Method and system for detecting gas scouring damage of electrolytic cell electrode catalytic layer

Abstract

The application discloses an electrolytic cell electrode catalytic layer gas scouring damage detection method and system, the method comprises the following steps: generating a gas-liquid two-phase bubble flow through a test device capable of simulating the gas scouring environment of the electrode in the electrolytic cell, and using the gas-liquid two-phase bubble flow to scour the catalytic layer surface of the fixedly arranged to-be-detected electrode; collecting test data of the to-be-detected electrode corresponding to the electrolyte element concentration data, the surface image data, the electrochemical data and the quality data at multiple time nodes in the scouring process; and determining the trend change data of the electrode catalytic layer gas scouring damage according to the test data. The detection method generates a gas-liquid two-phase bubble flow through a test device, simulates the gas scouring environment in the real electrolytic cell, matches the scouring working condition of the to-be-detected electrode with the actual application scene, and guarantees the working condition effectiveness of the detection data.

Description

Technical Field

[0001] This application belongs to the field of water electrolysis for hydrogen production technology, and in particular relates to a method and system for detecting gas erosion damage to the catalyst layer of an electrolyzer electrode. Background Technology

[0002] In the alkaline water electrolysis process for hydrogen production, the continuous generation of bubbles on the electrode surface erodes the catalyst layer, causing catalyst shedding and performance degradation. Therefore, testing the catalyst layer's resistance to gas erosion is a crucial aspect of electrode material development. Existing testing methods mostly employ single evaluation approaches, and many tests rely on actual electrolyzers, making precise control of operating parameters difficult. Furthermore, the testing process requires frequent electrode disassembly, hindering the continuous acquisition of multi-dimensional data to reconstruct the damage development process. Consequently, the assessment of catalyst layer erosion damage is often delayed and incomplete. Summary of the Invention

[0003] This application provides, in one aspect, a method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode. This method reconstructs the development process of erosion damage by analyzing the correlation between changes in data from different dimensions, thereby determining the trend of damage changes. Another aspect of this application provides a system for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode.

[0004] This application discloses a method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode, including: A gas-liquid two-phase bubble flow is generated by an experimental device that can simulate the gas scouring environment of electrodes in an electrolytic cell, and the gas-liquid two-phase bubble flow is used to scour the surface of the catalyst layer of the electrode under test that is fixedly set up. Experimental data, including electrolyte element concentration data, surface image data, electrochemical data, and mass data, were collected at multiple time points during the rinsing process for the electrode under test. Based on the experimental data, the trend of gas erosion damage to the electrode catalyst layer was determined.

[0005] Optionally, the trend change data for gas erosion damage to the electrode catalyst layer determined based on the experimental data include: Element dissolution correlation indicators are generated based on the electrolyte element concentration data; A catalytic layer morphology retention index is generated based on the surface image data, and a catalytic activity decay index is generated based on the electrochemical data. Based on the quality data, a mass loss change index is generated, and based on each individual index, trend change data of gas erosion damage to the electrode catalyst layer is generated.

[0006] Optionally, generating elemental dissolution correlation indicators based on the electrolyte elemental concentration data includes: The concentration ratio of active elements to framework elements at each time point is calculated to obtain the selective dissolution coefficient; The correlation coefficients between elements are calculated based on the time series data of element concentrations, and the associated edges between element nodes are established to construct an element association network. The element association network is a topological network constructed with metal elements as nodes and the time series correlation of element concentrations as the basis for connecting edges. Calculate the topological index of the element association network, identify the abrupt change points of the topological index over time, and generate element leaching association index based on the time corresponding to the abrupt change point and the selective leaching coefficient.

[0007] Optionally, generating catalytic layer morphology retention indices based on the surface image data includes: The surface image data is converted to grayscale and denoised to obtain a preprocessed image; The preprocessed image was divided into three regions using a dual threshold segmentation method: metal exposure area, pit area, and catalyst layer intact area. The pixel percentage of the three types of regions is calculated to obtain the catalyst layer retention ratio, and the catalyst layer morphology retention index is generated based on the catalyst layer retention ratio.

[0008] Optionally, generating catalytic activity decay indicators based on the electrochemical data includes: Extract current data from non-Radida intervals in electrochemical data, calculate the difference in charging current at different scan rates, and calculate the double-layer capacitance based on the linear relationship between scan rate and charging current difference to obtain the electrochemical active area. The electrochemical active area retention rate at different time points was calculated, and the decay time constant was obtained by fitting. Catalytic activity decay index is generated based on decay time constant.

[0009] Optionally, generating a quality loss change index based on the quality data includes: The baseline mass of the blank matrix is ​​determined to obtain the initial catalyst mass of the electrode to be tested; Calculate the corresponding cumulative mass loss based on the current mass of the electrode under test at each time point; Generate a time-cumulative mass loss curve, extract the steady-state mass loss rate from the curve, and generate a mass loss change index based on the steady-state mass loss rate.

[0010] Optionally, a gas-liquid two-phase bubble flow is generated using a test device capable of simulating the gas scouring environment of electrodes in an electrolytic cell. The gas-liquid two-phase bubble flow is then used to scour the catalyst layer surface of the fixedly mounted electrode under test, including: Compressed gas and alkaline electrolyte are passed into a gas-liquid two-phase static mixer for thorough mixing; A high-speed liquid flow carrying bubbles is output through the Venturi contraction tube at the outlet of the gas-liquid two-phase static mixer, forming a gas-liquid two-phase bubble flow. Adjusting the ratio of gas flow rate to liquid flow rate controls the size and density of bubbles; Adjust the position and angle of the electrode to be tested so that the gas-liquid two-phase bubble flow impacts the surface of the catalyst layer perpendicularly.

[0011] Optionally, trend change data on gas erosion damage to the electrode catalyst layer based on each individual indicator include: Determine the weighting coefficients for element dissolution correlation index, catalyst layer morphology retention index, catalytic activity decay index, and mass loss change index. The comprehensive evaluation value is obtained by multiplying each individual indicator by its corresponding weight coefficient and then summing the results. Based on the time sequence of the comprehensive evaluation values, trend data of gas erosion damage to the electrode catalyst layer are generated.

[0012] Optionally, identifying abrupt changes in topological metrics over time includes: Set the size of the sliding window and extract consecutive topological index data groups in chronological order; Calculate the network density for each data group and compare the network density of the current data group with that of the previous data group; When the decrease rate exceeds the preset threshold, the current time point is determined to be a point of inflection for the topological index.

[0013] Another aspect of this application discloses a gas erosion damage detection system for the catalyst layer of an electrolytic cell electrode, including a test device and a gas erosion damage detection device for the catalyst layer of an electrolytic cell electrode. The electrolytic cell electrode catalyst layer gas erosion damage detection device includes a gas erosion module, a data acquisition module, and a trend analysis module. The gas flushing module is used to generate a gas-liquid two-phase bubble flow through a test device that can simulate the gas flushing environment of the electrodes in an electrolytic cell, and to flush the surface of the catalyst layer of the fixed electrode under test with the gas-liquid two-phase bubble flow. The data acquisition module is used to collect experimental data such as electrolyte element concentration data, surface image data, electrochemical data, and mass data corresponding to the electrode under test at multiple time points during the rinsing process. The trend analysis module is used to determine the trend change data of gas erosion damage to the electrode catalyst layer based on the experimental data.

[0014] As can be seen from the above technical solution, the detection method of this application first generates a gas-liquid two-phase bubble flow through an experimental device to simulate the gas scouring environment in a real electrolytic cell, so that the scouring conditions of the electrode under test match the actual application scenario, ensuring the validity of the detection data. On this basis, four types of experimental data are collected simultaneously at multiple time points during the scouring process: electrolyte element concentration, surface image, electrochemistry, and mass. The data of different dimensions correspond to the damage manifestations of different aspects of the catalyst layer, such as element dissolution, morphological changes, activity decay, and mass loss. When multiple types of data are combined for analysis, the development process of catalyst layer scouring damage can be reconstructed through the corresponding relationship of changes in data of different dimensions, thereby determining the trend change data of damage. This can continuously present the damage development state, improve the accuracy of determining gas scouring damage, and achieve early warning of damage to the electrode catalyst layer. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are optional implementations of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic flowchart of the method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode provided in an embodiment of this application; Figure 2 This is a flowchart illustrating the trend of gas erosion damage to the electrode catalyst layer based on experimental data, provided in an embodiment of this application. Figure 3 This is a flowchart illustrating the generation of elemental dissolution correlation indicators provided in the embodiments of this application; Figure 4 This is a schematic diagram of the process for generating catalyst layer morphology retention indicators provided in the embodiments of this application; Figure 5 This is a schematic diagram of the process for generating catalytic activity attenuation indicators provided in the embodiments of this application; Figure 6 This is a schematic diagram of the process for generating quality loss change indicators provided in the embodiments of this application; Figure 7 This is a schematic flowchart of the gas-liquid two-phase bubble flow generation and flushing steps provided in the embodiments of this application; Figure 8 This is a schematic diagram of the overall structure of the laboratory gas flushing device provided in the embodiments of this application; Figure 9 This is a schematic diagram of the electrode fixing device structure provided in the embodiments of this application; Figure 10This is a schematic diagram of the process for generating trend change data of gas erosion damage to the electrode catalyst layer based on each individual indicator, provided in an embodiment of this application. Figure 11 This is a flowchart illustrating the process of identifying abrupt changes in topological indicators over time, as provided in an embodiment of this application. Figure 12 This is a schematic diagram of the modules of the electrolytic cell electrode catalyst layer gas erosion damage detection system provided in the embodiments of this application. Detailed Implementation

[0017] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not limiting, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without such specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0018] like Figure 1 As shown, this application discloses a method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode, comprising: S100: A gas-liquid two-phase bubble flow is generated by a test device that can simulate the gas scouring environment of the electrode in the electrolytic cell, and the gas-liquid two-phase bubble flow is used to scour the surface of the catalyst layer of the electrode to be tested, which is fixedly set up.

[0019] S200: Experimental data collected at multiple time points during the rinsing process, including electrolyte element concentration data, surface image data, electrochemical data, and mass data corresponding to the electrode under test.

[0020] S300: Determine the trend change data of gas erosion damage to the electrode catalyst layer based on the test data.

[0021] Specifically, the process of generating a gas-liquid two-phase bubble flow using the experimental device simulates the bubble scouring environment on the electrode surface during electrolytic cell operation without initiating the electrolytic reaction. In actual electrolytic cells, bubbles are directly generated by electrochemical reactions, and bubble parameters are coupled with multiple factors such as current density, electrolyte temperature, and electrode surface condition, making it difficult to individually control the scouring intensity for targeted testing.

[0022] This application employs an external gas-liquid two-phase mixing method to generate a bubble flow, which decouples the bubble scouring effect from the electrolytic reaction. This allows for the independent study of the damage effect of bubble mechanical scouring on the catalyst layer, eliminating interference from other factors such as electrochemical corrosion, and facilitating the clarification of the correlation between scouring and catalyst layer damage. The bubble flow parameters generated by the experimental setup can be adjusted according to target operating conditions, adapting to electrolytic cell scenarios under different current densities. The electrode under test is fixed in the bubble flow path, with the catalyst layer surface facing the bubble flow ejection direction. The electrode's fixing structure possesses sufficient clamping strength to maintain positional stability under high-speed liquid flow impact, preventing electrode displacement or deflection during scouring.

[0023] Test data was collected at multiple time points during the scouring process, with these time points evenly distributed throughout the test cycle according to preset time intervals. The length of the test cycle was set based on the expected scouring resistance of the electrode, with short-cycle tests covering tens of hours and long-cycle tests covering hundreds of hours. The interval between adjacent time points could be adjusted according to testing requirements; shorter time intervals were used for short-cycle tests to improve time resolution, while longer time intervals could be appropriately increased to reduce workload. Each type of test data corresponds to different manifestations of catalyst layer damage: electrolyte element concentration data reflects the degree of dissolution of metal elements from the catalyst layer into the electrolyte, corresponding to changes in the internal composition of the catalyst layer; surface image data reflects changes in the morphology of the catalyst layer surface and the extent of catalyst layer detachment, corresponding to changes in the external morphology of the catalyst layer; electrochemical data reflects changes in the electrochemical reactivity of the catalyst layer, corresponding to changes in the functional performance of the catalyst layer; and mass data reflects the overall mass loss of the catalyst layer, corresponding to changes in the macroscopic total mass of the catalyst layer. It should be noted that disturbance to the electrode under test was controlled to a minimum during data collection, and the electrode was not disassembled unless necessary to reduce additional damage to the catalyst layer caused by disassembly and assembly operations.

[0024] Determining the trend of gas erosion damage to the electrode catalyst layer based on experimental data involves integrating multi-dimensional detection results from discrete time points to generate continuous data reflecting the damage's development over time. This trend data is not a simple list of single values, but rather a holistic analysis of the damage development process based on the correlations between multi-dimensional data, identifying the characteristics of different stages of damage development. Compared to single-dimensional detection results, the trend data obtained through multi-dimensional data integration covers the complete damage chain from microscopic element dissolution to macroscopic mass loss, avoiding misjudgments caused by fluctuations in a single indicator and providing a more comprehensive reflection of the actual damage state of the catalyst layer.

[0025] In a specific example, the experimental apparatus capable of simulating the electrode gas scouring environment in an electrolytic cell includes a bubble generator and an electrode fixing device. The bubble generator comprises a compressed air supply module, including an air compressor, a storage tank, a pressure reducing valve, a flow meter, and gas pipelines. The air compressor operates at a pressure of 0.1-0.5 MPa, preferably 0.3 MPa, and the flow meter has an accuracy of ±1%FS. A liquid supply system includes a storage tank, a water pump, a flow meter, and liquid pipelines. The liquid is a 30wt% potassium hydroxide solution, with the temperature controlled at 60-80℃. The water pump flow rate is 1-5 L / min and adjustable. A gas-liquid two-phase static mixer, made of stainless steel, has internal spiral blades to promote thorough gas-liquid mixing. The mixer outlet diameter is 3-8 mm, preferably 5 mm, and utilizes the Venturi effect to generate a high-speed bubble flow. A bubble parameter control module includes a bubble size control unit and a bubble density control unit. By adjusting the ratio of gas flow rate to liquid flow rate, the bubble size is controlled to be 50-500 μm, and the bubble density to be 10³-10⁻⁶ μm. 6 The electrode fixing device includes a clamping plate made of 316L stainless steel with anti-slip texture, measuring 50mm × 50mm × 10mm; fixing bolts, M6 stainless steel bolts, capable of applying a preload of 5-10 N·m to ensure secure electrode clamping; a height adjusting screw, a precision lead screw with an adjustment accuracy of 0.1mm and an adjustment range of ±20mm; an angle adjuster, using a universal joint structure with an adjustment accuracy of ±1° to ensure the electrode surface is perpendicular to the bubble flow direction; a linear guide rail to ensure the electrode moves only in the vertical direction and prevent lateral displacement; and a splash guard installed around the electrode to prevent liquid splashing. In the above device, the adjustable parameters of the gas-liquid two-phase bubble flow include: bubble size 50-500μm and bubble density 10³-10⁻⁶. 6 The parameters are as follows: bubble density / cm³, bubble velocity 0.1-2.0 m / s, scouring angle 90°, scouring area (electrode exposure area) 10-50 cm², liquid temperature 60-80℃, and scouring time programmable for continuous or periodic operation. It should be noted that the above parameters are merely illustrative and are not intended to limit the scope of this application.

[0026] In specific examples, the time intervals are set according to the experimental data acquisition methods: the acquisition interval for surface image data is 0.5 to 2 hours, the acquisition interval for electrochemical data is 1 to 4 hours, the acquisition interval for mass data is 1 to 4 hours, and the sampling interval for electrolyte element concentration data is 1 to 8 hours. These parameters are merely illustrative and are not intended to limit the scope of this application.

[0027] In alternative implementations, such as Figure 2 As shown, the trend changes in gas erosion damage to the electrode catalyst layer, determined based on the experimental data, include: S110: Generate element dissolution correlation index based on the electrolyte element concentration data.

[0028] S120: Generate a catalyst layer morphology retention index based on the surface image data, and generate a catalytic activity decay index based on the electrochemical data.

[0029] S130: Generate a mass loss change index based on the mass data, and generate trend change data of gas erosion damage to the electrode catalyst layer based on each individual index.

[0030] For example, elemental dissolution correlation indices are generated based on electrolyte elemental concentration data, corresponding to damage characterization at the microscopic composition level of the catalyst layer. The catalyst layer is composed of multiple metal elements, each playing a different role in its structure. Some elements constitute the skeletal structure of the catalyst layer, maintaining its overall morphology and bonding strength; others serve as active components, providing active sites for electrochemical reactions. Under the combined effects of bubble erosion and electrolyte corrosion, the dissolution behavior of different elements varies, with the dissolution rate of active components typically faster than that of skeletal components. This differentiated dissolution is an early manifestation of catalyst layer performance degradation, at which point the macroscopic morphology and overall quality of the catalyst layer may not yet show significant changes. Elemental dissolution correlation indices capture this early change, identifying the nascent stage of damage.

[0031] Based on surface image data, a catalyst layer morphology retention index is generated, corresponding to the damage characterization at the mesoscopic morphological level of the catalyst layer. After continuous erosion by bubbles, the catalyst layer gradually exhibits morphological changes such as localized catalyst layer detachment, pit formation, and substrate exposure. These changes can intuitively reflect the physical detachment of the catalyst layer. The catalyst layer morphology retention index quantifies the proportion of intact areas on the surface, presenting the degree of damage to the catalyst layer surface. It can identify the development process of localized detachment of the catalyst layer and correspond to the stage of damage development. Compared with the mass loss index, this index can clearly reflect the spatial distribution characteristics of damage and identify different detachment modes such as localized concentrated detachment and uniform detachment.

[0032] Catalytic activity decay indices are generated based on electrochemical data, corresponding to damage characterization at the functional performance level of the catalyst layer. The catalyst layer provides active sites for electrochemical reactions. Damage to the catalyst layer reduces the number of active sites available for reaction, directly manifesting as a decrease in the electrochemical active area and reduced catalytic performance. Catalytic activity decay indices are directly related to the actual performance of the electrode and are one of the reference bases for evaluating electrode lifespan. These indices reflect the degree of functional decay of the catalyst layer, corresponding to the functional impact stage of damage, and are closer to the actual application of the electrode than morphological and quality indices.

[0033] Mass loss variation indices are generated based on quality data, corresponding to the macroscopic damage characterization of the catalyst layer. The overall mass loss of the catalyst layer is the most direct macroscopic manifestation of catalyst layer detachment, and the rate of steady-state mass loss directly reflects the strength of the catalyst layer's bonding. The mass loss variation indices distinguish between two stages—initial rapid loss and steady-state loss—by analyzing the cumulative mass loss variation pattern, quantifying the long-term erosion resistance of the catalyst layer and corresponding to the macroscopic manifestation stage of damage.

[0034] In a specific example, the steady-state mass loss rate within each time interval is calculated using the following formula: v i =Δmi / Δt.

[0035] Among them, v i Let Δmi be the steady-state mass loss rate within the i-th time interval, Δt be the cumulative mass loss increment within the i-th time interval, and Δt be the corresponding time interval. Based on the trend of the steady-state mass loss rate over time at each time node, the average value of the steady-state mass loss rate after it tends to stabilize is determined as the steady-state mass loss rate.

[0036] By correlating four indicators with their temporal sequence and magnitude of change, trend data on gas erosion damage to the electrode catalyst layer is generated. Typically, changes in elemental dissolution behavior occur earliest, followed by a slow decline in catalytic activity, then a significant change in surface morphology, and finally, observable macroscopic mass loss. The four indicators respond sequentially to different stages of damage, and the integrated trend data covers the entire process from damage initiation to macroscopic failure. Compared to a single indicator, this approach can identify damage changes earlier and provide a more comprehensive assessment of the damage extent.

[0037] In alternative implementations, such as Figure 3 As shown, the elemental dissolution correlation index generated based on the electrolyte elemental concentration data includes: S210: Calculate the concentration ratio of active elements to framework elements at each time point to obtain the selective dissolution coefficient.

[0038] S220: Calculate the correlation coefficient between elements based on the time series data of element concentration, establish the association edges between element nodes, and construct the element association network. The element association network is a topological network constructed with metal elements as nodes and the time series correlation of element concentration as the basis for connecting edges.

[0039] S230: Calculate the topological index of the element association network, identify the abrupt change points of the topological index over time, and generate element leaching association index based on the time corresponding to the abrupt change point and the selective leaching coefficient.

[0040] Specifically, to calculate the selective dissolution coefficient by determining the concentration ratio of active elements to framework elements at each time point, it is first necessary to clarify the elemental classification in the catalyst layer. Framework elements are the metallic elements that constitute the main structure of the catalyst layer, while active elements are the metallic elements that enhance catalytic performance. For common nickel-based multi-element catalyst layers, nickel serves as the framework element, forming the main framework of the catalyst layer, while elements such as molybdenum and cobalt are dispersed in the framework as active elements to enhance catalytic performance. The metallic elements in the electrolyte originate from the dissolution and shedding of the catalyst layer, and the concentration of different elements gradually increases with the extension of rinsing time. At each time point, electrolyte samples are taken for elemental concentration detection, and the concentration values ​​of each element are obtained. The concentration of active elements is divided by the concentration of framework elements to obtain the selective dissolution coefficient at that time point. The change in the selective dissolution coefficient reflects the change in the elemental composition of the catalyst layer. A continuously increasing coefficient indicates that the dissolution rate of active elements is faster than that of framework elements, resulting in selective dissolution of the catalyst layer. This deviation in the component ratio of the catalyst layer directly affects the catalytic performance.

[0041] In a specific example, electrolyte samples are taken from the middle of the collection tank, with a fixed volume for each sample. After sampling, an equal amount of fresh electrolyte is promptly added to the storage tank to maintain a constant total electrolyte volume in the circulation loop, preventing level changes caused by sampling from affecting the pump's output flow rate. The extracted electrolyte samples are first diluted and acidified to reduce alkalinity, and the elemental concentrations are adjusted to the appropriate range of the detection equipment to improve the accuracy of the results. The treated samples are then subjected to elemental concentration testing to obtain the concentration values ​​of each metal element.

[0042] The element correlation network uses the various metal elements contained in the catalyst layer as independent nodes, and establishes connections between nodes based on the correlation between the concentration changes of different elements over time. When the overall structure of the catalyst layer is intact, the catalyst layer detachment caused by bubble erosion is mainly a complete peeling, and various elements enter the electrolyte synchronously according to the original proportion of the catalyst layer. The concentration changes of different elements have high consistency, and the correlation between elements is high. When the internal structure of the catalyst layer is damaged, the dissolution behavior of active elements and framework elements becomes differentiated, some elements dissolve preferentially, the consistency of concentration changes of different elements decreases, and the correlation decreases. The correlation coefficient between each pair of elements is calculated. When the absolute value of the correlation coefficient reaches a set threshold, a correlation edge is established between the corresponding two element nodes. All nodes and correlation edges together constitute the element correlation network for the corresponding time node. In a specific example, the set threshold ranges from 0.7 to 0.9. Those skilled in the art can set it according to actual needs, and this application does not limit it.

[0043] Topological indices are used to quantify the structural characteristics of element-related networks. Commonly used topological indices include network density and modularity. Network density reflects the tightness of connections between nodes in the network; higher density indicates stronger consistency in element dissolution behavior. Modularity reflects the degree to which nodes cluster in the network; increased modularity indicates that elements differentiate into different groups, resulting in significant differences in dissolution behavior. As scouring time increases, damage to the catalyst layer gradually accumulates, and the topological indices of the element-related network undergo continuous changes. When the damage accumulates to a certain extent, the network structure exhibits a significant jump, corresponding to a mutation point in the topological indices. The time corresponding to the mutation point is the point in time when the internal structure of the catalyst layer undergoes a significant change. This point usually occurs earlier than the time of macroscopic morphological changes and mass loss, and can be used to capture early damage signals. Combining the time of the mutation point with the final value of the selective dissolution coefficient generates a comprehensive element dissolution correlation index, which includes both the time information of early damage mutations and the degree of element dissolution, comprehensively reflecting the damage state at the microscopic level. The identification of abrupt changes in topology metrics can simultaneously consider both network density and modularity. A decrease in network density and an increase in modularity usually occur simultaneously. A decrease in network density indicates weakened overall connectivity, while an increase in modularity indicates that elements have differentiated into different groups. When both metrics show significant changes simultaneously, the accuracy of identifying abrupt changes is higher, avoiding misjudgments caused by fluctuations in a single metric. It should be noted that a comprehensive judgment rule can be set: a change point is only identified when both metrics reach a change threshold, or an alert is triggered when either metric reaches a threshold, requiring further confirmation before being officially identified as a change point.

[0044] In alternative implementations, such as Figure 4 As shown, the morphology retention index of the catalyst layer generated based on the surface image data includes: S310: Perform grayscale conversion and noise reduction on the surface image data to obtain a preprocessed image.

[0045] S320: The preprocessed image is divided into three regions using a dual threshold segmentation method: metal exposure area, pit area, and catalyst layer intact area.

[0046] S330: Calculate the pixel ratio of the three types of regions to obtain the catalyst layer retention ratio, and generate the catalyst layer morphology retention index based on the catalyst layer retention ratio.

[0047] Specifically, the surface image data is converted to grayscale and denoised to obtain a preprocessed image. First, the acquired color surface image is converted to a grayscale image. In a grayscale image, each pixel uses only a grayscale value to represent brightness information, which can significantly simplify the data volume and eliminate the interference of color differences on subsequent segmentation processing. During image acquisition, factors such as illumination fluctuations, liquid refraction, and sensor noise can affect the image, resulting in randomly distributed noise. This noise can interfere with subsequent region segmentation processing, leading to deviations in the segmentation results. A filtering algorithm is used to denoise the grayscale image, which can eliminate random noise while preserving image edge information, thus improving image quality. The window size for filtering can be adjusted according to the image resolution to ensure denoising effect while avoiding excessive blurring of the catalyst layer edges.

[0048] A dual-threshold segmentation method was used to divide the preprocessed image into three regions: exposed metal areas, pitted areas, and areas with intact catalyst layers. Unlike conventional single-threshold segmentation, which only distinguishes between foreground and background, dual-threshold segmentation can identify three different surface states. The exposed metal area corresponds to regions where the catalyst layer has completely detached, exposing the underlying metal substrate. This area typically has a high grayscale value, exhibiting a bright tone characteristic of the metal substrate. The pitted area corresponds to regions where the catalyst layer has partially detached, forming depressions. This area has weak light reflection, typically a low grayscale value, exhibiting a dark tone. The areas with intact catalyst layers correspond to regions where the catalyst layer remains intact without significant detachment. Grayscale values ​​fall between those of the exposed metal area and the pitted area. Two grayscale thresholds were pre-set: a high threshold and a low threshold. Pixels with grayscale values ​​higher than the high threshold were classified as exposed metal areas, pixels with grayscale values ​​lower than the low threshold were classified as pitted areas, and pixels with grayscale values ​​between the two thresholds were classified as areas with intact catalyst layers. The values ​​of the two thresholds can be adjusted based on the actual grayscale performance of the catalyst layer and the substrate.

[0049] The catalytic layer retention ratio is calculated by determining the pixel proportions of the three types of regions. First, the total number of pixels in the entire preprocessed image is counted. Then, the pixel counts for the metal-exposed area, the pitted area, and the intact catalytic layer area are counted separately. The pixel counts of each region are divided by the total number of pixels to obtain the area proportion of the corresponding region. The area proportion of the intact catalytic layer is the catalytic layer retention ratio, which directly reflects the proportion of intact parts on the catalytic layer surface. Each time point corresponds to a catalytic layer retention ratio value, and the catalytic layer retention ratio gradually decreases with increasing scouring time. Based on the catalytic layer retention ratio, a catalytic layer morphology retention index is generated. Arranging the catalytic layer retention ratios at different time points in chronological order forms a sequence of morphological damage changes, presenting the development process of surface damage in the catalytic layer.

[0050] In a specific example, the surface image data is preprocessed by grayscale conversion and denoising. Surface images of the electrode under test are acquired at different time points using a high-resolution camera with a resolution of at least 20 megapixels, a shooting distance of 20 to 30 cm, and a shooting direction perpendicular to the electrode surface. The preferred acquisition time interval is 1 hour. The surface image data is then grayscale converted to obtain a grayscale image. A median filtering algorithm is then applied to the grayscale image for denoising, with a filtering window size of 5×5 pixels. High and low thresholds are set, with the high threshold ranging from 130 to 170 and the low threshold ranging from 60 to 80. Pixels with grayscale values ​​greater than the high threshold are marked as metal exposed areas, resulting in a metal exposed area mask; pixels with grayscale values ​​less than the low threshold are marked as pit areas, resulting in a pit area mask; and pixels with grayscale values ​​between the low and high thresholds are marked as areas with intact catalyst layers. The total number of pixels in the preprocessed image is obtained; the number of pixels with a grayscale value of 255 in the metal exposed area mask is calculated and recorded as the number of pixels in the metal exposed area; the number of pixels with a grayscale value of 255 in the pit area mask is calculated and recorded as the number of pixels in the pit area; the number of pixels in the intact area of ​​the catalyst layer is calculated according to the formula: Number of pixels in the intact area of ​​the catalyst layer = Total number of pixels - Number of pixels in the metal exposed area - Number of pixels in the pit area; then the catalyst layer retention ratio is calculated according to the formula: Catalyst layer retention ratio = Number of pixels in the intact area of ​​the catalyst layer / Total number of pixels. A catalyst layer morphology retention index is generated based on the catalyst layer retention ratio, and its calculation formula is: Catalyst layer morphology retention index = Catalyst layer retention ratio × 100. It should be noted that the above parameters are merely illustrative and are not intended to limit the scope of this application.

[0051] In alternative implementations, such as Figure 5 As shown, the catalytic activity decay index generated based on the electrochemical data includes: S410: Extract current data from the non-Radida interval of the electrochemical data and calculate the difference in charging current at different scan rates. Calculate the double-layer capacitance based on the linear relationship between the scan rate and the charging current difference to obtain the electrochemical active area.

[0052] S420: Calculate the electrochemical active area retention rate at different time points and fit the decay time constant.

[0053] S430: Generate catalytic activity decay index based on decay time constant.

[0054] Specifically, current data within the non-Faraday region of the electrochemical data were extracted, and the charging current difference at different scan rates was calculated. Electrochemical data was acquired through cyclic voltammetry using a three-electrode system. The electrode under test (DUT) was used as the working electrode, with the reference and counter electrodes immersed in the electrolyte. Cyclic voltammetry was performed by cyclically scanning the potential of the working electrode at a set rate, recording the current-potential variation curve. The potential scan range includes both the Faraday reaction region and the non-Faraday region. In the Faraday reaction region, redox reactions occur, and the current is mainly contributed by the Faraday current. In the non-Faraday region, no significant redox reactions occur, and the current is mainly contributed by the charging current of the electrode surface double layer. Selecting current data within the non-Faraday region for calculation eliminates interference from Faraday reactions and accurately reflects the magnitude of the double-layer capacitance. For each scan rate, the anodic and cathodic currents within the non-Faraday region were extracted, and the average of their absolute values ​​was taken as the charging current difference at that scan rate. Multiple different scan rates were set during the test, resulting in multiple charging current difference data points. During cyclic voltammetry testing, the three-electrode system is arranged at a fixed interval, with the working electrode in the middle, the reference electrode close to the surface of the working electrode, and the counter electrode opposite to the working electrode to ensure uniform electric field distribution. Before testing, the electrodes are left to stand in the electrolyte for a period of time to allow the electrode surface to be fully wetted and the potential to stabilize before starting the scan. Multiple cycles are repeated at each scan rate, and the stable data from the last few cycles are used for calculation.

[0055] The electrochemical active area is calculated by the bilayer capacitance based on the linear relationship between the scan rate and the difference in charging current. There is a linear positive correlation between the bilayer capacitance and the scan rate; the faster the scan rate, the larger the difference in charging current. Multiple sets of test data are plotted as a scatter plot with the scan rate on the x-axis and the charging current difference on the y-axis. Linear fitting of the scatter plot yields a straight line whose slope represents the bilayer capacitance. The size of the bilayer capacitance is directly proportional to the electrochemical active area of ​​the electrode. A unit area of ​​electrode corresponds to a fixed standard specific capacitance. Dividing the measured bilayer capacitance by the standard specific capacitance yields the electrochemical active area. The electrochemical active area is the effective area of ​​the electrode surface capable of participating in electrochemical reactions.

[0056] The electrochemical active area retention rate at different time points was calculated, and the decay time constant was obtained by fitting the data. Using the electrochemical active area measured in the initial state as a baseline, the electrochemical active area measured at each time point was divided by the initial area to obtain the electrochemical active area retention rate at the corresponding time point. As the flushing time increases, the electrochemical active area retention rate gradually decreases, and the decrease follows an exponential decay law. The retention rate data at each time point were arranged in chronological order, and an exponential function was used for fitting. The resulting decay time constant is used to characterize the rate of activity decay. A larger decay time constant indicates a slower decrease in the electrochemical active area and a stronger ability to maintain the performance of the catalyst layer. A catalytic activity decay index was generated based on the decay time constant to quantify the degree of damage to the functional layer of the catalyst layer. This index is directly related to the actual performance of the electrode and can be used to evaluate the electrode's lifespan.

[0057] In a specific example, current data within the non-Faraday region of the electrochemical data is extracted. This includes: performing cyclic voltammetry on the electrode under test using a three-electrode system. The three-electrode system uses the electrode under test as the working electrode, a mercuric oxide electrode as the reference electrode, and a platinum mesh or platinum wire electrode as the counter electrode. The electrolyte is a 30wt% potassium hydroxide solution. The cyclic voltammetry parameters are set as follows: initial potential 0V, maximum potential 0.6V, minimum potential 0V, and scan rates successively set to 10mV / s, 20mV / s, 50mV / s, and 100mV / s. Each scan rate is tested for 5 cycles, with a rest time of 2s. The non-Faraday region is the area with potentials between 0.1V and 0.2V, corresponding to the double-layer charging region. For the test data at each scan rate: current data within the potential range of 0.1V to 0.2V is extracted; the anolyte current I within this range is obtained. a and cathode current I c According to the formula Δi=(|I a |+|I c The charging current difference is calculated using |) / 2. When calculating the electrochemical active area of ​​the double-layer capacitor, the scanning rates v are used... i Using the charging current difference Δi as the independent variable and the corresponding charging current difference as the dependent variable, a scatter plot is constructed and linearly fitted to obtain the linear equation y=kx+b, where the slope k is the double-layer capacitor C. dl The formula for calculating the electrochemical active area (ECSA) is: ECSA = C dl / C ref C ref The standard specific capacitance constant is used. When calculating the retention rate of the electrochemical active area at different time points and fitting the decay time constant, the electrochemical active area E at different time points is obtained. CSA (t), calculate the electrochemical active area retention rate R(t) = E CSA (t) / E CSA 0, where E CSA0 represents the initial electrochemical active area; a time-electrochemical active area retention curve is established, and the decay time constant τ is obtained by exponential fitting of the curve. The exponential fitting formula is y = a × e (-x / τ) The formula for calculating the catalytic activity attenuation index based on the aforementioned attenuation time constant is: S2 = (τ / T) ref )×100, where T ref The preset lifespan baseline time is set to 200 hours.

[0058] It should be noted that the above parameters are merely illustrative and are not intended to limit the scope of this application.

[0059] In alternative implementations, such as Figure 6 As shown, the generation of quality loss change indicators based on the quality data includes: S510: Calibrate the baseline mass of the blank matrix to obtain the initial catalyst mass of the electrode under test.

[0060] S520: Calculate the corresponding cumulative mass loss based on the current mass of the electrode under test at each time point.

[0061] S530: Generates a time-cumulative mass loss curve, extracts the steady-state mass loss rate from the curve, and generates a mass loss change index based on the steady-state mass loss rate.

[0062] Specifically, a baseline mass of the blank substrate is calibrated to obtain the initial catalyst mass of the electrode under test. The blank substrate is an electrode substrate made of the same material, size, and fabrication process as the electrode under test, without a catalyst layer on its surface. It is used to subtract the mass of the substrate itself to obtain the mass of the pure catalyst layer. To calibrate the blank substrate mass, it is first dried in a vacuum oven to remove adsorbed moisture and impurities. After drying, it is cooled to room temperature before weighing. This drying and weighing process is repeated multiple times, and the average of the multiple weighings is taken as the baseline mass of the blank substrate to reduce the impact of weighing errors. The initial mass of the electrode under test is obtained using the same drying and weighing process. The initial catalyst mass, i.e., the total mass of the catalyst layer before rinsing, is obtained by subtracting the baseline mass of the blank substrate from the initial mass of the electrode under test. During the mass weighing process, the temperature of the vacuum oven is set to a fixed value, and the drying time is kept consistent to ensure that the degree of drying is the same each time. After drying, the electrode is placed in a desiccator to cool to room temperature to avoid adsorption of moisture from the air during the cooling process. Weighing was performed using a high-precision weighing instrument, with each weighing repeated multiple times and the average value taken as the final result to minimize the impact of weighing errors. The time between removing the electrodes from the oven and completing the weighing was minimized to reduce exposure to air and prevent moisture absorption from affecting weighing accuracy. After weighing, the electrodes were reinstalled in the test apparatus, adjusted to their original positions, and the flushing test continued.

[0063] The cumulative mass loss is calculated based on the current mass of the electrode under test at each time point. After each rinsing cycle, the electrode under test is removed from the test apparatus, rinsed with deionized water to remove residual electrolyte, and then dried in a vacuum oven under the same conditions. After cooling to room temperature, the current total mass is weighed. The current remaining catalyst mass is obtained by subtracting the reference mass of the blank substrate from the current total mass. The cumulative mass loss at that time point is obtained by subtracting the current remaining catalyst mass from the initial catalyst mass. The cumulative mass loss gradually increases over time, reflecting the continuous shedding of the catalyst layer.

[0064] A cumulative mass loss curve is generated, and the steady-state mass loss rate is extracted. The test data at each time point are plotted as a curve with time on the x-axis and cumulative mass loss on the y-axis. The cumulative mass loss curve typically exhibits a two-stage characteristic: initially, the mass loss rate is rapid, corresponding to the weakest bonding areas of the catalyst layer and surface protrusions that detach first, known as the rapid loss stage; after a period of time, the mass loss rate gradually stabilizes, and the mass loss per unit time remains relatively constant, known as the steady-state loss stage. The mass loss per unit time within the steady-state stage is calculated to obtain the steady-state mass loss rate. This parameter reflects the long-term detachment rate of the catalyst layer under stable scouring conditions and is an important parameter for evaluating the bonding strength and scouring resistance of the catalyst layer. Based on the steady-state mass loss rate, a mass loss change index is generated to quantify the degree of damage to the catalyst layer at the macroscopic level; the lower the steady-state loss rate, the better the long-term scouring resistance of the catalyst layer.

[0065] Optionally, generating the mass loss change index based on the steady-state mass loss rate further includes: calculating the weight loss curve score based on the steady-state mass loss rate, wherein the weight loss curve score is calculated using the formula: Weight loss curve score S3 = max(0, (1-v) / 2) steady / v threshold ))×100, where v steady Let v be the steady-state mass loss rate. threshold The mass loss rate threshold is a preset value, expressed in mg / (cm²·h). This threshold is pre-set based on the electrode substrate type and the catalyst layer system. For example, for an electrode system with a nickel foam substrate supporting a Ni-Mo-Co ternary catalyst layer, the v... threshold The value is set to 0.5 mg / (cm²·h); when the steady-state mass loss rate is greater than or equal to the mass loss rate threshold, the weight loss curve score is 0; when the steady-state mass loss rate is 0, the weight loss curve score is 100; a mass loss change index is generated based on the weight loss curve score.

[0066] In alternative implementations, such as Figure 7As shown, a gas-liquid two-phase bubble flow is generated by a test device capable of simulating the gas scouring environment of electrodes in an electrolytic cell. The surface of the catalyst layer of the electrode under test, which is fixedly positioned, is scouring by the gas-liquid two-phase bubble flow, including: S610: Compressed gas and alkaline electrolyte are passed into a gas-liquid two-phase static mixer for thorough mixing.

[0067] S620: A high-speed liquid flow carrying bubbles is output through the Venturi contraction tube at the outlet of the gas-liquid two-phase static mixer to form a gas-liquid two-phase bubble flow.

[0068] S630: Adjusts the ratio of gas flow rate to liquid flow rate to control the size and density of bubbles.

[0069] S640: Adjust the position and angle of the electrode to be tested so that the gas-liquid two-phase bubble flow impacts the surface of the catalyst layer perpendicularly.

[0070] Specifically, compressed gas and alkaline electrolyte are introduced into a gas-liquid two-phase static mixer for thorough mixing. The compressed gas is supplied by a gas supply unit, which includes a gas source, pressure regulating components, and flow metering components, providing gas with stable pressure and flow rate. The alkaline electrolyte is supplied by a liquid supply unit, which includes a storage tank, pump, and flow metering components, providing electrolyte with a stable flow rate and capable of heating the electrolyte to control its temperature within a target range. Gas and liquid pipelines are connected to the inlets of the gas-liquid two-phase static mixer, allowing both gas and liquid to enter simultaneously. The gas-liquid two-phase static mixer contains a spiral mixing component. As the fluid flows through this component, its flow direction continuously changes, generating shear and convection, breaking the gas phase into fine bubbles and uniformly dispersing them in the liquid phase, achieving thorough mixing of the two phases. The gas-liquid two-phase static mixer has no moving parts; mixing is achieved solely by the fluid's own flow, ensuring stable and reliable operation, making it suitable for long-term continuous testing. During the generation of the gas-liquid two-phase bubble flow, the electrolyte is supplied in a circulating manner. The electrolyte in the storage tank is pumped to the gas-liquid two-phase static mixer, where it mixes with the gas and washes the electrode surface before flowing into the collection tank below. The electrolyte in the collection tank returns to the storage tank through a return pipeline, forming a closed-loop circulation loop. A filter component is installed in the circulation loop to filter out large particles of catalyst layer debris that detach from the electrolyte, preventing these debris from impacting the electrode surface again and causing additional damage. This also prevents debris from clogging the pipeline and mixer. The electrolyte temperature is controlled by a heating element and a temperature detection element located in the storage tank. The temperature detection element monitors the electrolyte temperature in real time. When the temperature falls below the set value, the heating element is activated to raise the temperature; when the temperature reaches the set value, heating stops. This closed-loop control stabilizes the electrolyte temperature within the target range, keeping temperature fluctuations within a small range to avoid interference with the element dissolution rate and electrochemical test results.

[0071] A high-speed liquid flow carrying air bubbles is output through the Venturi constrictor at the outlet of the gas-liquid two-phase static mixer, forming a gas-liquid two-phase bubble flow. The outlet of the gas-liquid two-phase static mixer adopts a constricting Venturi constrictor, with a gradually decreasing cross-sectional area. As the fluid flows through the constriction section, the flow velocity increases and the pressure decreases. The high-speed flowing liquid further shears the bubbles, breaking large bubbles into smaller microbubbles, while simultaneously increasing the ejection velocity of the liquid flow, forming a high-speed impacting gas-liquid two-phase bubble flow. The cross-sectional area of ​​the outlet directly affects the outlet velocity and the effective area of ​​the bubble flow. The outlet structure of the appropriate specification can be selected according to the test area of ​​the electrode under test to ensure that the bubble flow can completely cover the test area of ​​the electrode.

[0072] The size and density of bubbles are controlled by adjusting the ratio of gas to liquid flow rates. The average size and number density of bubbles are determined by the gas-liquid flow rate ratio. With the liquid flow rate kept constant, increasing the gas flow rate increases the gas content per unit volume of liquid, increasing bubble density and average bubble size; conversely, decreasing the gas flow rate decreases bubble density and average size. By adjusting the flow control valves in the gas and liquid paths respectively, the gas-liquid flow rate ratio can be flexibly adjusted to control bubble size and density within the target range, simulating bubble environments under different operating conditions. The flow rate remains stable during the test to ensure consistent scouring intensity across different time periods.

[0073] The position and angle of the electrode under test are adjusted to ensure that the gas-liquid two-phase bubble flow impacts the catalyst layer surface perpendicularly. The electrode is mounted on an adjustable fixing structure with height and angle adjustment functions. Height adjustment changes the distance between the electrode and the mixer outlet, thereby adjusting the flow velocity and effective area of ​​the bubble flow upon reaching the electrode surface. Angle adjustment adjusts the angle between the normal direction of the electrode surface and the direction of the bubble flow. During adjustment, the angle value is confirmed by the angle indicator component, and the height value is confirmed by the position scale. After adjusting the electrode to the preset position, it is locked and fixed to ensure that the electrode maintains a stable posture during the scouring process. Ensuring that the bubble flow impacts the catalyst layer surface perpendicularly ensures that the scouring intensity is uniform across all areas of the electrode surface, avoiding excessively strong or weak scouring in certain areas due to angle tilt, and ensuring the accuracy of the test results.

[0074] like Figure 8As shown, in a specific example, compressed air enters the device through compressed air inlet 1, and 30wt% potassium hydroxide alkaline electrolyte enters the device through liquid inlet 2. After initial contact inside the bubble generator body 3, the gas and liquid phases are fed into the gas-liquid two-phase static mixer 4 for thorough mixing. The gas-liquid two-phase static mixer is equipped with a spiral mixing component, which, through the shearing and convection of the fluid, disperses the gas phase into fine bubbles and uniformly disperses them in the liquid phase. The gas-liquid mixed fluid enters the Venturi constriction tube 5 from the outlet of the gas-liquid two-phase static mixer. This tube adopts a constriction-type Venturi constriction tube, and the flow velocity increases sharply when the fluid flows through the constriction section, further shearing the bubbles into micron-sized bubbles, and finally ejecting a high-speed bubble stream 6 from the outlet, impacting the surface of the electrode under test. The electrode under test 7 is mounted on the electrode fixing device 8. The vertical distance between the electrode and the outlet of the Venturi constriction tube is adjusted by the height adjustment screw 18. The angle adjustment device 19 adjusts the angle between the normal direction of the electrode surface and the direction of the bubble flow to ensure that the high-speed bubble flow impacts the catalyst layer surface perpendicularly. The anti-splash baffle 21 is installed around the electrode to prevent liquid splashing. The gas-liquid mixture after impacting the electrode falls into the waste liquid collection tank 12 at the bottom. The electrolyte in the collection tank returns to the storage tank through the return pipeline, forming a closed loop. A filter component is installed in the loop to filter out the detached catalyst layer fragments to prevent them from impacting the electrode surface again and causing additional damage. The control panel 13 is used to set and monitor operating parameters such as flushing time, gas-liquid flow rate, and electrolyte temperature. The flushing time can be set to continuous or periodic mode according to the test requirements.

[0075] like Figure 9The electrode fixing device is shown. The electrode 7 to be tested is placed between clamping plates 15. The clamping plates 15 are made of 316L stainless steel and have dimensions of 50×50×10mm. The surface has anti-slip texture 16 to increase friction. The electrode is clamped by applying a preload of 5-10 N·m through fixing bolts 17. The fastening handle 23 is used for manual quick locking or loosening of the clamps, facilitating electrode installation and removal. The height adjustment screw 18 adopts a precision lead screw structure with an adjustment accuracy of 0.1mm and an adjustment range of ±20mm. It is used to change the vertical distance between the electrode and the outlet of the Venturi constriction tube, thereby adjusting the flow velocity and effective area of ​​the bubble flow when it reaches the electrode surface. The guide rail 20 is a linear guide rail, ensuring that the electrode moves only in the vertical direction and preventing lateral displacement. The angle adjuster 19 adopts a universal joint structure to adjust the angle between the normal direction of the electrode surface and the direction of the bubble flow, so that the high-speed bubble flow impacts the catalyst layer surface perpendicularly. After the angle is adjusted, it is fixed by a locking mechanism to prevent the angle from shifting during the scouring process. A position scale 24 is located on the side of the guide rail to quantify the height position of the electrode; an angle indicator 25 is located next to the angle adjuster 19 to quantify the deflection angle of the electrode. The operator confirms the adjustment values ​​using the position scale and angle indicator, adjusts the electrode to the preset position, and then locks it in place to ensure the consistency and reproducibility of the test conditions. A splash guard 21 is installed around the electrode to prevent electrolyte splashes from high-speed liquid flow impacting the electrode surface from affecting surrounding equipment or causing injury. The base 22 serves as the load-bearing base for the entire fixing device, ensuring the stability of the device during the flushing process.

[0076] In alternative implementations, such as Figure 10 As shown, the trend change data of gas erosion damage to the electrode catalyst layer based on each individual index include: S710: Determine the weighting coefficients for each of the following indicators: element dissolution correlation index, catalyst layer morphology retention index, catalytic activity decay index, and mass loss change index.

[0077] S720: The comprehensive evaluation value is obtained by multiplying each individual indicator by its corresponding weight coefficient and then summing the results.

[0078] S730: Based on the time sequence of the comprehensive evaluation values, generate trend data of gas erosion damage to the electrode catalyst layer.

[0079] Specifically, the weighting coefficients for elemental dissolution correlation indicators, catalyst layer morphology retention indicators, catalytic activity decay indicators, and mass loss change indicators are determined. These weighting coefficients reflect the importance of different individual indicators in the comprehensive evaluation; higher weight indicators have a greater impact on the final result. The weighting coefficients can be determined in various ways, based on research experience in relevant fields or optimized through statistical analysis of multiple sets of test data. When using statistical analysis, the variance distribution of each indicator can be analyzed to distinguish the discriminative power of different indicators. Indicators with higher discriminative power are assigned higher weights, and those with lower discriminative power are assigned lower weights, allowing the comprehensive evaluation results to more clearly differentiate the performance differences of different electrodes. The weighting process considers both early damage identification and overall performance assessment, assigning corresponding weights to indicators reflecting early damage and indicators reflecting macroscopic performance to ensure the comprehensiveness of the comprehensive evaluation. The weight optimization process can be iterative, first giving initial weights, then substituting multiple sets of electrode test data with known performance for verification, adjusting the weight allocation based on the verification results until the comprehensive evaluation result matches the actual performance best, thus completing the weight optimization.

[0080] The comprehensive evaluation value is obtained by multiplying each individual indicator by its corresponding weight coefficient and then summing the results. Before weighted calculation, each individual indicator is normalized to unify indicators with different dimensions and value ranges into the same numerical interval, eliminating the influence of dimensional differences. Normalization can be performed using linear normalization, mapping the indicator values ​​to the interval between zero and one; higher values ​​indicate better erosion resistance. Alternatively, extreme value normalization can be used. For positive indicators (i.e., higher values ​​indicate better performance), the actual value is subtracted from the minimum value, and then divided by the difference between the maximum and minimum values ​​to obtain a normalized value between zero and one. For negative indicators (i.e., higher values ​​indicate worse performance), a reverse transformation is performed before normalization. Through normalization, all individual indicators are mapped to the same numerical interval, exhibiting additivity and allowing for direct weighted calculation. The normalized individual indicator values ​​are then multiplied by their corresponding weight coefficients, and all products are summed to obtain the final comprehensive evaluation value.

[0081] Based on the time-varying sequence of comprehensive evaluation values, trend data of gas erosion damage to the electrode catalyst layer is generated. The comprehensive evaluation values ​​corresponding to each time point are arranged chronologically to form a time series of the comprehensive evaluation. This time series is smoothed to eliminate interference from random fluctuations, resulting in a continuous damage change curve, which is the damage trend data. The trend data clearly presents the overall trend of damage development and can be used to identify different stages of damage development and predict the subsequent rate of damage progression. The trend data can also be used to extrapolate the time when the electrode reaches the failure threshold, thus assessing the expected service life of the electrode.

[0082] After generating trend data on gas erosion damage to the electrode catalyst layer based on the change sequence of the comprehensive evaluation values ​​over time, the method further includes determining the evaluation level of the catalyst layer's resistance to gas erosion based on the change sequence of the comprehensive evaluation values ​​over time. The evaluation level is divided into five levels according to the range of the comprehensive evaluation values: Level 1: Comprehensive evaluation value ≥ 0.8 and ≤ 1.0, indicating excellent resistance to gas erosion of the catalyst layer; Level 2: Comprehensive evaluation value ≥ 0.6 and < 0.8, indicating good resistance to gas erosion of the catalyst layer; Level 3: Comprehensive evaluation value ≥ 0.4 and < 0.6, indicating average resistance to gas erosion of the catalyst layer; Level 4: Comprehensive evaluation value ≥ 0.2 and < 0.4, indicating poor resistance to gas erosion of the catalyst layer; Level 5: Comprehensive evaluation value ≥ 0.0 and < 0.2, indicating extremely poor resistance to gas erosion of the catalyst layer.

[0083] In alternative implementations, such as Figure 11 As shown, the points of abrupt change in topological indices over time include: S810: Set the size of the sliding window and extract consecutive topology index data groups in chronological order.

[0084] S820: Calculate the network density corresponding to each data group and compare the network density of the current data group with the network density of the previous data group.

[0085] S830: When the decrease rate exceeds the preset threshold, the current time point is determined to be a mutation point of the topology index.

[0086] Specifically, a sliding window size is set, and consecutive sets of topological index data are extracted sequentially according to time. Sliding window analysis is a time-series data processing method that moves a fixed-length window frame by frame along the time axis, performing statistical analysis on the data within each window segment to identify abrupt changes. The window size refers to the number of time nodes contained in a single window; the size of the window affects the sensitivity and anti-interference capability of abrupt change detection. If the window size is too small, the amount of data within the window is insufficient, making it susceptible to random fluctuations in a single test and leading to misjudgments; if the window size is too large, the time range covered by the window is too long, reducing the temporal resolution of the detection and making it impossible to identify abrupt changes in a timely manner. In practical applications, an appropriate window size can be selected based on the interval between time nodes and the total test duration, balancing detection sensitivity and anti-interference capability. The window starts from the first time node and moves forward one time node at a time, extracting multiple consecutive sets of data until the entire test time is covered.

[0087] Calculate the network density for each data set. For each time period corresponding to a sliding window, recalculate the correlation coefficients between elements based on the electrolyte element concentration data within that time period, and construct the element association network for that window. Network density is a parameter that measures the tightness of network connections. It is calculated as the ratio of the actual number of associated edges in the network to the total number of all possible associated edges. The value of network density ranges from zero to one. The closer the value is to one, the tighter the connections between nodes and the stronger the consistency of element dissolution behavior; the closer the value is to zero, the sparser the connections and the more obvious the differentiation of element dissolution behavior. Calculate the network density for each sliding window sequentially to obtain a sequence of network density changes over time. In a specific example, the formula for calculating the network density is D=E / (N×(N-1) / 2), where D is the network density, E is the actual number of associated edges in the element association network, and N is the number of element nodes. The value of network density ranges from 0 to 1. The closer the value is to 1, the stronger the consistency of element dissolution behavior; the closer the value is to 0, the more obvious the differentiation of element dissolution behavior.

[0088] The network density of the current data set is compared with that of the previous data set. When the decrease ratio exceeds a preset threshold, the current time point is determined to be a mutation point in the topology index. As damage to the catalyst layer gradually accumulates, the density of the element association network gradually decreases. When the damage accumulates to a critical level, the network structure will change significantly, and the network density will drop sharply, indicating substantial damage to the internal structure of the corresponding catalyst layer. By comparing the network density of adjacent windows, the time point of sharp density decrease can be identified. A preset decrease ratio threshold is set. When the decrease ratio of the network density of the current window compared to the previous window exceeds this threshold, the time point corresponding to the current window is determined to be a mutation point in the topology index. The threshold value can be adjusted according to the test accuracy requirements. The lower the threshold, the higher the detection sensitivity and the easier it is to identify slight structural changes; the higher the threshold, the stronger the anti-interference ability and the more obvious the structural mutation.

[0089] Optionally, after identifying the abrupt change point of the topology index over time, the method further includes: generating an elemental dissolution correlation index based on the time corresponding to the abrupt change point and the selective dissolution coefficient, and then calculating the elemental correlation network score; when the abrupt change point of the topology index over time is identified, the calculation formula for the elemental correlation network score is: Elemental correlation network score S4 = (1-t) / (t) mutation / t max )×100,t mutation t represents the time corresponding to the mutation point. max The total test time is defined as S4 = 100 when no abrupt change in the topology index is detected over time. Element dissolution correlation index is generated based on the element correlation network score.

[0090] In a specific example, the size of the sliding window is 2 to 5 time nodes, preferably 3 time nodes; the preset decrease ratio threshold ranges from 20% to 40%, preferably 30%. Those skilled in the art can set it according to actual needs, and this application does not limit it in this regard.

[0091] Another aspect of this application discloses a gas erosion damage detection system for the electrode catalyst layer of an electrolyzer, such as... Figure 12 As shown, it includes a test apparatus and a gas erosion damage detection device for the electrolytic cell electrode catalyst layer.

[0092] The electrolytic cell electrode catalyst layer gas erosion damage detection device includes a gas erosion module, a data acquisition module, and a trend analysis module.

[0093] The gas flushing module 31 is used to generate a gas-liquid two-phase bubble flow through a test device that can simulate the gas flushing environment of the electrode in the electrolytic cell, and to flush the surface of the catalyst layer of the fixed electrode under test with the gas-liquid two-phase bubble flow.

[0094] The data acquisition module 32 is used to collect experimental data such as electrolyte element concentration data, surface image data, electrochemical data, and mass data corresponding to the electrode under test at multiple time points during the rinsing process.

[0095] The trend analysis module 33 is used to determine the trend change data of gas erosion damage to the electrode catalyst layer based on the experimental data.

[0096] Based on the same principle, specific embodiments of the system in this application can be found in the implementation of the method, and will not be described in detail here.

[0097] The parameters mentioned above in this application are all exemplary, and those skilled in the art can set them according to actual needs. This application does not limit them in this regard.

[0098] This application uses specific embodiments to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the methods and ideas of this application. At the same time, for those skilled in the art, there may be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode, characterized in that, include: A gas-liquid two-phase bubble flow is generated by an experimental device that can simulate the gas scouring environment of electrodes in an electrolytic cell, and the gas-liquid two-phase bubble flow is used to scour the surface of the catalyst layer of the electrode under test that is fixedly set up. Experimental data, including electrolyte element concentration data, surface image data, electrochemical data, and mass data, were collected at multiple time points during the rinsing process for the electrode under test. Based on the experimental data, determine the trend of gas erosion damage to the electrode catalyst layer; The trend of gas erosion damage to the electrode catalyst layer determined based on the experimental data includes: Element dissolution correlation indicators are generated based on the electrolyte element concentration data; A catalytic layer morphology retention index is generated based on the surface image data, and a catalytic activity decay index is generated based on the electrochemical data. Based on the quality data, a mass loss change index is generated, and based on each individual index, trend change data of gas erosion damage to the electrode catalyst layer is generated. The elemental dissolution correlation index generated based on the electrolyte elemental concentration data includes: The concentration ratio of active elements to framework elements at each time point is calculated to obtain the selective dissolution coefficient; The correlation coefficients between elements are calculated based on the time series data of element concentrations, and the associated edges between element nodes are established to construct an element association network. The element association network is a topological network constructed with metal elements as nodes and the time series correlation of element concentrations as the basis for connecting edges. Calculate the topological index of the element association network, identify the abrupt change points of the topological index over time, and generate element leaching association index based on the time corresponding to the abrupt change point and the selective leaching coefficient.

2. The method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode according to claim 1, characterized in that, The catalytic layer morphology retention index generated based on the surface image data includes: The surface image data is converted to grayscale and denoised to obtain a preprocessed image; The preprocessed image was divided into three regions using a dual threshold segmentation method: metal exposure area, pit area, and catalyst layer intact area. The pixel percentage of the three types of regions is calculated to obtain the catalyst layer retention ratio, and the catalyst layer morphology retention index is generated based on the catalyst layer retention ratio.

3. The method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode according to claim 1, characterized in that, Based on the aforementioned electrochemical data, catalytic activity attenuation indicators are generated, including: Extract current data from non-Radida intervals in electrochemical data, calculate the difference in charging current at different scan rates, and calculate the double-layer capacitance based on the linear relationship between scan rate and charging current difference to obtain the electrochemical active area. The electrochemical active area retention rate at different time points was calculated, and the decay time constant was obtained by fitting. Catalytic activity decay index is generated based on decay time constant.

4. The method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode according to claim 1, characterized in that, The quality loss change index generated based on the quality data includes: The baseline mass of the blank matrix is ​​determined to obtain the initial catalyst mass of the electrode to be tested; Calculate the corresponding cumulative mass loss based on the current mass of the electrode under test at each time point; Generate a time-cumulative mass loss curve, extract the steady-state mass loss rate from the curve, and generate a mass loss change index based on the steady-state mass loss rate.

5. The method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode according to claim 1, characterized in that, A gas-liquid two-phase bubble flow is generated using a test device capable of simulating the gas scouring environment of electrodes in an electrolytic cell. The gas-liquid two-phase bubble flow is then used to scour the surface of the catalyst layer of the electrode under test, which is fixedly positioned therein. Compressed gas and alkaline electrolyte are passed into a gas-liquid two-phase static mixer for thorough mixing; A high-speed liquid flow carrying bubbles is output through the Venturi contraction tube at the outlet of the gas-liquid two-phase static mixer, forming a gas-liquid two-phase bubble flow. Adjusting the ratio of gas flow rate to liquid flow rate controls the size and density of bubbles; Adjust the position and angle of the electrode to be tested so that the gas-liquid two-phase bubble flow impacts the surface of the catalyst layer perpendicularly.

6. The method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode according to claim 1, characterized in that, The trend change data of gas erosion damage to the electrode catalyst layer based on each individual indicator includes: Determine the weighting coefficients for element dissolution correlation index, catalyst layer morphology retention index, catalytic activity decay index, and mass loss change index. The comprehensive evaluation value is obtained by multiplying each individual indicator by its corresponding weight coefficient and then summing the results. Based on the time sequence of the comprehensive evaluation values, trend data of gas erosion damage to the electrode catalyst layer are generated.

7. The method for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode according to claim 1, characterized in that, Identifying abrupt changes in topological metrics over time includes: Set the size of the sliding window and extract consecutive topological index data groups in chronological order; Calculate the network density for each data group and compare the network density of the current data group with that of the previous data group; When the decrease rate exceeds the preset threshold, the current time point is determined to be a point of inflection for the topological index.

8. A system for detecting gas erosion damage to the catalyst layer of an electrolytic cell electrode, characterized in that, Includes testing equipment and a device for detecting gas erosion damage to the electrode catalyst layer of an electrolytic cell; The electrolytic cell electrode catalyst layer gas erosion damage detection device includes a gas erosion module, a data acquisition module, and a trend analysis module. The gas flushing module is used to generate a gas-liquid two-phase bubble flow through a test device that can simulate the gas flushing environment of the electrodes in an electrolytic cell, and to flush the surface of the catalyst layer of the fixed electrode under test with the gas-liquid two-phase bubble flow. The data acquisition module is used to collect experimental data such as electrolyte element concentration data, surface image data, electrochemical data, and mass data corresponding to the electrode under test at multiple time points during the rinsing process. The trend analysis module is used to determine the trend change data of gas erosion damage to the electrode catalyst layer based on the experimental data; The trend of gas erosion damage to the electrode catalyst layer determined based on the experimental data includes: Element dissolution correlation indicators are generated based on the electrolyte element concentration data; A catalytic layer morphology retention index is generated based on the surface image data, and a catalytic activity decay index is generated based on the electrochemical data. Based on the quality data, a mass loss change index is generated, and based on each individual index, trend change data of gas erosion damage to the electrode catalyst layer is generated. The elemental dissolution correlation index generated based on the electrolyte elemental concentration data includes: The concentration ratio of active elements to framework elements at each time point is calculated to obtain the selective dissolution coefficient; The correlation coefficients between elements are calculated based on the time series data of element concentrations, and the associated edges between element nodes are established to construct an element association network. The element association network is a topological network constructed with metal elements as nodes and the time series correlation of element concentrations as the basis for connecting edges. Calculate the topological index of the element association network, identify the abrupt change points of the topological index over time, and generate element leaching association index based on the time corresponding to the abrupt change point and the selective leaching coefficient.

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