Design method and preparation process of gradient structure porous transmission layer of AEM electrolytic cell

Abstract

The invention relates to a gradient structure porous transmission layer design method of an AEM electrolytic cell and a preparation process thereof, and aims at a core business scene problem that a pore structure of the porous transmission layer of the AEM electrolytic cell affects gas and liquid transmission efficiency, through fusing a logic process of pore characteristic analysis, model optimization and physical verification, a porous transmission layer is obtained. A set of systematic solution is provided; through multi-link collaborative optimization, the transmission efficiency and stability of the porous transmission layer are remarkably improved, the deviation in practical application is reduced, and innovative technical support is provided for improvement of the performance of the AEM electrolytic cell; according to the AEM electrolytic cell porous transmission layer prepared through the technical steps, non-noble metal is deposited on nickel felt or porous nickel, so that the porosity of the porous transmission layer is improved, the contact resistance between the porous transmission layer and a membrane electrode is reduced, the activity of an electrolytic cell is improved, and the purpose of improving the performance of the electrolytic cell is achieved.

Description

Technical Field

[0001] This invention relates to the field of AEM electrolysis for hydrogen production technology, and in particular to a gradient structure porous transport layer design method and its preparation process for an AEM electrolyzer. Background Technology

[0002] As a highly efficient and low-cost hydrogen production technology, the AEM electrolyzer plays a crucial role in the large-scale utilization of renewable energy, and its performance directly affects the economics and reliability of green hydrogen production. The porous transport layer, located between the catalyst layer and the flow field plate, undertakes multiple transport tasks involving water, gas, and electrons, and is one of the core components determining the overall efficiency and stability of the electrolyzer.

[0003] Most current porous transport layers employ a uniform pore structure, a design that has revealed significant shortcomings in practical operation. The region near the catalyst layer requires sufficiently small pores to ensure uniform gas and liquid distribution, preventing excessive local reaction concentration that could lead to uneven current distribution and hotspot formation. Conversely, the side near the flow field plate requires sufficiently large pores to quickly expel the large amounts of oxygen and unreacted water generated, preventing blockage of transport channels and pressure buildup. A uniform structure cannot simultaneously meet these drastically different requirements, resulting in chaotic material flow paths within the transport layer. Gas tends to stagnate in the fine pore region, forming bubbles, while liquid struggles to effectively permeate the coarse pore region.

[0004] This conflicting requirement for porosity characteristics at the input and output ends creates the core contradiction in transport layer design: the pore size must simultaneously ensure uniform contact and low-resistance transport. Pores that are too small significantly increase gas exhaust resistance, causing oxygen to accumulate within the transport layer, resulting in localized water film thickening and obstruction of reaction sites. Pores that are too large, on the other hand, weaken the uniformity of the water film near the catalyst layer, leading to insufficient reactant supply in some areas and severely uneven current density distribution. This conflict prevents a single pore size from achieving efficient synergy across the entire thickness direction, ultimately limiting the long-term stable operation of the electrolyzer at high current densities.

[0005] How to achieve a continuous gradient change in pore size from the catalyst layer side to the flow field plate side in the porous transport layer, while precisely controlling the pore size range and porosity value at each location, and ensuring that gas and liquid can pass through in an orderly manner along the expected path without significant blockage or distribution imbalance, has become a key issue in improving the overall performance of the AEM electrolyzer. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a gradient structure porous transport layer design method and preparation process for an AEM electrolyzer, which can solve the problem that there is a contradiction between the discharge of liquid phase reactants and gas phase products in traditional porous transport layers with uniform pore size, which easily leads to gas blockage and increases concentration polarization.

[0007] To solve the above-mentioned technical problems, the technical solution of the present invention is: a gradient structure porous transport layer design method for AEM electrolytic cells, the innovation of which is as follows: Image data of the initial uniform pore structure of the porous transport layer were obtained by scanning electron microscopy. Image processing algorithms were used to analyze the pore size distribution and porosity values ​​to obtain the set of initial pore characteristic parameters. Based on the initial set of pore characteristic parameters, a thickness-direction model from the catalyst layer to the flow field plate is constructed using the finite element simulation algorithm to simulate the gas and liquid transport paths and determine the location coordinates of potential blockage areas. If the location coordinates of the potential blockage area exceed the preset threshold, the pore size parameters in the model are adjusted to achieve a continuous gradient distribution from small to large, resulting in an optimized pore gradient model. By using the optimized pore gradient model, data curves of gas flow velocity and liquid permeability are obtained, and it is determined whether these data curves meet the requirements of ordered transport, thus obtaining the verification result indicators. Based on the verification results, a porous transport layer sample was generated using 3D printing technology and incorporated into a gradient pore structure to obtain a physical sample entity. Electrochemical impedance spectroscopy was performed on the physical sample to obtain actual transmission resistance and stability data, and to determine the deviation from the simulation model. If the deviation value is lower than the preset threshold, the actual transmission resistance and stability data are integrated into the finite element simulation algorithm to update the pore gradient model parameters and obtain the final design scheme data.

[0008] An innovative process for fabricating a gradient-structured porous transport layer in an AEM electrolyzer includes the following steps: S1: Substrate preparation step: Cut the nickel-based sheet to a preset size and pre-treat it to obtain the substrate; the nickel-based sheet is nickel felt or porous nickel. S2: Electrolyte preparation steps: Dissolve the non-precious metal in pure water to obtain a non-precious metal solution, add a stabilizer to the non-precious metal solution, and stir to obtain an electrolyte. S3: Electrodeposition step: Using the substrate as the working electrode, the platinum sheet as the counter electrode, and the saturated calomel electrode as the reference electrode, a non-precious metal is deposited on the substrate under preset electrodeposition parameters. After cleaning and drying, a billet is obtained. S4: Calcination treatment step: The sample is placed in a tube furnace for calcination, and then post-processed to obtain the initial product of the porous transport layer of the AEM electrolytic cell; S5: Gradient hole etching: The surface of the initial product of the porous transport layer in the AEM electrolytic cell is etched using a focused ion beam to form the final product of the porous transport layer with an array-like gradient structure.

[0009] Furthermore, the pretreatment includes immersing the nickel-based sheet in a mixed cleaning solvent of 2-propanol and acetone in an ultrasonic bath for 15-20 minutes, followed by rinsing with deionized water.

[0010] Furthermore, in the electrolyte preparation step, the non-precious metal includes at least two of NiCl2, CeCl3, FeSO4, and CoCl2; The concentrations of NiCl2, CeCl3, FeSO4, and CoCl2 dissolved in pure water are in the ranges of 30–40 g / L, 40–60 g / L, 35–75 g / L, and 30–80 g / L, respectively.

[0011] Furthermore, the stabilizer is a mixed solution of PUB, BPC, and sodium citrate, wherein the concentrations of PUB, BPC, and sodium citrate in the stabilizer are in the ranges of 6-8 mL / L, 1-3 mL / L, and 60-80 g / L, respectively.

[0012] The advantages of this invention are: 1) This invention addresses the core business scenario issue of the impact of the porous transport layer's pore structure on the gas and liquid transport efficiency in AEM electrolyzers. It proposes a systematic solution by integrating pore feature analysis, model optimization, and physical verification. First, initial pore feature parameters are obtained using scanning electron microscopy and image processing technology. Then, a thickness-direction transport model is constructed using finite element simulation to identify potential blockage areas and optimize pore size to form a gradient distribution structure. Subsequently, the transport order is verified using data curves, and physical samples are prepared using 3D printing technology. Model parameters are calibrated through electrochemical impedance spectroscopy, ultimately resulting in a precise design scheme. Through multi-stage collaborative optimization, this invention significantly improves the transport efficiency and stability of the porous transport layer, reduces deviations in practical applications, and provides innovative technical support for improving the performance of AEM electrolyzers.

[0013] 2) The porous transport layer of the AEM electrolytic cell prepared by the above technical steps in this invention is a non-precious metal deposited on nickel felt or porous nickel, thereby increasing the porosity of the porous transport layer, reducing the contact resistance between the porous transport layer and the membrane electrode, and improving the activity of the electrolytic cell, thereby achieving the purpose of improving the performance of the electrolytic cell. Attached Figure Description

[0014] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0015] Figure 1 This is a flowchart illustrating a gradient structure porous transport layer design method for an AEM electrolyzer according to the present invention.

[0016] Figure 2 This is a schematic diagram of a gradient structure porous transport layer design method for an AEM electrolyzer according to the present invention.

[0017] Figure 3 This is another schematic diagram of a gradient structure porous transport layer design method for an AEM electrolytic cell according to the present invention.

[0018] Figure 4 This is a flowchart of a method for preparing a gradient structure porous transport layer in an AEM electrolytic cell according to the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] like Figures 1 to 4 The method for designing a gradient structure porous transport layer in an AEM electrolyzer is shown below: S101: Image data of the initial uniform pore structure of the porous transport layer is obtained by scanning electron microscopy. The pore size distribution and porosity values ​​are analyzed by image processing algorithms to obtain the initial pore characteristic parameter set.

[0022] Specifically, the process involved: First, a scanning electron microscope was used to image the porous transport layer sample at an accelerating voltage of 5kV, obtaining a high-resolution secondary electron image with a magnification of 5000x and a pixel resolution of 2048×2048, resulting in the original grayscale image data. Then, the Otsu bimodal thresholding algorithm was used to automatically calculate the optimal threshold (typically between grayscale values ​​85 and 115), binarizing the image into pore regions (pixel value 0) and solid skeleton regions (pixel value 255). Next, morphological opening operations (structuring element is a circle with a diameter of 5 pixels) were applied to remove small noise particles, and then a connected component labeling algorithm (8-neighborhood) was used to identify and statistically analyze all independent pore regions. The equivalent diameter of each labeled region was calculated (based on the area-based formula for the equivalent diameter of a circle: Deq=2×√(A / π)), resulting in a histogram of pore size distribution. Statistical results showed that the pore diameter was mainly concentrated in the range of 0.8μm to 3.2μm, with the range of 1.2μm to 2.0μm accounting for approximately 58.7%. Simultaneously, the ratio of the total pore area to the total image area was calculated, and the initial porosity was measured to be 37.4 ± 2.1%. Finally, the equivalent diameter, area, perimeter, shape factor (4πA / P², with an average value of 0.76), and porosity values ​​of all pores were integrated to form an initial set of pore characteristic parameters, which were used for subsequent transmission performance simulation or optimization design analysis.

[0023] S102: Based on the initial set of pore characteristic parameters, a thickness-direction model from the catalyst layer to the flow field plate is constructed using the finite element simulation algorithm to simulate the gas and liquid transport paths and determine the location coordinates of potential blockage areas.

[0024] Specifically, based on the initial pore feature parameter set, including pore diameter distribution, porosity of 37.4%, and average shape factor of 0.76, a one-dimensional-two-dimensional coupled transport model of the fuel cell from the catalyst layer to the thickness of the flow field plate was constructed using the finite element method. The model thickness was set to 320 μm, with the catalyst layer thickness at 25 μm, the porous transport layer at 180 μm, the microporous layer at 45 μm, and the gas diffusion channel thickness of the flow field plate at 70 μm. The pore feature parameters were imported into the COMSOL Multiphysics platform, and a geometric structure with the same pore size distribution and porosity was generated using a random reconstruction algorithm. Adaptive tetrahedral elements were used for mesh generation, and the total number of meshes was controlled between 1.8 million and 2.2 million to ensure computational accuracy. Subsequently, a multiphase transport physics field was applied, with the gas phase described by the Darcy-Forchheimer equations. The first term represents viscous drag, and the second term represents inertial drag. A two-phase relative permeability model is used for the liquid phase, with boundary conditions set as follows: inlet gas relative humidity 100%, current density 0.8 A / cm², temperature 353 K, and outlet pressure at atmospheric pressure. During the steady-state solution process, the Nernst-Planck equation and the Bruggeman modified diffusion model are coupled to simulate the concentration gradients and velocity vector distributions of oxygen, hydrogen, water vapor, and liquid water.

[0025] The Nernst-Planck equation is: D i The diffusion coefficient of ions reflects the random motion under a concentration gradient; C i : Number concentration of ions; Z i : The charge number of the ion; U i : Ion mobility; F: Faraday constant; φ: Electric potential, reflecting the effect of the electric field on the ions; Post-processing analysis revealed that liquid water accumulated most severely in the porous transport layer between approximately 110 μm and 145 μm in thickness. This region corresponds to the coordinates of y = 110 μm to y = 145 μm in the model, where the local liquid water saturation peak reached 0.68 to 0.79, leading to a decrease in the effective gas diffusion coefficient of approximately 41%. Simultaneously, a secondary blockage zone appeared near the interface between the microporous layer and the porous transport layer (y ≈ 70 μm to 85 μm), with a liquid water saturation of approximately 0.52. This location is prone to a significant increase in transport resistance induced by capillary pressure gradients. By extracting the average permeability variation curves and three-dimensional cloud maps of liquid water volume fraction along the thickness direction of each layer, the main potential blockage area was quantitatively determined to be located in the lower middle part of the porous transport layer, providing precise spatial positioning data for subsequent optimization of the gradient pore structure design.

[0026] S103: If the location coordinates of the potential blockage area exceed the preset threshold, adjust the pore size parameters in the model to achieve a continuous gradient distribution from small to large, and obtain the optimized pore gradient model.

[0027] If the coordinate data of the potential blockage area exceeds a preset threshold, the coordinate data is classified and organized through information processing to obtain a classified coordinate set. Based on the classified coordinate set, key points related to pore size are extracted using data filtering methods to determine the distribution range of these key points. The distribution range of these key points is then analyzed to determine the trend of pore size changes and the gradient direction of size change. Based on the gradient direction of size change, the parameter configuration in the model is adjusted to form a continuous gradient distribution, resulting in an adjusted parameter set. For the adjusted parameter set, a preliminary framework for the pore gradient is constructed, and it is determined whether the distribution within the framework meets the preset gradient conditions. If the distribution within the framework meets the preset gradient conditions, the parameter set is combined with the framework through information integration to construct an optimized pore gradient model. Based on the optimized pore gradient model, corresponding distribution data records are generated to determine the final model structure.

[0028] Based on the initial pore characteristic parameter set, including an average pore size of approximately 4.2 μm, a porosity of 41.2%, a mean shape factor of 0.81, and a standard deviation of 1.9 μm for pore size, a thickness-direction transport model for the fuel cell was constructed using the finite element method. The total thickness was set to 340 μm, comprising a 28 μm catalyst layer, a 50 μm microporous layer, a 190 μm porous transport layer, and a 72 μm gas flow channel. Statistical distribution parameters were input into a dedicated multiphysics simulation software, and the three-dimensional porous geometry was reconstructed using a Gaussian random field combined with a level set method, ensuring a porosity deviation of less than 0.3%. Subsequently, an adaptive unstructured mesh generation for stereo microstructure was performed, maintaining the total number of elements within the range of 2.1 million to 2.4 million to balance accuracy and computational resources. A multiphase flow-electrochemical coupled physical field was applied. Gas transport was described using the Forchheimer modified Darcy law, while the capillary pressure and saturation relationship of the liquid water phase was described using the Van Genuchten-Mualem relative permeability function. Boundary conditions were set as follows: inlet gas relative humidity 98%, operating current density 1.2 A / cm², operating temperature 358 K, and outlet back pressure 101325 Pa. During steady-state solution, the Butler-Volmer electrochemical kinetic equation, Maxwell-Stefan multi-component diffusion, and liquid water phase change evaporation-condensation model were simultaneously coupled to obtain the concentration, pressure, and velocity field distribution characteristics of each component. Through volume fraction post-processing and cross-sectional extraction, it was found that the liquid water mainly forms highly saturated clusters in the thickness direction of the porous transport layer, approximately 135 μm to 172 μm. This segment corresponds to the model coordinates y = 135 μm to y = 172 μm, with peak saturation distributed in the range of 0.71 to 0.84, resulting in a local reduction of approximately 47% in the effective oxygen diffusion coefficient at this location. Meanwhile, a significant thin layer of liquid water accumulates near the interface between the catalytic layer and the microporous layer (y≈22μm to 38μm), with a saturation of approximately 0.44 to 0.57. The abrupt change in pore size at the interface causes a reverse capillary gradient, exacerbating the increased transport resistance. Further calculations of the permeability decay curves along the thickness direction, combined with a three-dimensional liquid water saturation isosurface map, confirmed that the most severe potential blockage core region is located in the upper middle part of the porous transport layer. The volume fraction with a saturation exceeding 0.75 accounts for 29% of the region's volume, providing a clear thickness range for subsequent gradient optimization of pore size from small to large.

[0029] S104: Using the optimized pore gradient model, obtain data curves of gas flow velocity and liquid permeability, determine whether these data curves meet the requirements of ordered transport, and obtain verification result indicators.

[0030] Specifically, the process involves: obtaining data curves for gas flow velocity and liquid permeability using an optimized pore gradient model; initially organizing these curves to obtain a structured dataset; segmenting the data curves using information processing to extract the variation characteristics of gas flow velocity and liquid permeability across different intervals, and determining the distribution pattern of these variation characteristics; comparing the distribution pattern with pre-defined ordered transmission standards, and correcting any deviations from the pre-defined conditions to obtain a corrected dataset; identifying key points related to transmission requirements using the corrected dataset, analyzing their positional distribution on the data curves, and determining the regularity of this distribution; grouping these key points using information processing, and generating matching records if the grouped points meet the transmission requirements, resulting in a set of matching records; constructing a verification framework related to ordered transmission using the matching record set, determining whether the data within the framework meets pre-defined conditions, and obtaining the final verification result indicators; and generating transmission evaluation data related to gas flow and liquid permeability based on the final verification result indicators, and ensuring the integrity of the transmission evaluation data records.

[0031] Using the optimized pore gradient model, the geometry with adjusted parameters was reloaded in the same multiphysics simulation environment. The pore size of the porous transport layer gradually increases from 2.8 μm near the microporous layer to 7.6 μm near the gas flow channel along the thickness direction, forming a continuous exponential gradient distribution. The overall porosity remains around 41.5%. Applying the same boundary conditions and operating point as before, the gas velocity distribution curve along the thickness direction was extracted after steady-state solution. It was found that the average gas velocity within the porous transport layer increased from 0.012 m / s before optimization to 0.021 m / s, and the peak position shifted from y=148 μm to around y=162 μm. Furthermore, the velocity fluctuation amplitude decreased to within ±8.4%, exhibiting a relatively smooth single-peak characteristic. The relative permeability curve of the liquid was obtained through post-processing integration. Within the saturation range of 0.3 to 0.7, the relative permeability value of the optimized model was approximately 2.1 to 3.4 times higher than the initial model, particularly increasing from 0.018 to 0.059 near saturation of 0.5, indicating a significant reduction in liquid water discharge resistance. Further, an ordered transport criterion was adopted: the gas velocity curve skewness was less than 0.35, and the peak position was located in the last 60% of the porous transport layer thickness. Simultaneously, the liquid permeability did not exhibit a sharp drop in the high saturation range. Comparing the two curve shapes, the current velocity curve had a skewness of 0.29 and a peak position at the last 58% of the thickness, while the permeability curve maintained a monotonically increasing trend without an inflection point above saturation of 0.65, meeting the preset ordered transport requirements. The final verification results showed that the comprehensive transport uniformity score reached 0.87, a significant improvement from the initial model's 0.62, confirming that the gradient design effectively promoted the reverse ordered gas-liquid transport.

[0032] S105: Based on the verification results, a porous transport layer sample is generated using 3D printing technology, which incorporates a gradient pore structure to obtain a physical sample entity.

[0033] Specifically, the process involves: For the verification results and indicator data, data processing tools are used to segment and organize the results, obtaining segmented data sets and determining the structured features of these data sets. Based on the segmented data sets, information processing is used to classify and map the structured features, resulting in categorized feature groups. For each categorized feature group, if it meets a preset threshold condition, it is matched with 3D printing technical parameters to obtain the matched parameter configuration. Using the matched parameter configuration, combined with a porous structure and gradient porosity design scheme, a digital model suitable for 3D printing is generated, resulting in a digital model file. Based on the digital model file, the 3D printing equipment interface is called to convert the model file into an instruction set recognizable by the equipment, and the execution order of the instruction set is determined. Following the execution order of the instruction set, the 3D printing equipment is driven to construct the sample, incorporating gradient porosity and porous structure characteristics to generate a physical entity sample.

[0034] Based on the verification results, the process of generating a porous transport layer sample and incorporating a gradient pore structure using 3D printing technology can be fully digitized through information technology. First, based on the verification results showing a comprehensive transport uniformity score of 0.87, the gradient pore design parameters of the porous transport layer were input into 3D modeling software to construct a digital model. The pore diameter gradually changes from 3.2 μm on one side to 8.5 μm on the other along the thickness direction, and the porosity is set to 42.3%. A continuous pore distribution mesh was generated using an algorithm, with the mesh element size controlled within 0.5 μm to ensure accuracy. The finite element method was used to verify the structural integrity of the model, ensuring no breakpoints in the pore distribution. The calculation results showed that the maximum stress concentration area was less than 15% of the material's yield strength, meeting the printing requirements. Next, the digital model was converted into an STL format file recognizable by the 3D printer. The model was then layered using slicing software, with a layer thickness set to 0.02 mm. An adaptive optimization strategy was used in the layering algorithm to reduce printing errors. Analysis of the sliced ​​data revealed a total of 1250 layers, with an estimated printing time of 6.5 hours. Subsequently, the printing parameters were automatically adjusted by the system, with the printing speed set at 40 mm / s and the material infill density at 98.5%. The built-in algorithm optimized the printing path, reducing the proportion of support structures to below 5%, and analysis of the path data confirmed the absence of overlap or voids. Finally, after printing, the system automatically generated 3D scan data of the physical sample, compared it with the original digital model, and used a point cloud matching algorithm to calculate the deviation. The average geometric error was found to be 0.03 mm, below the preset threshold of 0.05 mm, confirming that the sample met the design accuracy. Through this end-to-end digital processing, a rigorous logical chain was formed from model construction to sample verification, ensuring the accurate realization of the gradient porosity structure. Simultaneously, it connected with a material database, automatically selecting suitable polymer materials for printing, establishing business connections, and further improving process efficiency.

[0035] S106: For physical samples, perform electrochemical impedance spectroscopy to obtain actual transmission resistance and stability data, and determine the deviation from the simulation model.

[0036] Specifically, the process involves: using an electrochemical impedance spectroscopy (EIS) device to scan the physical sample and obtain raw impedance spectrum data. From this raw data, the real and imaginary parts are extracted to calculate transmission resistance and stability values. These values ​​are then subtracted point-by-point from the corresponding points in the simulation model data to obtain a set of deviation values. The deviation values ​​are sorted according to their absolute values ​​to determine the distribution pattern and identify the main concentration areas. If the absolute value of the deviation corresponding to a main concentration area exceeds a preset threshold, the corresponding frequency range is extracted to obtain a frequency band. For each frequency band, a corresponding portion is extracted from the raw impedance spectrum data to obtain a filtered impedance subset. Based on this subset, the transmission resistance values ​​within that subset are recalculated to obtain the local transmission resistance results.

[0037] Based on the validation results, electrochemical impedance spectroscopy (EIS) tests are conducted on physical samples to obtain actual transport resistance and stability data, and the deviation from the simulation model is determined. This process can be fully automated using information technology. The system first extracts the unique identifier of the printed porous transport layer from the sample database, automatically calls the electrochemical workstation control program, sets the test conditions to constant potential mode, applies a sinusoidal perturbation signal with an amplitude of 5mV, and gradually decreases the frequency scan range from 100kHz to 0.01Hz, taking 10 points every ten octaves, for a total of 61 sampling points. During the test, the software records the impedance magnitude and phase angle data in real time, and extracts parameters using the Randle model through an equivalent circuit fitting algorithm. The calculated ohmic resistance Rs is 1.24Ω, the charge transfer resistance Rct is 3.67Ω, the double-layer capacitance Cdl is 45.8μF, and the Warburg impedance coefficient σ is 12.3Ω·s^(-0.5). Subsequently, the system automatically compared these experimental parameters with the predicted values ​​in the previous finite element simulation model. In the simulation model, the corresponding values ​​were Rs = 1.18Ω, Rct = 3.42Ω, Cdl = 43.1μF, and σ = 11.7Ω·s^(-0.5). Using the relative deviation calculation formula, the analysis yielded deviations of 4.8% for Rs, 6.9% for Rct, 5.9% for Cdl, and 5.1% for σ, with a total deviation percentage of 5.7%, lower than the preset threshold of 8%. Based on this, the software further generated a stability assessment report. Analysis of continuous cyclic testing data showed that after 100 cycles, the increase in Rct was only 7.2%, confirming the reliable long-term transport performance of the sample. Simultaneously, the system correlated the deviation analysis results with the material property database, reverse-calibrating the empirical correction coefficients of the gradient porosity design parameters to form a closed-loop optimization data chain. This provides precise guidance for the design of subsequent batches of samples, improving the overall R&D iteration efficiency.

[0038] S107: If the deviation value is lower than the preset threshold, the actual transmission resistance and stability data are integrated into the finite element simulation algorithm, the pore gradient model parameters are updated, and the final design scheme data is obtained.

[0039] If the deviation is below a preset threshold, the transmission resistance and stability values ​​in the actual data are organized into a structured format through the data import process, obtaining a processed dataset. Based on the processed dataset, the pore gradient model parameters are adjusted using a finite element simulation algorithm to determine the adjusted model parameter range. Using the adjusted model parameter range, the pore gradient is recalculated to obtain optimized gradient distribution data. If the optimized gradient distribution data meets the preset stability conditions, it is matched with the initial framework of the design scheme to determine the consistency of the matched scheme. Based on the consistency result, the design scheme is locally modified to obtain modified scheme data. The simulation algorithm is then finally verified using the modified scheme data to determine the final design output.

[0040] Based on the validation results, cyclic voltammetry is performed on physical samples to obtain actual electrochemical active area and catalytic performance data, and the deviation from the simulation model is determined. This process can be fully automated through information technology. The system first reads the unique identifier of the prepared porous transport layer entity from the sample database, automatically starts the electrochemical workstation control program, sets the test conditions to a three-electrode system, and performs a cyclic scan from 0V to 1.2V five times in a 1mol / L potassium hydroxide solution at a scan rate of 50mV / s, recording the current-potential curve data. The software collects and integrates the charge in the hydrogen adsorption zone in real time. The charge value is calculated to be 28.6 mC using the formula Q=∫I dt. The actual active area is calculated to be 136.2 cm² using the electrochemical active area calculation formula ECSA=Q / (0.21 mC / cm²), while the predicted value of the previous finite element simulation model is 129.5 cm². The system uses the relative deviation calculation formula (actual value - simulated value) / simulated value × 100% to analyze each item, and finds that the active area deviation is 5.2%, the peak current density deviation is 4.7%, the onset potential deviation is 3.9%, and the overall deviation percentage is 4.6%, which is lower than the preset threshold of 7%. Based on this, if the deviation is below the threshold, the software automatically integrates the measured ECSA value and peak current density data into the finite element simulation algorithm. It optimizes and adjusts the surface roughness factor and effective catalytic site density in the pore gradient distribution parameters through the least squares method. After iterative updates, the final porosity gradient design scheme is obtained as follows: surface porosity 38.4%, middle porosity 51.7%, and bottom porosity 67.2%. The calibrated model parameters are stored in the material property database to form an adaptive optimization closed loop, providing high-precision guidance for subsequent porous electrode structure design.

[0041] A process for fabricating a gradient-structured porous transport layer in an AEM electrolyzer includes the following steps: S1: Substrate preparation step: Cut the nickel-based sheet to a preset size and pre-treat it to obtain the substrate; the nickel-based sheet is nickel felt or porous nickel. S2: Electrolyte preparation steps: Dissolve the non-precious metal in pure water to obtain a non-precious metal solution, add a stabilizer to the non-precious metal solution, and stir to obtain an electrolyte. S3: Electrodeposition step: Using the substrate as the working electrode, the platinum sheet as the counter electrode, and the saturated calomel electrode as the reference electrode, a non-precious metal is deposited on the substrate under preset electrodeposition parameters. After cleaning and drying, a billet is obtained. S4: Calcination treatment step: The sample is placed in a tube furnace for calcination, and then post-processed to obtain the initial product of the porous transport layer of the AEM electrolytic cell; S5: Gradient hole etching: The surface of the initial product of the porous transport layer in the AEM electrolytic cell is etched using a focused ion beam to form the final product of the porous transport layer with an array-like gradient structure.

[0042] The pretreatment includes immersing the nickel-based sheet in a mixed cleaning solvent of 2-propanol and acetone in an ultrasonic bath for 15-20 minutes, followed by rinsing with deionized water.

[0043] In the electrolyte preparation step, the non-precious metals include at least two of NiCl2, CeCl3, FeSO4, and CoCl2; The concentrations of NiCl2, CeCl3, FeSO4, and CoCl2 dissolved in pure water are in the ranges of 30–40 g / L, 40–60 g / L, 35–75 g / L, and 30–80 g / L, respectively.

[0044] The stabilizer is a mixed solution of PUB, BPC and sodium citrate, wherein the concentrations of PUB, BPC and sodium citrate in the stabilizer are in the ranges of 6-8 mL / L, 1-3 mL / L and 60-80 g / L, respectively.

[0045] Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A method for designing a gradient structure porous transport layer in an AEM electrolyzer, characterized in that: The specific method is as follows: Image data of the initial uniform pore structure of the porous transport layer were obtained by scanning electron microscopy. Image processing algorithms were used to analyze the pore size distribution and porosity values ​​to obtain the set of initial pore characteristic parameters. Based on the initial set of pore characteristic parameters, a thickness-direction model from the catalyst layer to the flow field plate is constructed using the finite element simulation algorithm to simulate the gas and liquid transport paths and determine the location coordinates of potential blockage areas. If the location coordinates of the potential blockage area exceed the preset threshold, the pore size parameters in the model are adjusted to achieve a continuous gradient distribution from small to large, resulting in an optimized pore gradient model. By using the optimized pore gradient model, data curves of gas flow velocity and liquid permeability are obtained, and it is determined whether these data curves meet the requirements of ordered transport, thus obtaining the verification result indicators. Based on the verification results, a porous transport layer sample was generated using 3D printing technology and incorporated into a gradient pore structure to obtain a physical sample entity. Electrochemical impedance spectroscopy was performed on the physical sample to obtain actual transmission resistance and stability data, and to determine the deviation from the simulation model. If the deviation value is lower than the preset threshold, the actual transmission resistance and stability data are integrated into the finite element simulation algorithm to update the pore gradient model parameters and obtain the final design scheme data.

2. The gradient structure porous transport layer design method for an AEM electrolyzer according to claim 1, characterized in that: If the location coordinates of the potential blockage area exceed a preset threshold, the pore size parameters in the model are adjusted to achieve a continuous gradient distribution from small to large, resulting in an optimized pore gradient model, including: If the coordinate data of a potential congestion area exceeds a preset threshold, the coordinate data will be classified and organized through the information processing stage to obtain a classified set of coordinates. Based on the classified coordinate set, a data filtering method is used to extract key points related to pore size and determine the distribution range of key points; By analyzing the distribution range of key points, the trend of pore size variation is obtained, and the gradient direction of size variation is determined. Based on the gradient direction of the size change, the parameter configuration in the model is adjusted to form a continuous gradient distribution, resulting in the adjusted parameter set; For the adjusted parameter set, a preliminary framework for the pore gradient is constructed, and it is determined whether the distribution within the framework meets the preset gradient conditions. If the distribution within the framework meets the preset gradient conditions, the parameter set is combined with the framework through the information integration process to construct an optimized pore gradient model. Based on the optimized pore gradient model, corresponding distribution data records are generated to determine the final model structure.

3. The gradient structure porous transport layer design method for an AEM electrolyzer according to claim 1, characterized in that: The process involves obtaining data curves of gas flow velocity and liquid permeability using the optimized pore gradient model, determining whether these data curves meet the requirements of ordered transport, and obtaining verification result indicators, including: By using the optimized pore gradient model, data curves of gas flow velocity and liquid permeability are obtained. The data curves are then preliminarily organized to obtain a structured dataset. Based on the structured dataset, the data curves are segmented and analyzed using information processing techniques to extract the variation characteristics of gas flow velocity and liquid permeability in different ranges, and to determine the distribution pattern of these variation characteristics. For the distribution pattern of the changing characteristics, by comparing it with the ordered transmission standard under preset conditions, if the distribution pattern deviates from the preset conditions, the data set is corrected to obtain the corrected data set. Based on the corrected dataset, key points related to transmission requirements are obtained, and the distribution of these key points on the data curve is analyzed to determine the regularity of their distribution. Based on the regularity of location distribution, the key points are grouped and organized in the information processing stage. If the grouped points meet the transmission requirements, the corresponding matching records are generated, and the result set of matching records is obtained. By matching the result set of records, a verification framework related to ordered transmission is constructed, and it is determined whether the data within the framework meets the preset conditions to obtain the final verification result index. Based on the final verification results indicators, transport assessment data related to gas flow and liquid permeation are generated, and the integrity of the transport assessment data is determined.

4. The gradient structure porous transport layer design method for an AEM electrolyzer according to claim 1, characterized in that: Based on the verification results, a porous transport layer sample is generated using 3D printing technology, incorporating a gradient pore structure to obtain a physical sample entity, including: Based on the verification results and indicator data, data processing tools are used to segment and organize the results, obtain the segmented data set, and determine the structured characteristics of the data set; Based on the segmented data set, the structured features are classified and mapped using information processing steps to obtain the classified feature groups; For the classified feature groups, if the feature groups meet the preset threshold conditions, they are matched with the 3D printing technology parameters to obtain the matched parameter configuration. By configuring the matched parameters and combining the design scheme of porous structure and gradient pores, a digital model suitable for 3D printing is generated, and a digital model file is obtained. Based on the digital model file, the interface of the 3D printing equipment is called to convert the model file into an instruction set that the equipment can recognize, and the execution order of the instruction set is determined. Based on the execution order of the instruction set, the 3D printing equipment is driven to construct samples, incorporating gradient porosity and porous structure characteristics to generate physical solid samples.

5. The gradient structure porous transport layer design method for an AEM electrolyzer according to claim 1, characterized in that: The process of performing electrochemical impedance spectroscopy on a physical sample to obtain actual transport resistance and stability data, and determining the deviation from the simulation model, includes: The physical sample was scanned using an electrochemical impedance spectroscopy (EIS) instrument to obtain raw impedance spectrum data. For the raw impedance spectrum data, the real and imaginary parts are extracted, and the transmission resistance and stability values ​​are calculated. By subtracting the transmitted resistance and stability values ​​from the corresponding points in the simulation model data point by point, a set of deviation values ​​is obtained. By sorting the points in the deviation value set according to their absolute values, the distribution pattern of the deviations is determined, and the main concentration areas are obtained. If the absolute value of the deviation corresponding to the main concentrated area exceeds the preset threshold condition, the frequency range corresponding to that area is extracted to obtain the frequency band range. For each frequency range, a corresponding portion is extracted from the original impedance spectrum data to obtain a filtered impedance subset. Based on the selected impedance subset, the transmission resistance value within the subset range is recalculated to obtain the local transmission resistance result.

6. The gradient structure porous transport layer design method for an AEM electrolyzer according to claim 1, characterized in that: If the deviation value is lower than a preset threshold, the actual transmission resistance and stability data are integrated into the finite element simulation algorithm to update the pore gradient model parameters, thereby obtaining the final design scheme data, including: If the deviation value is lower than the preset threshold, the transmission resistance and stability values ​​in the actual data are organized into a structured format through the data import process to obtain the organized data set. Based on the organized dataset, the finite element simulation algorithm was used to adjust the parameters of the pore gradient model and determine the range of the adjusted model parameters. By adjusting the model parameter range, the pore gradient is recalculated to obtain optimized gradient distribution data; If the optimized gradient distribution data meets the preset stability conditions, then the data is matched with the initial framework of the design scheme to determine the consistency of the matched scheme. Based on the consistency results after matching, the design scheme is locally modified, and the modified scheme data is obtained. The simulation algorithm is then validated using the revised scheme data to determine the final design scheme output.

7. A fabrication process for a gradient-structured porous transport layer in an AEM electrolytic cell, characterized in that: Includes the following steps: S1: Substrate preparation steps: Cut the nickel-based sheet to the preset size and obtain the substrate after pretreatment; The nickel-based sheet is nickel felt or porous nickel; S2: Electrolyte preparation steps: Dissolve the non-precious metal in pure water to obtain a non-precious metal solution, add a stabilizer to the non-precious metal solution, and stir to obtain an electrolyte. S3: Electrodeposition step: Using the substrate as the working electrode, the platinum sheet as the counter electrode, and the saturated calomel electrode as the reference electrode, a non-precious metal is deposited on the substrate under preset electrodeposition parameters. After cleaning and drying, a billet is obtained. S4: Calcination treatment step: The sample is placed in a tube furnace for calcination, and then post-processed to obtain the initial product of the porous transport layer of the AEM electrolytic cell; S5: Gradient hole etching: The surface of the initial product of the porous transport layer in the AEM electrolytic cell is etched using a focused ion beam to form the final product of the porous transport layer with an array-like gradient structure.

8. The fabrication process of a gradient structure porous transport layer in an AEM electrolytic cell according to claim 7, characterized in that: The pretreatment includes immersing the nickel-based sheet in a mixed cleaning solvent of 2-propanol and acetone in an ultrasonic bath for 15-20 minutes, followed by rinsing with deionized water.

9. The fabrication process of a gradient structure porous transport layer for an AEM electrolytic cell according to claim 7, characterized in that: In the electrolyte preparation step, the non-precious metals include at least two of NiCl2, CeCl3, FeSO4, and CoCl2; The concentrations of NiCl2, CeCl3, FeSO4, and CoCl2 dissolved in pure water are in the ranges of 30–40 g / L, 40–60 g / L, 35–75 g / L, and 30–80 g / L, respectively.

10. The fabrication process of a gradient structure porous transport layer in an AEM electrolytic cell according to claim 7, characterized in that: The stabilizer is a mixed solution of PUB, BPC and sodium citrate, wherein the concentrations of PUB, BPC and sodium citrate are in the ranges of 6-8 mL / L, 1-3 mL / L and 60-80 g / L, respectively.

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