resistance data of the samples are analyzed in conjunction with the dispersion of platinum particle size distribution during the interfacial electronic synergy test. The correspondence between the limited interfacial electron transport and the conductive structure of the support and the platinum loading state is identified and recorded. The construction method of the carbonaceous support conductive network and the loading configuration of platinum particles are optimized and adjusted in the catalyst design stage. S52. Based on samples marked as unqualified due to intermediate regulation conflict, the electrochemical performance data were analyzed during the intermediate regulation evaluation process using methanol oxidation peak current density, peak potential shift, and CO oxidation onset potential data. The conflict between catalytic activity enhancement and intermediate inhibition ability was determined, and performance response characteristics related to the electronic state of platinum active sites and the chemical environment of the support surface were extracted. These performance response characteristics were recorded and used to optimize and adjust the catalyst composition design or structural design stage. S53. Based on samples marked as unqualified for structural evolution tolerance, multi-scale structural characterization of the carbonaceous support was performed before and after the structural evolution tolerance test. The obtained structural evolution data was time-aligned and jointly modeled with the current retention rate time series and platinum particle size change data collected during constant potential aging. Multivariate correlation analysis was used to extract the coupled response characteristics of the structural changes of the carbonaceous support to the stability of platinum particle size and the decay behavior of electrochemical performance. The coupled response characteristics were recorded to optimize and adjust the catalyst design and screening strategy.

[0018] This invention provides a method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion. It has the following beneficial effects: (1) The screening method for platinum-based catalysts for methanol batteries based on multi-feature fusion simultaneously collects structural interface parameters, electrochemical behavior parameters and stability tolerance parameters, and normalizes, corrects time consistency and suppresses anomalies in data of different dimensions and multiple test stages to construct a unified multi-feature dataset. This enables the performance of catalysts under different carbonaceous supports and platinum loading states to have a quantifiable and comparable evaluation basis, avoiding the one-sidedness of traditional single-index screening.

[0019] (2) The screening method for platinum-based catalysts for methanol batteries based on multi-feature fusion constructs the interface electronic synergy coefficient JDX, the intermediate regulation conflict coefficient ZTCX, and the structural evolution tolerance coefficient YNX. It stratifies and judges the interface electronic transport efficiency, methanol oxidation activity and anti-poisoning ability balance and long-term structural stability. It can accurately identify the key links that limit performance and overcome the problem of "high activity but poor stability" or "strong anti-poisoning but limited kinetics" in the existing technology.

[0020] (3) The screening method for platinum-based catalysts for methanol batteries based on multi-feature fusion generates clear performance labels for unqualified samples during the screening process, and conducts targeted analysis from three dimensions: interfacial electron transport, intermediate reaction regulation and structural evolution tolerance. The analysis results are directly fed back to the construction method of carbonaceous support conductive network, platinum particle loading configuration and composition design, forming a closed-loop process of "test-evaluation-feedback-optimization", which significantly reduces blind trial and error in catalyst development.

[0021] (4) The screening method for platinum-based catalysts for methanol batteries based on multi-feature fusion introduces durability indicators such as constant potential aging, current retention rate and platinum particle size evolution in the screening process, and performs joint modeling with structural parameters. This invention not only focuses on initial catalytic activity, but also focuses on evaluating performance degradation and structural stability under long-term operation, so that the catalysts finally screened are more in line with the actual requirements of reliability and lifespan for methanol fuel cell engineering applications. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the steps in the screening method for platinum-based catalysts for methanol batteries based on multi-feature fusion, as described in this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1 Please see Figure 1 This invention provides a method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion, comprising the following steps: Step 1: Simultaneously test platinum-based catalyst candidate samples with graphene and carbon materials as carbon supports under methanol fuel cell anode test conditions, and collect support and interface basic parameter data, electrochemical behavior parameter data, and stability tolerance parameter data; after preprocessing, form structural interface feature dataset, electrochemical behavior feature dataset, and stability tolerance feature dataset. Step 2: Based on the structural interface feature dataset and the electrochemical behavior feature dataset, calculate and obtain the interface electronic synergy coefficient JDX, and compare it with the interface synergy threshold Jth to determine whether the interface electronic synergy is qualified. If qualified, the platinum-based catalyst sample is marked to generate a qualified sample set of interface electronic synergy; if unqualified, the current sample is marked as an unqualified sample of interface electronic synergy. Step 3: Based on the electrochemical behavior characteristic dataset and the stability tolerance characteristic dataset, calculate and obtain the intermediate regulation conflict coefficient ZTCX, and compare it with the intermediate conflict threshold range. The comparison is performed to determine whether the intermediate regulation ability is qualified; if qualified, the platinum-based catalyst sample is marked to generate a qualified intermediate regulation sample set; if unqualified, the current sample is marked as an unqualified intermediate regulation conflict sample. Step 4: Based on the structural interface feature dataset and the stability tolerance feature dataset, calculate and obtain the structural evolution tolerance coefficient YNX, and compare it with the structural tolerance threshold Yth to determine whether the current sample's structural evolution tolerance is qualified. If qualified, the platinum-based catalyst sample is marked to generate a candidate catalyst set for methanol battery engineering applications; if unqualified, the current sample is marked as a sample with unqualified structural evolution tolerance. Step 5: Based on the catalyst samples marked as unqualified in the evaluation results, targeted analysis is carried out from three dimensions: interfacial electron transport, intermediate reaction regulation, and structural evolution tolerance. By correlating key electrical parameters, electrochemical performance indicators, and structural evolution characteristics, the dominant influencing factors that limit performance are identified, and the analysis results are fed back to the catalyst support structure, platinum loading mode, and composition design and screening stages for optimization and adjustment.

[0025] In this embodiment, by sequentially constructing the interface electronic synergy coefficient, intermediate regulation conflict coefficient, and structural evolution tolerance coefficient, platinum-based catalysts are screened and judged in three key dimensions: electron transport efficiency, reaction intermediate regulation capability, and long-term structural stability. The performance limiting factors of unqualified samples are fed back to the support structure and platinum loading design, realizing a closed-loop linkage between catalyst screening and structural optimization. This effectively improves the accuracy, engineering applicability, and R&D efficiency of the screening results of platinum-based catalysts for methanol fuel cells.

[0026] Example 2 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, step one includes: S11. By real-time monitoring of the anode test conditions of methanol fuel cells, multi-source basic data collection is performed on platinum-based catalyst candidate samples with graphene and carbon materials as carbon supports; by simultaneously monitoring the support structural state, interfacial electronic behavior, and service stability of platinum-based catalysts during the methanol electrocatalytic reaction, basic support and interface parameter data, electrochemical behavior parameter data, and stability tolerance parameter data are collected, specifically through the following steps: S111. The specific surface area (Sc) of the carbonaceous support was collected by performing nitrogen adsorption-desorption tests on the platinum-based catalyst sample in the sample testing chamber; the conductivity of the carbonaceous support was collected by setting up a four-probe testing device on the carbonaceous support tablet sample in the sample testing chamber. The average particle size Dpt of platinum particles on the carbonaceous support was collected by imaging the platinum-based catalyst sample using a transmission electron microscope in the sample testing chamber; the dispersion Vpt of the platinum particle size distribution was collected by statistically analyzing the size of platinum particles in the transmission electron microscope images. S112. Cyclic voltammetry tests were performed on the platinum-based catalyst electrode on a methanol fuel cell electrochemical testing platform to collect the peak current density Im of the methanol oxidation reaction; the position of the oxidation peak in the cyclic voltammetry test curve was recorded to collect the offset of the methanol oxidation peak potential relative to the reference potential. The charge transfer resistance Rct at the electrode interface was collected by performing electrochemical impedance spectroscopy on the platinum-based catalyst electrode on the same testing platform. S113. By conducting continuous operation tests on the platinum-based catalyst electrode under constant potential conditions, the current retention rate during the test cycle is collected. The onset potential (ECO) of the CO oxidation reaction was acquired by performing CO stripping voltammetry on the platinum-based catalyst electrode on the test platform. The average particle size change rate of platinum particles was compared by performing microstructural imaging on the platinum-based catalyst sample before and after accelerated aging tests. .

[0027] In this embodiment, by simultaneously and systematically collecting multi-source data on the support structure parameters, interfacial electronic behavior parameters, and service stability parameters of platinum-based catalyst samples under methanol fuel cell anode conditions, the intrinsic correlation between platinum particle loading status, interfacial charge transport characteristics, and operational degradation behavior can be comprehensively reflected within the same test system. This avoids evaluation bias caused by parameter fragmentation under single test conditions, thereby improving the completeness of platinum-based catalyst performance characterization and the reliability of screening results.

[0028] Example 3 This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically, step one also includes: S12, based on the specific surface area Sc and conductivity of the carbonaceous carrier. The average particle size (Dpt) and dispersion (Vpt) of platinum particles were processed using a dimensionless normalization method to unify the scale of structural and interface parameters with different physical dimensions. Furthermore, the time consistency correction method was used to align and correct repeated test data using timestamp information from multiple test results, eliminating the influence of test batch differences on parameter values. Outlier suppression and outlier removal methods were employed to process data that significantly deviated from the statistical range, obtaining a set of basic standard parameters for the carrier and interface, and establishing a structural interface feature dataset. S13, based on the acquired peak current density Im and offset In addition to charge transfer resistance (Rct) data, a multi-parameter normalization mapping method was used to perform dimensionless transformation of electrochemical behavior parameters under different test dimensions; and based on the time series information of cyclic voltammetry and electrochemical impedance spectroscopy, a test stage consistency correction method was used to eliminate the influence of different test stages and environmental fluctuations on parameter comparability; and a statistical interval constraint method was used to suppress abnormal fluctuation data, obtain a standard parameter set of electrochemical behavior, and establish an electrochemical behavior feature dataset. S14, Current holding rate based on data acquisition Initiation potential (ECO) and average particle size change rate of platinum particles The data were processed using normalization and scaling methods to unify the dimensions of stability parameters obtained under long-term operation and accelerated aging conditions. Time series data from continuous operation tests were processed using time consistency and trend smoothing methods to reduce the impact of instantaneous fluctuations on stability parameters. Stability interval screening methods were used to constrain abnormal decay or mutation data to obtain a standard parameter set for structural evolution and tolerance, and a stability tolerance feature dataset was established.

[0029] In this embodiment, by implementing dimensionless normalization, time consistency correction, and outlier data constraint processing on structural interface parameters, electrochemical behavior parameters, and stability tolerance parameters, a unified expression and comparable analysis of multi-source data under different test dimensions, test stages, and operating conditions were achieved. This effectively reduced the impact of test batch differences and instantaneous fluctuations on the evaluation results, thereby constructing a stable and standardized feature dataset, providing a reliable data foundation for subsequent multi-feature fusion evaluation and catalyst performance determination.

[0030] Example 4 This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically, step two includes: S21. Extract the processed conductivity using the structural interface feature dataset and the electrochemical behavior feature dataset. Based on the discreteness Vpt and charge transfer resistance Rct data, the interfacial electronic cooperation coefficient JDX is calculated using the following formula:

[0031] In the formula, w1, w2 and w3 represent weighting coefficients.

[0032] Characterizing the overall electrical conductivity of carbonaceous supports The impact on interfacial electronic synergy has the highest weight and is the core indicator of interfacial electron transport capability; conductivity directly reflects the continuity of the conductive network and electron migration efficiency inside graphene or carbon carrier, and plays a decisive role in the rapid output of electrons in the methanol oxidation reaction, so it is given the highest weight. : The reciprocal of the charge transfer resistance Rct The influence on interfacial electronic synergy has a high weight, reflecting the ease of cross-boundary electron transfer at the interface between platinum particles and carbonaceous support; the smaller Rct is, the lower the barrier to interfacial electron transfer, and the more significant its contribution to reaction kinetics, so it is given the second highest weight. : The reciprocal of Vpt, which characterizes the dispersion of platinum particle size distribution. The influence on interfacial electronic synergy has a medium weight and is used to reflect the effect of platinum particle loading uniformity on interfacial electronic continuity; lower particle size dispersion is conducive to the formation of uniform electron transport channels, but its influence is relatively indirect, so its weight is slightly lower. By increasing the conductivity of the carrier phase Interface electronic cross-border capability and platinum loading uniformity By performing weighted fusion, an interface electronic synergy coefficient (JDX) is constructed to achieve a comprehensive quantification of the continuity and synergy of electron transport at the platinum-carbon interface, providing a unified evaluation basis for screening interface electronic properties.

[0033] In this embodiment, by weighting and fusing the conductivity of the carbonaceous support, charge transfer resistance, and the dispersion of platinum particle size distribution, an interfacial electronic synergy coefficient is constructed. This coefficient provides a comprehensive quantitative characterization of the support's conductivity, interfacial electron transport efficiency, and platinum loading uniformity. This transforms the interfacial electronic synergy from a qualitative judgment into a calculable and comparable evaluation index, thereby improving the objectivity and accuracy of screening the interfacial performance of platinum-based catalysts.

[0034] Example 5 This embodiment is an explanation based on Embodiment 4. Please refer to it. Figure 1 Specifically, step two also includes: S22. By setting a preset interface collaboration threshold Jth, and comparing and analyzing the interface electronic collaboration coefficient JDX with the interface collaboration threshold Jth, the first evaluation results are obtained, including: When the interfacial electronic synergy coefficient JDX ≥ interfacial synergy threshold Jth, it indicates that the interfacial electronic synergy is qualified, and the first qualified label is generated. The platinum-based catalyst sample is then marked to generate a set of qualified samples for interfacial electronic synergy. When the interfacial electronic synergy coefficient JDX < the interfacial synergy threshold Jth, it indicates that the interfacial electronic synergy is unqualified. The platinum-based catalyst sample has problems with discontinuous electron transport or abnormal interfacial impedance at the interface between platinum and carbonaceous support, which leads to the risk of reduced electron migration efficiency and limited electron supply in the methanol oxidation reaction. This triggers the first warning instruction and generates the first strategy: mark the current sample as a sample with unqualified interfacial electronic synergy and terminate the screening process.

[0035] The interface synergy threshold Jth was obtained by statistically analyzing interfacial electron transport test data from a large number of platinum-based catalyst samples with different carbonaceous supports. The distribution range of the interfacial electron synergy coefficient JDX under continuous and significantly restricted interfacial electron transport states was extracted. Combined with the engineering requirements for electron supply stability in the methanol fuel cell anode reaction and the experience of technical personnel, a critical threshold was determined to distinguish between acceptable and unacceptable interfacial electron synergy. Referring to relevant fuel cell testing specifications, electrode design principles, and engineering operation experience, the interface synergy threshold Jth was developed to effectively identify whether the electron transport at the platinum-carbonaceous support interface meets the reaction requirements.

[0036] In this embodiment, by setting an interface synergy threshold and determining the threshold of the interface electronic synergy coefficient, rapid qualification and automatic labeling of the interface electronic synergy of platinum-based catalysts can be achieved. This allows samples with limited electron transport or abnormal interface impedance to be identified and eliminated in the early screening stage, thereby effectively reducing the resource consumption of subsequent testing and evaluation and improving the efficiency and targeting of the screening process for platinum-based catalysts for methanol fuel cells.

[0037] Example 6 This embodiment is an explanation based on Embodiment 5. Please refer to it. Figure 1 Specifically, step three includes: S31. Based on the qualified sample set of interface electronic collaboration, extract the corresponding peak current density Im and offset from the electrochemical behavior feature dataset. By combining the initial potential ECO from the stability tolerance feature dataset, the intermediate regulation conflict coefficient ZTCX is calculated as follows:

[0038] In the formula, a1, a2 and a3 represent weighting coefficients.

[0039] The peak current density Im, which characterizes the effect of methanol oxidation on intermediate regulation, has the highest weight and is a direct reflection of the catalytic activity level. The higher the peak current density, the higher the number of active sites and the higher the reaction rate, making it a core indicator for evaluating catalytic performance. Characterizing peak potential shift The impact of intermediate regulation conflict has a medium to high weighting, reflecting the degree of interference of reaction kinetics hindrance and intermediate adsorption on the reaction pathway; a large potential shift usually means that the reaction process is restricted. The influence of the CO oxidation initiation potential Eco on the regulation of intermediates is characterized by a medium weight. It is used to reflect the removal ability of platinum active sites on poisoning intermediates such as CO. It has an important influence on long-term stability, but its contribution to transient activity is relatively indirect. By analyzing the catalytic activity enhancement term Im and the intermediate inhibition-related penalty term... We use weighted differential modeling with Eco to construct the intermediate regulation conflict coefficient ZTCX, which enables quantitative identification of imbalance states such as "high activity - strong poisoning" or "low activity - weak poisoning", and is used to determine whether the catalyst has controllable intermediate regulation capability.

[0040] In this embodiment, by comprehensively quantifying the peak current density of methanol oxidation, peak potential shift, and CO oxidation onset potential, an intermediate regulation conflict coefficient is constructed. This enables a unified characterization of the synergistic relationship between catalytic activity enhancement and intermediate inhibition ability. Consequently, the intermediate regulation state of the catalyst can be intuitively reflected under a single index, improving the accuracy and comparability of the comprehensive evaluation of the reaction performance and anti-poisoning ability of platinum-based catalysts.

[0041] Example 7 This embodiment is an explanation based on Embodiment 6. Please refer to it. Figure 1 Specifically, step three also includes: S32, via a preset intermediate conflict threshold range And the intermediate regulation conflict coefficient ZTCX and the intermediate conflict threshold range are used. A comparative analysis was conducted to obtain the second evaluation results, including: When the intermediate regulation conflict coefficient ZTCX ∈ the intermediate conflict threshold range When the time is right, it indicates that the catalytic activity and anti-poisoning performance of the current platinum-based catalyst sample are in a controllable and coordinated state, and the intermediate regulation ability is qualified; generate a second qualified label to mark the platinum-based catalyst sample and generate a qualified sample set of intermediate regulation; When the intermediate modulates the conflict coefficient ZTCX Intermediate conflict threshold range When the signal is triggered, it indicates that the catalytic activity and anti-poisoning performance of the current platinum-based catalyst sample are in an uncontrolled conflict state, the intermediate regulation ability is unqualified, and there is a risk of active site poisoning, reaction kinetic limitation or performance degradation. This triggers a second warning instruction and generates a second strategy: mark the current sample as an unqualified sample with intermediate regulation conflict and terminate the screening process.

[0042] Intermediate conflict threshold range The method of obtaining the data involves statistical analysis of a large amount of electrochemical performance test data of platinum-based catalysts in the methanol oxidation process. The distribution range of the intermediate-mediated conflict coefficient ZTCX was extracted when catalytic activity and anti-poisoning performance were in a coordinated state and when significant conflicts occurred. Combined with the long-term operational stability requirements of methanol fuel cells and the experience of experts in the field, a reasonable conflict judgment threshold range was determined. This threshold range is used to distinguish whether the improvement of catalytic activity and the ability to inhibit intermediates are in a controllable balance, thereby avoiding the risk of performance degradation caused by intermediate accumulation.

[0043] In this embodiment, by setting an intermediate conflict threshold range and determining the intermediate regulation conflict coefficient within a range, rapid classification and automatic screening of the synergistic state of catalytic activity and anti-poisoning performance can be achieved. This can promptly identify platinum-based catalyst samples with unbalanced intermediate regulation and terminate their screening process in advance, thereby preventing samples without engineering application potential from entering subsequent stages and improving catalyst screening efficiency and result reliability.

[0044] Example 8 This embodiment is an explanation based on Embodiment 7. Please refer to it. Figure 1 Specifically, step four includes: S41. Based on the qualified sample set regulated by intermediates, combined with the specific surface area Sc of the corresponding carbonaceous support in the structural interface feature dataset, and the current retention rate in the stability tolerance feature dataset. Average particle size variation rate of platinum particles The structural evolution tolerance coefficient YNX is calculated using the following formula:

[0045] In the formula, s1, s2 and s3 represent weighting coefficients.

[0046] Characterizing current retention rate The influence on structural evolution tolerance has the highest weight and is a direct reflection of the catalyst's ability to maintain performance under long-term operating conditions, comprehensively reflecting the stability of the support and the structural stability of the platinum particles. Characterizing the rate of change in average particle size of platinum particles The influence on structural tolerance has a high weight and is used to reflect the aggregation and migration behavior of platinum particles during the aging process. It is the main characterization parameter for structural instability. : The reciprocal of the specific surface area Sc of carbonaceous supports The impact on structural tolerance is of secondary importance, and is used to reflect the fundamental supporting role of the carrier pore structure and specific surface area in the anchoring ability and anti-sintering ability of platinum particles. By maintaining the ability to integrate performance Structural degradation rate Basic characteristics of carrier structure We constructed a structural evolution tolerance coefficient YNX to achieve a comprehensive quantitative evaluation of the structural stability and service reliability of platinum-based catalysts under long-term operation and accelerated aging conditions, providing a basis for screening for engineering applications.

[0047] In this embodiment, by integrating the specific surface area of ​​the carbonaceous support, the current retention rate, and the change rate of platinum particle size, a structural evolution tolerance coefficient is constructed. This enables a comprehensive quantitative assessment of the long-term operation and structural stability of platinum-based catalysts, which can simultaneously reflect the support tolerance, activity retention level, and platinum particle stability. This provides a unified, intuitive, and comparable basis for catalyst screening for engineering applications.

[0048] Example 9 This embodiment is an explanation based on Embodiment 8. Please refer to it. Figure 1 Specifically, step four also includes: S42. By setting a pre-defined structural tolerance threshold Yth, and comparing the structural evolution tolerance coefficient YNX with the structural tolerance threshold Yth, the third evaluation results are obtained, including: When the structural evolution tolerance coefficient YNX ≥ the structural tolerance threshold Yth, it means that the current sample meets the engineering application requirements in terms of activity retention, structural stability and support tolerance, and is judged as a qualified sample in terms of structural evolution tolerance. A third qualified label is generated to mark the platinum-based catalyst sample and generate a set of candidate catalysts for methanol battery engineering applications. When the structural evolution tolerance coefficient YNX < structural tolerance threshold Yth, it indicates that the current sample does not meet the engineering application requirements in terms of activity retention, structural stability, or carrier tolerance. It is judged as a sample with unqualified structural evolution tolerance, triggering the third warning instruction and generating the third strategy: marking the current sample as a sample with unqualified structural evolution tolerance and terminating the screening process.

[0049] The structural tolerance threshold Yth was obtained by statistically analyzing the structural stability and performance retention data of various carbon-supported platinum-based catalysts under accelerated aging and long-term operation test conditions. The distribution characteristics of the structural evolution tolerance coefficient YNX under the controllable structural evolution state and the obvious instability state were extracted. Combined with the requirements of engineering application for activity retention rate, platinum particle stability and support durability, and with reference to fuel cell durability test specifications and engineering application experience, the structural tolerance threshold Yth used to determine whether the catalyst meets the engineering application conditions was determined.

[0050] In this embodiment, by setting a structural tolerance threshold and comparing it with the structural evolution tolerance coefficient, the engineering suitability of platinum-based catalysts can be quickly determined. This can effectively distinguish between candidate samples that meet the requirements for long-term operation and samples with insufficient structural stability during the screening stage, preventing catalysts with substandard durability from entering subsequent application stages, thereby improving the reliability of methanol fuel cell catalyst screening and the success rate of engineering applications.

[0051] Example 10 This embodiment is an explanation based on Embodiment 9. Please refer to it. Figure 1 Specifically, step five includes: S51. Based on samples marked as unqualified for interfacial electronic synergy, the overall conductivity and charge transfer resistance data of the samples are analyzed in conjunction with the dispersion of platinum particle size distribution during the interfacial electronic synergy test. The correspondence between the limited interfacial electron transport and the conductive structure of the support and the platinum loading state is identified and recorded. The construction method of the carbonaceous support conductive network and the loading configuration of platinum particles are optimized and adjusted in the catalyst design stage. S52. Based on samples marked as unqualified due to intermediate regulation conflict, the electrochemical performance data were analyzed during the intermediate regulation evaluation process using methanol oxidation peak current density, peak potential shift, and CO oxidation onset potential data. The conflict between catalytic activity enhancement and intermediate inhibition ability was determined, and performance response characteristics related to the electronic state of platinum active sites and the chemical environment of the support surface were extracted. These performance response characteristics were recorded and used to optimize and adjust the catalyst composition design or structural design stage. S53. Based on samples marked as unqualified for structural evolution tolerance, multi-scale structural characterization of the carbonaceous support was performed before and after the structural evolution tolerance test. The obtained structural evolution data was time-aligned and jointly modeled with the current retention rate time series and platinum particle size change data collected during constant potential aging. Multivariate correlation analysis was used to extract the coupled response characteristics of the structural changes of the carbonaceous support to the stability of platinum particle size and the decay behavior of electrochemical performance. The coupled response characteristics were recorded to optimize and adjust the catalyst design and screening strategy.

[0052] In this embodiment, targeted mechanistic analyses were conducted on different types of unqualified platinum-based catalyst samples. Key issues such as limited interfacial electron transport, imbalanced intermediate regulation, and unstable structural evolution were correlated with corresponding electrical parameters, electrochemical performance indicators, and structural characteristics. This enabled effective feedback of screening results to catalyst design and screening strategies, thereby improving the targeting and efficiency of catalyst optimization and adjustment, reducing blind trial and error, and enhancing the overall R&D efficiency and application reliability of platinum-based catalysts for methanol fuel cells.

[0053] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0054] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion, characterized in that, Includes the following steps: Step 1: Simultaneously test platinum-based catalyst candidate samples with graphene and carbon materials as carbon supports under methanol fuel cell anode test conditions, and collect basic support and interface parameter data, electrochemical behavior parameter data, and stability tolerance parameter data. After preprocessing, a dataset of structural interface features, an dataset of electrochemical behavior features, and a dataset of stability tolerance features are generated. Step 2: Based on the structural interface feature dataset and the electrochemical behavior feature dataset, calculate and obtain the interface electronic synergy coefficient JDX, and compare it with the interface synergy threshold Jth to determine whether the interface electronic synergy is qualified. If qualified, the platinum-based catalyst sample is marked to generate a qualified sample set of interface electronic synergy; if unqualified, the current sample is marked as an unqualified sample of interface electronic synergy. Step 3: Based on the electrochemical behavior characteristic dataset and the stability tolerance characteristic dataset, calculate and obtain the intermediate regulation conflict coefficient ZTCX, and compare it with the intermediate conflict threshold range. A comparison is made to determine whether the intermediate regulation capability is qualified; if qualified, the platinum-based catalyst samples are marked to generate a qualified intermediate regulation sample set. If the sample fails to meet the requirements, the current sample will be marked as an intermediate regulation conflict non-compliant sample. Step 4: Based on the structural interface feature dataset and the stability tolerance feature dataset, calculate and obtain the structural evolution tolerance coefficient YNX, and compare it with the structural tolerance threshold Yth to determine whether the current sample's structural evolution tolerance is qualified. If qualified, the platinum-based catalyst sample is marked to generate a candidate catalyst set for methanol battery engineering applications; if unqualified, the current sample is marked as a sample with unqualified structural evolution tolerance. Step 5: Based on the catalyst samples marked as unqualified in the evaluation results, targeted analysis is carried out from three dimensions: interfacial electron transport, intermediate reaction regulation, and structural evolution tolerance. By correlating key electrical parameters, electrochemical performance indicators, and structural evolution characteristics, the dominant influencing factors that limit performance are identified, and the analysis results are fed back to the catalyst support structure, platinum loading mode, and composition design and screening stages for optimization and adjustment.

2. The method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion according to claim 1, characterized in that, Step one includes: S11. By real-time monitoring of the anode test conditions of methanol fuel cells, multi-source basic data collection is performed on platinum-based catalyst candidate samples with graphene and carbon materials as carbon supports; by simultaneously monitoring the support structural state, interfacial electronic behavior, and service stability of platinum-based catalysts during the methanol electrocatalytic reaction, basic support and interface parameter data, electrochemical behavior parameter data, and stability tolerance parameter data are collected, specifically through the following steps: S111. The specific surface area (Sc) of the carbonaceous support was collected by performing nitrogen adsorption-desorption tests on the platinum-based catalyst sample in the sample testing chamber; the conductivity of the carbonaceous support was collected by setting up a four-probe testing device on the carbonaceous support tablet sample in the sample testing chamber. The average particle size Dpt of platinum particles on the carbonaceous support was collected by imaging the platinum-based catalyst sample using a transmission electron microscope in the sample testing chamber; the dispersion Vpt of the platinum particle size distribution was collected by statistically analyzing the size of platinum particles in the transmission electron microscope images. S112. Cyclic voltammetry tests were performed on the platinum-based catalyst electrode on a methanol fuel cell electrochemical testing platform to collect the peak current density Im of the methanol oxidation reaction; the position of the oxidation peak in the cyclic voltammetry test curve was recorded to collect the offset of the methanol oxidation peak potential relative to the reference potential. The charge transfer resistance Rct at the electrode interface was collected by performing electrochemical impedance spectroscopy on the platinum-based catalyst electrode on the same testing platform. S113. By conducting continuous operation tests on the platinum-based catalyst electrode under constant potential conditions, the current retention rate during the test cycle is collected. The onset potential (ECO) of the CO oxidation reaction was acquired by performing CO stripping voltammetry on the platinum-based catalyst electrode on the test platform. The average particle size change rate of platinum particles was compared by performing microstructural imaging on the platinum-based catalyst sample before and after accelerated aging tests. .

3. The method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion according to claim 2, characterized in that, Step one also includes: S12, based on the specific surface area Sc and conductivity of the carbonaceous carrier. The average particle size (Dpt) and dispersion (Vpt) of platinum particles were processed using a dimensionless normalization method to unify the scale of structural and interface parameters with different physical dimensions. Furthermore, the time consistency correction method was used to align and correct repeated test data using timestamp information from multiple test results, eliminating the influence of test batch differences on parameter values. Outlier suppression and outlier removal methods were employed to process data that significantly deviated from the statistical range, obtaining a set of basic standard parameters for the carrier and interface, and establishing a structural interface feature dataset. S13, based on the acquired peak current density Im and offset In addition to charge transfer resistance (Rct) data, a multi-parameter normalization mapping method was used to perform dimensionless transformation of electrochemical behavior parameters under different test dimensions; and based on the time series information of cyclic voltammetry and electrochemical impedance spectroscopy, a test stage consistency correction method was used to eliminate the influence of different test stages and environmental fluctuations on parameter comparability; and a statistical interval constraint method was used to suppress abnormal fluctuation data, obtain a standard parameter set of electrochemical behavior, and establish an electrochemical behavior feature dataset. S14, Current holding rate based on data acquisition Initiation potential (ECO) and average particle size change rate of platinum particles The data were processed using normalization and scaling methods to unify the dimensions of stability parameters obtained under long-term operation and accelerated aging conditions. Time series data from continuous operation tests were processed using time consistency and trend smoothing methods to reduce the impact of instantaneous fluctuations on stability parameters. Stability interval screening methods were used to constrain abnormal decay or mutation data to obtain a standard parameter set for structural evolution and tolerance, and a stability tolerance feature dataset was established.

4. The method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion according to claim 3, characterized in that, Step two includes: S21. Extract the processed conductivity using the structural interface feature dataset and the electrochemical behavior feature dataset. The interface electronic cooperation coefficient JDX was calculated using data on dispersion Vpt and charge transfer resistance Rct.

5. The method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion according to claim 4, characterized in that, Step two also includes: S22. By setting a preset interface collaboration threshold Jth, and comparing and analyzing the interface electronic collaboration coefficient JDX with the interface collaboration threshold Jth, the first evaluation results are obtained, including: When the interfacial electronic synergy coefficient JDX ≥ interfacial synergy threshold Jth, it indicates that the interfacial electronic synergy is qualified, and the first qualified label is generated. The platinum-based catalyst sample is then marked to generate a set of qualified samples for interfacial electronic synergy. When the interfacial electronic synergy coefficient JDX < the interfacial synergy threshold Jth, it indicates that the interfacial electronic synergy is unqualified. The platinum-based catalyst sample has problems with discontinuous electron transport or abnormal interfacial impedance at the interface between platinum and carbonaceous support, which leads to the risk of reduced electron migration efficiency and limited electron supply in the methanol oxidation reaction. This triggers the first warning instruction and generates the first strategy: mark the current sample as a sample with unqualified interfacial electronic synergy and terminate the screening process.

6. The method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion according to claim 5, characterized in that, Step three includes: S31. Based on the qualified sample set of interface electronic collaboration, extract the corresponding peak current density Im and offset from the electrochemical behavior feature dataset. By combining the initial potential ECO in the stability tolerance feature dataset, the intermediate regulation conflict coefficient ZTCX is calculated and obtained.

7. The method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion according to claim 6, characterized in that, Step three also includes: S32, via a preset intermediate conflict threshold range And the intermediate regulation conflict coefficient ZTCX and the intermediate conflict threshold range are used. A comparative analysis was conducted to obtain the second evaluation results, including: When the intermediate regulation conflict coefficient ZTCX ∈ the intermediate conflict threshold range When the time is right, it indicates that the catalytic activity and anti-poisoning performance of the current platinum-based catalyst sample are in a controllable and coordinated state, and the intermediate regulation ability is qualified; generate a second qualified label to mark the platinum-based catalyst sample and generate a qualified sample set of intermediate regulation; When the intermediate modulates the conflict coefficient ZTCX Intermediate conflict threshold range When the signal is triggered, it indicates that the catalytic activity and anti-poisoning performance of the current platinum-based catalyst sample are in an uncontrolled conflict state, the intermediate regulation ability is unqualified, and there is a risk of active site poisoning, reaction kinetic limitation or performance degradation. This triggers a second warning instruction and generates a second strategy: mark the current sample as an unqualified sample with intermediate regulation conflict and terminate the screening process.

8. The method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion according to claim 7, characterized in that, Step four includes: S41. Based on the qualified sample set regulated by intermediates, combined with the specific surface area Sc of the corresponding carbonaceous support in the structural interface feature dataset, and the current retention rate in the stability tolerance feature dataset. Average particle size variation rate of platinum particles The structural evolution tolerance coefficient YNX is calculated and obtained.

9. The method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion according to claim 8, characterized in that, Step four also includes: S42. By setting a pre-defined structural tolerance threshold Yth, and comparing the structural evolution tolerance coefficient YNX with the structural tolerance threshold Yth, the third evaluation results are obtained, including: When the structural evolution tolerance coefficient YNX ≥ the structural tolerance threshold Yth, it means that the current sample meets the engineering application requirements in terms of activity retention, structural stability and support tolerance, and is judged as a qualified sample in terms of structural evolution tolerance. A third qualified label is generated to mark the platinum-based catalyst sample and generate a set of candidate catalysts for methanol battery engineering applications. When the structural evolution tolerance coefficient YNX < structural tolerance threshold Yth, it indicates that the current sample does not meet the engineering application requirements in terms of activity retention, structural stability, or carrier tolerance. It is judged as a sample with unqualified structural evolution tolerance, triggering the third warning instruction and generating the third strategy: marking the current sample as a sample with unqualified structural evolution tolerance and terminating the screening process.

10. The method for screening platinum-based catalysts for methanol batteries based on multi-feature fusion according to claim 9, characterized in that, Step five includes: S51. Based on samples marked as unqualified for interfacial electronic synergy, the overall conductivity and charge transfer resistance data of the samples are analyzed in conjunction with the dispersion of platinum particle size distribution during the interfacial electronic synergy test. The correspondence between the limited interfacial electron transport and the conductive structure of the support and the platinum loading state is identified and recorded. The construction method of the carbonaceous support conductive network and the loading configuration of platinum particles are optimized and adjusted in the catalyst design stage. S52. Based on samples marked as unqualified due to intermediate regulation conflict, the electrochemical performance data were analyzed during the intermediate regulation evaluation process using methanol oxidation peak current density, peak potential shift, and CO oxidation onset potential data. The conflict between catalytic activity enhancement and intermediate inhibition ability was determined, and performance response characteristics related to the electronic state of platinum active sites and the chemical environment of the support surface were extracted. These performance response characteristics were recorded and used to optimize and adjust the catalyst composition design or structural design stage. S53. Based on samples marked as unqualified for structural evolution tolerance, multi-scale structural characterization of the carbonaceous support was performed before and after the structural evolution tolerance test. The obtained structural evolution data was time-aligned and jointly modeled with the current retention rate time series and platinum particle size change data collected during constant potential aging. Multivariate correlation analysis was used to extract the coupled response characteristics of the structural changes of the carbonaceous support to the stability of platinum particle size and the decay behavior of electrochemical performance. The coupled response characteristics were recorded to optimize and adjust the catalyst design and screening strategy.

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