Anion exchange membrane for AEM electrolyzed water and preparation method of anion exchange membrane

Abstract

The invention discloses an anion exchange membrane for AEM electrolyzed water and a preparation method of the anion exchange membrane. The anion exchange membrane is designed through a multi-layer structure comprising a substrate layer, an anion exchange layer, an interface optimization layer, a polarity degradation resistant layer and a self-healing layer. The anion exchange membrane is prepared through an automatic preparation system comprising a substrate layer preparation module, an anion exchange layer preparation module, an interface optimization layer preparation module, a polarity degradation resistant layer preparation module, a self-healing layer preparation module, a performance test evaluation module and a process optimization module. The automatic preparation system evaluates the performance of the prepared anion exchange membrane through a machine learning algorithm and continuously optimizes the preparation process, so that the performance of the prepared anion exchange membrane is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of anion exchange membranes, and more specifically to an anion exchange membrane for AEM water electrolysis and a preparation method thereof. Background Art

[0002] Water electrolysis is a technology that produces hydrogen by electrolyzing water, and is often used to store electricity generated by renewable energy. In this process, water is decomposed into hydrogen and oxygen in an electrolyzer. Water electrolysis devices usually include a cathode and an anode, which are separated by an anion exchange membrane. This membrane only allows anions to pass through, thus ensuring spatial separation of the reactions, improving the efficiency and safety of the entire process.

[0003] In the electrolysis of water, the anion exchange membrane can prevent the hydrogen produced at the cathode from mixing with the oxygen produced at the anode, which may lead to the risk of explosion. It can allow anions to move from the anode to the cathode, thereby maintaining the charge balance during the electrolysis process. However, some traditional anion exchange membranes have low ion conduction efficiency, which affects the electrolysis efficiency. They may degrade after long-term use, and the degradation products will cause damage to the electrolysis equipment. In addition, the traditional anion exchange membrane preparation process is complicated, and the prepared anion exchange membrane has low performance. Summary of the invention

[0004] In view of the deficiencies of the above-mentioned technologies, the present invention discloses an anion exchange membrane for AEM water electrolysis and a preparation method thereof, designs an anion exchange membrane with good ion transport properties, and is capable of anti-oxidation, anti-degradation and self-healing. The anion exchange membrane is prepared by an automated system, and the performance of the anion exchange membrane is tested and evaluated. The preparation process is optimized according to the test and evaluation results to improve the performance of the anion exchange membrane.

[0005] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0006] An anion exchange membrane for AEM water electrolysis, comprising:

[0007] A base layer, using a polytetrafluoroethylene polymer substrate as a base to support the entire membrane structure, the polytetrafluoroethylene polymer substrate can carry out ion transmission, and the base layer is located at the bottom layer;

[0008] An anion exchange layer, using 4-vinylbenzyltrimethylammonium polymer with anion exchange function, capable of transmitting anions, the anion exchange layer is closely attached to the base layer;

[0009] An interface optimization layer, which introduces hydrophilic aluminum hydroxide nanoparticles to adjust the hydrophilicity of the membrane surface, and introduces ion exchange resin to increase the cation rejection effect, and the interface optimization layer is located on the surface of the anion exchange layer;

[0010] An anti-polar degradation layer, in which a fluorinated polymer having chemical stability is introduced to enable the membrane to resist degradation under oxidation and alkaline conditions, and the anti-polar degradation layer is coated on the interface optimization layer;

[0011] The self-healing layer incorporates a self-healing microencapsulated monomer polymer, which will break when a tiny crack occurs in the membrane, release the monomer and trigger in-situ polymerization to repair the membrane damage. The self-healing layer is dispersed on the anti-polar degradation layer.

[0012] As a further embodiment of the present invention, in the anion exchange layer, the 4-vinylbenzyltrimethylammonium polymer has a benzene ring, a benzyl group is connected to the 4 position of the benzene ring, a vinyl group is connected to the other end of the benzyl group, and a trimethylammonium group is connected to the other side of the vinyl group.

[0013] As a further embodiment of the present invention, a method for preparing an anion exchange membrane for AEM water electrolysis, preparing an anion exchange membrane for AEM water electrolysis according to claim 1, comprising the following steps:

[0014] Step (1), preparing a base layer;

[0015] The base layer is prepared by a base layer preparation module, wherein the base layer preparation module cleans the polytetrafluoroethylene substrate, prepares a microporous structure to form a base layer by stretching, calendering and puffing, and performs surface activation treatment on the base layer by plasma treatment;

[0016] Step (2), forming an anion exchange layer;

[0017] An anion exchange layer is prepared by an anion exchange layer preparation module, wherein the anion preparation module synthesizes 4-vinylbenzyltrimethylammonium polymer, and the 4-vinylbenzyltrimethylammonium polymer is dissolved in ethanol, and a solution containing the 4-vinylbenzyltrimethylammonium polymer is uniformly coated on a substrate layer by a coating technique, and the coating is thermally cured to form a stable anion exchange layer;

[0018] Step (3), preparing an interface optimization layer;

[0019] The interface optimization layer is prepared by an interface optimization layer preparation module, wherein the interface optimization layer preparation module mixes aluminum hydroxide nanoparticles and ion exchange resin in an aqueous solvent at a ratio of 2:1 to form a dispersion, and the dispersion is coated on the anion exchange layer, and the aluminum hydroxide and ion exchange resin are uniformly distributed through a drying and curing process to form an interface optimization layer;

[0020] Step (4), applying an anti-polar degradation layer;

[0021] The anti-polarity degradation layer is prepared by an anti-polarity degradation layer preparation module, wherein the anti-polarity degradation layer preparation module selects a polyvinylidene fluoride polymer, and dissolves the polyvinylidene fluoride polymer by a tetrahydrofuran solvent at a high temperature, and coats the solution containing the fluorinated polymer on the interface optimization layer by a coating technology, and forms the anti-polarity degradation layer after drying and curing;

[0022] Step (5), integrating a self-healing layer;

[0023] The self-healing layer is prepared by a self-healing layer preparation module, wherein the self-healing layer preparation module prepares a dispersion containing microencapsulated monomer polymers, and evenly disperses the microcapsule dispersion in the anti-polar degradation layer to form a self-healing layer, and ensures that the microcapsules will not break during the curing process;

[0024] Step (6), performance test evaluation;

[0025] The prepared anion exchange membrane is tested and evaluated using a performance test evaluation module, wherein the performance test evaluation module tests the ion exchange capacity, conductivity, mechanical strength, chemical stability and self-healing effect of the prepared anion exchange membrane, and analyzes and evaluates the test results through a neural network algorithm model, and outputs the evaluation results;

[0026] Step (7) process optimization;

[0027] The process optimization module is used to optimize the preparation process of the anion exchange membrane. The process optimization module improves the preparation conditions of the anion exchange membrane through a genetic algorithm model according to the test evaluation results, and optimizes the preparation parameters and the preparation process.

[0028] As a further embodiment of the present invention, the base layer preparation module is located below the anion exchange membrane preparation module, the anion exchange membrane preparation module is located below the interface optimization layer preparation module, the interface optimization layer preparation module is located below the anti-polarity degradation layer preparation module, the anti-polarity degradation layer preparation module is located below the self-healing layer preparation module, the anion membrane output end of the self-healing layer preparation module is connected to the input end of the performance test and evaluation module, and the signal output end of the performance test and evaluation module is connected to the signal input end of the process optimization module.

[0029] As a further embodiment of the present invention, the performance test evaluation module includes an ion exchange capacity test module, a conductivity test module, a mechanical strength test module, a self-healing effect test module, a test result processing module, a performance comprehensive evaluation module and a test evaluation report output module. The ion exchange capacity test module places the prepared anion exchange membrane into an ion exchange solution by an immersion method, and measures the ion exchange capacity by using an ion chromatograph to measure the concentration of ions adsorbed into the anion exchange membrane. The conductivity test module measures the conductivity of the anion exchange membrane by a four-probe method, and the mechanical strength test module measures the self-healing effect by a universal material testing machine. The anion exchange membrane is stretched, compressed and punctured to evaluate the mechanical properties of the anion exchange membrane. The self-healing effect test module evaluates the self-healing effect of the anion exchange membrane by artificially damaging the anion exchange membrane, observing and recording the repair of the damaged area. The test result processing module receives the test result data of each module, and cleans, normalizes and converts the data. The comprehensive performance evaluation module evaluates the performance of the anion exchange membrane through a neural network algorithm model based on the test result data. The test evaluation report output module generates and outputs a test evaluation report by integrating the test results and the performance evaluation results.

[0030] The output ends of the ion exchange capacity test module, the conductivity test module, the mechanical strength test module and the self-healing effect test module are connected to the input end of the test result processing module, the output end of the test result processing module is connected to the input end of the comprehensive performance evaluation module, and the output end of the comprehensive performance evaluation module is connected to the input end of the test evaluation report output module.

[0031] As a further embodiment of the present invention, the working method of the ion exchange capacity testing module is:

[0032] S1, cutting an anion exchange membrane of standard size as a membrane sample, and immersing the membrane sample in an ion exchange solution of known concentration;

[0033] S2. Through immersion, ions in the solution are exchanged with fixed charged groups in the membrane sample;

[0034] S3. After the prescribed immersion time is over, the membrane sample is taken out from the solution and the membrane sample is thoroughly washed with deionized water to remove unreacted ions;

[0035] S4, placing the cleaned membrane sample in a new ion exchange solution to release the adsorbed ions back into the solution;

[0036] S5, using an ion chromatograph to measure the concentration of ions released in the solution to obtain the amount of ions adsorbed by the membrane sample;

[0037] S6. Calculate the ion exchange capacity of the membrane sample according to the amount of ions adsorbed by the membrane sample and the mass of the membrane sample, the calculation method being:

[0038]

[0039] In formula (1), IEC is the ion exchange capacity in milliequivalents / gram, C e is the ion concentration in the equilibrium solution in mol / L, C i is the ion concentration in the initial solution in mol / L, V is the volume of the solution in liters, and W is the mass of the dry film sample in grams.

[0040] As a further embodiment of the present invention, the working method of the self-healing effect testing module is:

[0041] S1. Cutting an anion exchange membrane of standard size as a membrane sample, wherein the surface of the sample is smooth and has no obvious defects, and artificially making cracks, scratches and holes on the surface of the membrane sample;

[0042] S2. immediately start observing the membrane sample by scanning electron microscopy after the damage is caused, and record the morphology, size and location of the damaged area;

[0043] S3. Expose the damaged film sample to a simulated temperature, humidity and atmospheric pressure environment, and regularly observe and record the repair status of the damaged area, including the degree, time and method of repair;

[0044] S4. Observe the microstructure of the repaired area of ​​the repaired membrane sample by scanning electron microscopy to evaluate the quality of crack filling and interface fusion;

[0045] S4. For the repaired membrane sample, a hardness test is performed on the repaired area by a hardness tester, and the elastic modulus of the repaired membrane sample is measured by a dynamic mechanical analyzer to evaluate the mechanical properties of the repaired membrane sample;

[0046] S5. For the repaired membrane sample, the changes in the chemical composition and molecular structure of the repaired area are detected by infrared spectroscopy analysis to evaluate the chemical properties of the repaired membrane sample;

[0047] S6. The self-healing effect of the prepared anion exchange membrane was evaluated by comparing the microstructure, mechanical properties, and chemical properties of the membrane samples before and after repair.

[0048] As a further embodiment of the present invention, the working method of the comprehensive performance evaluation module is:

[0049] S1. Collect data including ion exchange capacity, conductivity, mechanical strength and self-healing effect of anion exchange membranes, annotate the data according to performance, and divide the annotated data set into a test set and a training set;

[0050] S2. Construct a neural network algorithm model to test and train the data. The neural network algorithm model includes an input layer, a depthwise separable convolution layer, a feature fusion layer, a densely connected layer, an attention mechanism layer and an output layer. The output end of the input layer is connected to the input end of the feature fusion layer, the output end of the feature fusion layer is connected to the input end of the depthwise separable convolution layer, the output end of the depthwise separable convolution layer is connected to the input end of the densely connected layer, the output end of the densely connected layer is connected to the input end of the attention mechanism layer, and the output end of the attention mechanism layer is connected to the input end of the output layer.

[0051] S3. Extracting the features of ion exchange capacity, conductivity, mechanical strength and self-healing effect according to the performance indicators through the depth-separable convolution layer, wherein the depth-separable convolution includes depth-wise convolution and point-wise convolution, and the feature extraction is:

[0052]

[0053] In formula (2), D is the intermediate feature vector of the deep convolution, W pqm is the weight of the depth convolution kernel for the mth channel, F is the intermediate feature vector of the point-by-point convolution, is the weight of the point-by-point convolution kernel, b m is the bias of the depthwise convolution, b l is the bias of the point-wise convolution.

[0054] S4, weighted fusion of the extracted features is performed through a feature fusion layer, wherein the feature fusion is:

[0055] f=α·f a +β·f b +δ·f c +γ·f d (3)

[0056] In formula (3), α is the ion exchange capacity f a The weight of β is the conductivity f b The weight of δ is the mechanical strength f c The weight of γ is the self-healing effect f d The weight of .

[0057] S5. The extracted feature data is transformed nonlinearly and combined through the dense connection layer, and the key features are focused through the attention mechanism layer. Finally, the output layer regresses the extracted feature data to output a comprehensive score that can integrate all performance indicators. The regression output is:

[0058] F Score =W o ·F attended +b o (4)

[0059] In formula (4), W o is the weight matrix of the output layer, F attended is the feature vector after attention mechanism processing, b o is the bias of the output layer;

[0060] S6. Use the training set for back propagation, reduce the loss function by gradient descent during the iteration process, and output the performance evaluation model of the anion exchange membrane when the loss function is less than the set threshold;

[0061] S7. Input the received test results of ion exchange capacity, conductivity, mechanical strength and self-healing effect of the anion exchange membrane into the performance evaluation model, and output the comprehensive performance score of the anion exchange membrane through the performance evaluation model to evaluate the performance of the anion exchange membrane.

[0062] Positive beneficial effects:

[0063] The present invention discloses an anion exchange membrane for AEM water electrolysis and a preparation method thereof. The anion exchange membrane is designed with a multilayer structure having a substrate layer, an anion exchange layer, an interface optimization layer, an anti-polarity degradation layer and a self-healing layer, so that good ion transport, oxidation resistance, degradation resistance and self-healing properties can be provided. The anion exchange membrane is prepared by an automated preparation system including a substrate layer preparation module, an anion exchange layer preparation module, an interface optimization layer preparation module, an anti-polarity degradation layer preparation module, a self-healing layer preparation module, a performance test evaluation module and a process optimization module, so that the preparation efficiency of the anion exchange membrane is improved. The automated preparation system evaluates the performance of the prepared anion exchange membrane through a machine learning algorithm and continuously optimizes the preparation process, so that the performance of the prepared anion exchange membrane is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a schematic diagram of the structure of an anion exchange membrane for AEM water electrolysis of the present invention;

[0065] Figure 2 The present invention is a schematic diagram of the working process of a method for preparing an anion exchange membrane for AEM water electrolysis;

[0066] Figure 3 It is a structural schematic diagram of an automated preparation system of a method for preparing an anion exchange membrane for AEM electrolysis of water according to the present invention;

[0067] Figure 4 This is a schematic structural diagram of a performance test and evaluation module of a method for preparing an anion exchange membrane for AEM water electrolysis according to the present invention;

[0068] Figure 5 This is a schematic diagram of the working process of an ion exchange capacity test module of a method for preparing an anion exchange membrane for AEM water electrolysis of the present invention;

[0069] Figure 6 This is a schematic diagram of the working process of a self-healing effect test module of a method for preparing an anion exchange membrane for AEM water electrolysis of the present invention;

[0070] Figure 7 The present invention is a schematic diagram of the workflow of a comprehensive performance evaluation module of a method for preparing an anion exchange membrane for AEM water electrolysis. DETAILED DESCRIPTION

[0071] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0072] In a specific embodiment, Figure 1 As shown, the present invention is an anion exchange membrane for AEM water electrolysis, which optimizes the physical and chemical properties of the membrane by using a multilayer structure design, and also significantly improves its stability and efficiency in water electrolysis applications. The anion exchange membrane for AEM water electrolysis comprises:

[0073] Base layer 1, using a polytetrafluoroethylene polymer substrate as a base to support the entire membrane structure, the polytetrafluoroethylene polymer substrate can carry out ion transmission, and the base layer 1 is located at the bottom layer;

[0074] Anion exchange layer 2, using 4-vinylbenzyltrimethylammonium polymer with anion exchange function, capable of transmitting anions, the anion exchange layer 2 is closely attached to the base layer 1;

[0075] An interface optimization layer 3 is provided, wherein hydrophilic aluminum hydroxide nanoparticles are introduced to adjust the hydrophilicity of the membrane surface, and ion exchange resin is introduced to increase the cation rejection effect, and the interface optimization layer 3 is located on the surface of the anion exchange layer 2;

[0076] The anti-polar degradation layer 4 is a fluorinated polymer with chemical stability, so that the membrane can resist degradation under oxidation and alkaline conditions, and the anti-polar degradation layer 4 is coated on the interface optimization layer 3;

[0077] The self-healing layer 5 incorporates a self-healing microencapsulated monomer polymer, which will break when a tiny crack occurs in the membrane, releasing the monomer and triggering in-situ polymerization to repair the membrane damage. The self-healing layer 5 is dispersed on the anti-polar degradation layer 4.

[0078] In a specific embodiment, in the anion exchange layer, the 4-vinylbenzyltrimethylammonium polymer has a benzene ring, a benzyl group is connected to the 4 position of the benzene ring, a vinyl group is connected to the other end of the benzyl group, and a trimethylammonium group is connected to the other side of the vinyl group.

[0079] like Figure 2 As shown, the present invention provides a method for preparing an anion exchange membrane for AEM electrolysis of water, and the preparation method of the anion exchange membrane for AEM electrolysis of water as claimed in claim 1 comprises the following steps:

[0080] Step (1), preparing a base layer 1;

[0081] The base layer 1 is prepared by a base layer preparation module, wherein the base layer preparation module cleans the polytetrafluoroethylene substrate, prepares a microporous structure to form the base layer 1 by stretching, calendering and puffing, and performs surface activation treatment on the base layer 1 by plasma treatment;

[0082] Step (2), forming an anion exchange layer 2;

[0083] An anion exchange layer 2 is prepared by an anion exchange layer preparation module, wherein the anion preparation module synthesizes a 4-vinylbenzyltrimethylammonium polymer, and the 4-vinylbenzyltrimethylammonium polymer is dissolved in ethanol, and a solution containing the 4-vinylbenzyltrimethylammonium polymer is uniformly coated on the base layer 1 by a coating technology, and the coating is thermally cured to form a stable anion exchange layer 2;

[0084] Step (3), preparing an interface optimization layer 3;

[0085] The interface optimization layer 3 is prepared by an interface optimization layer preparation module, wherein the interface optimization layer preparation module mixes aluminum hydroxide nanoparticles and ion exchange resin in an aqueous solvent at a ratio of 2:1 to form a dispersion, and the dispersion is coated on the anion exchange layer 2, and the aluminum hydroxide and the ion exchange resin are uniformly distributed through a drying and curing process to form the interface optimization layer 3;

[0086] Step (4), applying the anti-polar degradation layer 4;

[0087] The anti-polarity degradation layer 4 is prepared by the anti-polarity degradation layer preparation module, wherein the anti-polarity degradation layer preparation module selects a polyvinylidene fluoride polymer, and dissolves the polyvinylidene fluoride polymer by a tetrahydrofuran solvent at a high temperature, and coats the solution containing the fluorinated polymer on the interface optimization layer 3 by a coating technology, and forms the anti-polarity degradation layer 4 after drying and curing;

[0088] Step (5), integrating the self-healing layer 5;

[0089] The self-healing layer 5 is prepared by a self-healing layer preparation module, wherein the self-healing layer preparation module prepares a dispersion containing microencapsulated monomer polymers, and evenly disperses the microcapsule dispersion in the anti-polar degradation layer 4 to form the self-healing layer 5, and ensures that the microcapsules will not break during the curing process;

[0090] Step (6), performance test evaluation;

[0091] The prepared anion exchange membrane is tested and evaluated using a performance test evaluation module, wherein the performance test evaluation module tests the ion exchange capacity, conductivity, mechanical strength, chemical stability and self-healing effect of the prepared anion exchange membrane, and analyzes and evaluates the test results through a neural network algorithm model, and outputs the evaluation results;

[0092] Step (7) process optimization;

[0093] The process optimization module is used to optimize the preparation process of the anion exchange membrane. The process optimization module improves the preparation conditions of the anion exchange membrane through a genetic algorithm model according to the test evaluation results, and optimizes the preparation parameters and the preparation process.

[0094] In a specific embodiment, in the preparation process of anion exchange membrane, the control conditions and parameters of each step are key factors to ensure the quality and performance of the prepared anion exchange membrane. The following are the main control conditions and parameters that should be paid attention to in each step:

[0095] During the preparation of L1, base layer 1: the speed is controlled between 1-10 mm / s to ensure uniform stretching and calendering of the PTFE substrate and prevent uneven thickness of the material. The expansion temperature is set between 320-380°C to form a uniform microporous structure. The processing time and power are adjusted according to the characteristics of the base material to enhance the activity of the surface and improve the adhesion of the subsequent layers.

[0096] During the preparation of L2, anion exchange layer 2: the concentration of 4-vinylbenzyltrimethylammonium polymer is 12%, the coating speed is controlled to be uniformly coated at 20 cm / min to ensure the uniformity and smoothness of the coating, and the curing temperature is controlled between 80°C and 150°C to ensure that the polymer chains are fully cross-linked and the structural stability is improved;

[0097] During the preparation of L3, interface optimization layer 3: the ratio of aluminum hydroxide nanoparticles to ion exchange resin was 2:1, the dispersion was evenly coated by spin coating to avoid aggregation, and the curing temperature was controlled at 80°C to ensure the uniformity and stability of the final layer;

[0098] During the preparation of L4, the anti-polar degradation layer 4: the concentration of tetrahydrofuran solvent is 10%, the polyvinylidene fluoride solution is evenly coated by doctor blade coating, the drying temperature is controlled at 30°C, and the curing temperature is controlled between 80°C and 150°C to avoid material degradation caused by excessively high temperature;

[0099] During the preparation of L5 and the self-healing layer 5, polyurethane is selected as the shell material of the microcapsule monomer to ensure the stability of the microcapsule and avoid rupture during the curing process. The curing temperature is controlled at 80°C and the curing humidity is 30% to ensure the integrity and functionality of the self-healing layer.

[0100] L6. During the performance test, ensure the consistency of test conditions such as temperature, humidity, voltage, etc., so as to accurately evaluate the performance of the prepared anion exchange membrane.

[0101] like Figure 3 As shown, the base layer preparation module of the present invention is located below the anion exchange membrane preparation module, the anion exchange membrane preparation module is located below the interface optimization layer preparation module, the interface optimization layer preparation module is located below the anti-polarity degradation layer preparation module, the anti-polarity degradation layer preparation module is located below the self-healing layer preparation module, the anion membrane output end of the self-healing layer preparation module is connected to the input end of the performance test and evaluation module, and the signal output end of the performance test and evaluation module is connected to the signal input end of the process optimization module.

[0102] like Figure 4As shown, the performance test evaluation module of the present invention includes an ion exchange capacity test module, a conductivity test module, a mechanical strength test module, a self-healing effect test module, a test result processing module, a performance comprehensive evaluation module and a test evaluation report output module. The ion exchange capacity test module puts the prepared anion exchange membrane into an ion exchange solution by an immersion method, and measures the ion exchange capacity by measuring the concentration of ions adsorbed into the anion exchange membrane using an ion chromatograph. The conductivity test module measures the conductivity of the anion exchange membrane by a four-probe method. The mechanical strength test module measures the conductivity of the anion exchange membrane by a universal material testing machine. The anion exchange membrane is stretched, compressed and punctured to evaluate the mechanical properties of the anion exchange membrane. The self-healing effect testing module evaluates the self-healing effect of the anion exchange membrane by artificially damaging the anion exchange membrane, observing and recording the repair of the damaged area. The test result processing module receives the test result data of each module, and cleans, normalizes and converts the data. The comprehensive performance evaluation module evaluates the performance of the anion exchange membrane through a neural network algorithm model according to the test result data. The test evaluation report output module generates and outputs a test evaluation report by integrating the test results and performance evaluation results.

[0103] The output ends of the ion exchange capacity test module, the conductivity test module, the mechanical strength test module and the self-healing effect test module are connected to the input end of the test result processing module, the output end of the test result processing module is connected to the input end of the comprehensive performance evaluation module, and the output end of the comprehensive performance evaluation module is connected to the input end of the test evaluation report output module.

[0104] In a specific embodiment, Figure 5 As shown, the working method of the ion exchange capacity test module is:

[0105] S1. Cut the anion exchange membrane into membrane samples of required size, the mass of the cut membrane samples is 0.5 g, and soak the membrane samples in an initial ion exchange solution with a concentration of 0.01 M;

[0106] S2. Through immersion, ions in the solution are exchanged with fixed charged groups in the membrane sample;

[0107] S3. After the prescribed immersion time is over, the membrane sample is taken out from the solution and the membrane sample is thoroughly washed with deionized water to remove unreacted ions;

[0108] S4, placing the cleaned membrane sample in a new ion exchange solution with a volume of 1 L to release the adsorbed ions back into the solution;

[0109] S5. Using an ion chromatograph to measure the concentration of ions released in the solution, that is, the ion concentration of the solution after equilibrium is 0.008 M;

[0110] S6. Calculate the ion exchange capacity of the membrane sample according to the ion exchange capacity calculation formula:

[0111]

[0112] The ion exchange capacity of the membrane sample was calculated to be 0.004 meq / g.

[0113] like Figure 6 As shown, in a specific embodiment, the working method of the self-healing effect testing module is:

[0114] S1. Cutting an anion exchange membrane of standard size as a membrane sample, wherein the surface of the sample is smooth and has no obvious defects, observing and recording the microstructure before damage by scanning electron microscopy, and artificially making cracks of about 2 mm in length and holes of about 1 mm in diameter on the surface of the membrane sample;

[0115] S2. Immediately after the film sample was damaged, the scanning electron microscope was used to observe the crack length of 2.05 mm and the hole diameter of 1.02 mm.

[0116] S3, exposing the damaged film sample to an environment with a temperature of 25°C and a relative humidity of 65% for 48 hours;

[0117] S4. After 48 hours, the microstructure of the repaired area was observed again using SEM. The length of the crack was reduced to 0.10 mm after self-healing, and the hole was basically closed. The hardness of the repaired area was measured using a hardness tester, and the hardness of the repaired area was reduced from 3.2 GPa to 2.9 GPa. The elastic modulus of the repaired membrane sample was measured using a dynamic mechanical analyzer, and the elastic modulus was reduced from the original 900 MPa to 860 MPa.

[0118] S5. Changes in the chemical composition of the repaired area were analyzed by infrared spectroscopy. The infrared spectrum showed that there was a slight shift in some absorption peaks in the repaired area, indicating a slight change in the chemical structure;

[0119] S6. The repaired membrane samples showed good self-healing effects in microstructure, mechanical properties and chemical properties. The effective closure of cracks and holes indicated that the material had high self-healing ability.

[0120] In a specific embodiment, Figure 7 As shown, the working method of the comprehensive performance evaluation module is:

[0121] S1. Collect data including ion exchange capacity, conductivity, mechanical strength and self-healing effect of anion exchange membranes, annotate the data according to performance, and divide the annotated data set into a test set and a training set;

[0122] S2. Construct a neural network algorithm model to test and train the data. The neural network algorithm model includes an input layer, a depthwise separable convolution layer, a feature fusion layer, a densely connected layer, an attention mechanism layer and an output layer. The output end of the input layer is connected to the input end of the feature fusion layer, the output end of the feature fusion layer is connected to the input end of the depthwise separable convolution layer, the output end of the depthwise separable convolution layer is connected to the input end of the densely connected layer, the output end of the densely connected layer is connected to the input end of the attention mechanism layer, and the output end of the attention mechanism layer is connected to the input end of the output layer.

[0123] S3. Extracting the features of ion exchange capacity, conductivity, mechanical strength and self-healing effect according to the performance indicators through the depth-separable convolution layer, wherein the depth-separable convolution includes depth-wise convolution and point-wise convolution, and the feature extraction is:

[0124]

[0125] In formula (2), D is the intermediate feature vector of the deep convolution, W pqm is the weight of the depth convolution kernel for the mth channel, F is the intermediate feature vector of the point-by-point convolution, is the weight of the point-by-point convolution kernel, b m is the bias of the depthwise convolution, b l is the bias of the point-wise convolution.

[0126] S4, weighted fusion of the extracted features is performed through a feature fusion layer, wherein the feature fusion is:

[0127] f=α·f a +β·f b +δ·f c +γ·f d (3)

[0128] In formula (3), α is the ion exchange capacity f a The weight of β is the conductivity f b The weight of δ is the mechanical strength f c The weight of γ is the self-healing effect f d The weight of .

[0129] S5. The extracted feature data is transformed nonlinearly and combined through the dense connection layer, and the key features are focused through the attention mechanism layer. Finally, the output layer regresses the extracted feature data to output a comprehensive score that can integrate all performance indicators. The regression output is:

[0130] F Score =W o ·F attended +b o (4)

[0131] In formula (4), W o is the weight matrix of the output layer, F attended is the feature vector after attention mechanism processing, b o is the bias of the output layer;

[0132] S6. Use the training set for back propagation, reduce the loss function by gradient descent during the iteration process, and output the performance evaluation model of the anion exchange membrane when the loss function is less than the set threshold;

[0133] S7. Input the received test results of ion exchange capacity, conductivity, mechanical strength and self-healing effect of the anion exchange membrane into the performance evaluation model, and output the comprehensive performance score of the anion exchange membrane through the performance evaluation model to evaluate the performance of the anion exchange membrane.

[0134] In a specific embodiment, the performance of 5 membrane samples was evaluated by the comprehensive performance evaluation module, and the evaluation results are shown in the following table:

[0135]

[0136] Sample 1 and Sample 4: have the same performance indicators and the same comprehensive score, indicating that the performance evaluation model of the present invention provides consistent evaluation for samples with similar characteristics.

[0137] Sample 2: The ion exchange capacity and mechanical strength of sample 2 are slightly higher than those of samples 1 and 4, and the performance evaluation model of the present invention also gives a slightly higher score accordingly.

[0138] Sample 3: showed slightly lower ion exchange capacity, conductivity, mechanical strength, and self-healing effect than the other samples, and thus received a lower score.

[0139] Sample 5: exhibited the best ion exchange capacity, conductivity, mechanical strength, and self-healing effect, and therefore received the highest score.

[0140] It can be seen from the evaluation results that the performance evaluation model of the present invention can distinguish the performance of different samples based on the subtle differences in various performance indicators. This subtle difference is achieved through the feature extraction and weighting process of the depth-separable convolution layer, feature fusion layer, and attention mechanism layer. Through such an evaluation, researchers or producers can better understand the performance of each sample and optimize or select as needed.

[0141] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. An anion exchange membrane for AEM water electrolysis, characterized in that: include: A base layer (1), using a polytetrafluoroethylene polymer substrate as a base to support the entire membrane structure, the polytetrafluoroethylene polymer substrate is capable of ion transmission, and the base layer (1) is located at the bottom layer; The anion exchange layer (2) uses a 4-vinylbenzyltrimethylammonium polymer having anion exchange function and is capable of transmitting anions. The anion exchange layer (2) is closely attached to the base layer (1); An interface optimization layer (3) is introduced into the hydrophilic aluminum hydroxide nanoparticles to adjust the hydrophilicity of the membrane surface, and an ion exchange resin is introduced into the interface optimization layer (3) to increase the cation rejection effect, and the interface optimization layer (3) is located on the surface of the anion exchange layer (2); An anti-polar degradation layer (4) is introduced with a fluorinated polymer having chemical stability so that the membrane can resist degradation under oxidation and alkaline conditions, and the anti-polar degradation layer (4) is coated on the interface optimization layer (3); The self-healing layer (5) incorporates a self-healing microencapsulated monomer polymer, which will break when a tiny crack occurs in the membrane, release the monomer and trigger in-situ polymerization to repair the membrane damage. The self-healing layer (5) is dispersed on the anti-polar degradation layer (4).

2. According to claim 1, an anion exchange membrane for AEM water electrolysis, characterized in that: In the anion exchange layer, the 4-vinylbenzyltrimethylammonium polymer has a benzene ring, a benzyl group is connected to the 4 position of the benzene ring, a vinyl group is connected to the other end of the benzyl group, and a trimethylammonium group is connected to the other side of the vinyl group.

3. A method for preparing an anion exchange membrane for AEM water electrolysis, characterized in that: The preparation of an anion exchange membrane for AEM water electrolysis according to claim 1 comprises the following steps: Step (1), preparing a base layer (1); The base layer (1) is prepared by a base layer preparation module, wherein the base layer preparation module cleans a polytetrafluoroethylene substrate, prepares a microporous structure to form the base layer (1) by stretching, calendering and puffing, and performs surface activation treatment on the base layer (1) by plasma treatment; Step (2), forming an anion exchange layer (2); An anion exchange layer (2) is prepared by an anion exchange layer preparation module, wherein the anion preparation module synthesizes a 4-vinylbenzyltrimethylammonium polymer and dissolves the 4-vinylbenzyltrimethylammonium polymer in ethanol, and the solution containing the 4-vinylbenzyltrimethylammonium polymer is evenly coated on the base layer (1) by a coating technique, and the coating is thermally cured to form a stable anion exchange layer (2); Step (3), preparing an interface optimization layer (3); An interface optimization layer (3) is prepared by an interface optimization layer preparation module, wherein the interface optimization layer preparation module mixes aluminum hydroxide nanoparticles and ion exchange resin in an aqueous solvent at a ratio of 2:1 to form a dispersion, and the dispersion is coated on the anion exchange layer (2), and a drying and curing process is performed to ensure that the aluminum hydroxide and the ion exchange resin are evenly distributed to form the interface optimization layer (3); Step (4), applying an anti-polar degradation layer (4); The anti-polar degradation layer (4) is prepared by an anti-polar degradation layer preparation module, wherein the anti-polar degradation layer preparation module selects a polyvinylidene fluoride polymer, and dissolves the polyvinylidene fluoride polymer at a high temperature by using a tetrahydrofuran solvent, and coats the solution containing the fluorinated polymer on the interface optimization layer (3) by using a coating technology, and forms the anti-polar degradation layer (4) after drying and curing; Step (5), integrating the self-healing layer (5); The self-healing layer (5) is prepared by a self-healing layer preparation module, wherein the self-healing layer preparation module prepares a dispersion containing microencapsulated monomer polymers, and evenly disperses the microcapsule dispersion in the anti-polar degradation layer (4) to form the self-healing layer (5), and ensures that the microcapsules will not break during the curing process; Step (6), performance test evaluation; The prepared anion exchange membrane is tested and evaluated using a performance test evaluation module, wherein the performance test evaluation module tests the ion exchange capacity, conductivity, mechanical strength, chemical stability and self-healing effect of the prepared anion exchange membrane, and analyzes and evaluates the test results through a neural network algorithm model, and outputs the evaluation results; Step (7) process optimization; The process optimization module is used to optimize the preparation process of the anion exchange membrane. The process optimization module improves the preparation conditions of the anion exchange membrane through a genetic algorithm model according to the test evaluation results, and optimizes the preparation parameters and the preparation process.

4. The method for preparing an anion exchange membrane for AEM water electrolysis according to claim 3, characterized in that: The base layer preparation module is located below the anion exchange membrane preparation module, the anion exchange membrane preparation module is located below the interface optimization layer preparation module, the interface optimization layer preparation module is located below the anti-polarity degradation layer preparation module, the anti-polarity degradation layer preparation module is located below the self-healing layer preparation module, the anion membrane output end of the self-healing layer preparation module is connected to the input end of the performance test and evaluation module, and the signal output end of the performance test and evaluation module is connected to the signal input end of the process optimization module.

5. The method for preparing an anion exchange membrane for AEM water electrolysis according to claim 3, characterized in that: The performance test evaluation module includes an ion exchange capacity test module, a conductivity test module, a mechanical strength test module, a self-healing effect test module, a test result processing module, a comprehensive performance evaluation module and a test evaluation report output module. The ion exchange capacity test module places the prepared anion exchange membrane into an ion exchange solution by an immersion method, and measures the ion exchange capacity by using an ion chromatograph to measure the concentration of ions adsorbed into the anion exchange membrane. The conductivity test module measures the conductivity of the anion exchange membrane by a four-probe method. The mechanical strength test module measures the conductivity of the anion exchange membrane by a universal material testing machine. The membrane is stretched, compressed and punctured to evaluate the mechanical properties of the anion exchange membrane. The self-healing effect testing module evaluates the self-healing effect of the anion exchange membrane by artificially damaging the anion exchange membrane, observing and recording the repair of the damaged area. The test result processing module receives the test result data of each module, and cleans, normalizes and converts the data. The comprehensive performance evaluation module evaluates the performance of the anion exchange membrane through a neural network algorithm model according to the test result data. The test evaluation report output module generates and outputs a test evaluation report by integrating the test results and performance evaluation results. The output ends of the ion exchange capacity test module, the conductivity test module, the mechanical strength test module and the self-healing effect test module are connected to the input end of the test result processing module, the output end of the test result processing module is connected to the input end of the comprehensive performance evaluation module, and the output end of the comprehensive performance evaluation module is connected to the input end of the test evaluation report output module.

6. The method for preparing an anion exchange membrane for AEM water electrolysis according to claim 5, characterized in that: The working method of the ion exchange capacity test module is: S1, cutting an anion exchange membrane of standard size as a membrane sample, and immersing the membrane sample in an ion exchange solution of known concentration; S2. Through immersion, ions in the solution are exchanged with fixed charged groups in the membrane sample; S3. After the prescribed immersion time is over, the membrane sample is taken out from the solution and the membrane sample is thoroughly washed with deionized water to remove unreacted ions; S4, placing the cleaned membrane sample in a new ion exchange solution to release the adsorbed ions back into the solution; S5, using an ion chromatograph to measure the concentration of ions released in the solution to obtain the amount of ions adsorbed by the membrane sample; S6. Calculate the ion exchange capacity of the membrane sample according to the amount of ions adsorbed by the membrane sample and the mass of the membrane sample, the calculation method being: In formula (1), IEC is the ion exchange capacity in milliequivalents / gram, C e is the ion concentration in the equilibrium solution in mol / L, C i is the ion concentration in the initial solution in mol / L, V is the volume of the solution in liters, and W is the mass of the dry film sample in grams.

7. The method for preparing an anion exchange membrane for AEM water electrolysis according to claim 5, characterized in that: The working method of the self-healing effect test module is: S1. Cutting an anion exchange membrane of standard size as a membrane sample, wherein the surface of the sample is smooth and has no obvious defects, and artificially making cracks, scratches and holes on the surface of the membrane sample; S2. immediately start observing the membrane sample by scanning electron microscopy after the damage is caused, and record the morphology, size and location of the damaged area; S3. Expose the damaged film sample to a simulated temperature, humidity and atmospheric pressure environment, and regularly observe and record the repair status of the damaged area, including the degree, time and method of repair; S4. Observe the microstructure of the repaired area of ​​the repaired membrane sample by scanning electron microscopy to evaluate the quality of crack filling and interface fusion; S4. For the repaired membrane sample, a hardness test is performed on the repaired area by a hardness tester, and the elastic modulus of the repaired membrane sample is measured by a dynamic mechanical analyzer to evaluate the mechanical properties of the repaired membrane sample; S5. For the repaired membrane sample, the changes in the chemical composition and molecular structure of the repaired area are detected by infrared spectroscopy analysis to evaluate the chemical properties of the repaired membrane sample; S6. The self-healing effect of the prepared anion exchange membrane was evaluated by comparing the microstructure, mechanical properties, and chemical properties of the membrane samples before and after repair.

8. The method for preparing an anion exchange membrane for AEM water electrolysis according to claim 5, characterized in that: The working method of the comprehensive performance evaluation module is: S1. Collect data including ion exchange capacity, conductivity, mechanical strength and self-healing effect of anion exchange membranes, annotate the data according to performance, and divide the annotated data set into a test set and a training set; S2. Construct a neural network algorithm model to test and train the data. The neural network algorithm model includes an input layer, a depthwise separable convolution layer, a feature fusion layer, a densely connected layer, an attention mechanism layer and an output layer. The output end of the input layer is connected to the input end of the feature fusion layer, the output end of the feature fusion layer is connected to the input end of the depthwise separable convolution layer, the output end of the depthwise separable convolution layer is connected to the input end of the densely connected layer, the output end of the densely connected layer is connected to the input end of the attention mechanism layer, and the output end of the attention mechanism layer is connected to the input end of the output layer. S3. Extracting the features of ion exchange capacity, conductivity, mechanical strength and self-healing effect according to the performance indicators through the depth-separable convolution layer, wherein the depth-separable convolution includes depth-wise convolution and point-wise convolution, and the feature extraction is: In formula (2), D is the intermediate feature vector of the deep convolution, W pqm is the weight of the depth convolution kernel for the mth channel, F is the intermediate feature vector of the point-by-point convolution, is the weight of the point-by-point convolution kernel, b m is the bias of the depthwise convolution, b l is the bias of the point-wise convolution. S4, weighted fusion of the extracted features is performed through a feature fusion layer, wherein the feature fusion is: f=α·f a +β·f b +δ·f c +γ·f d (3) In formula (3), α is the ion exchange capacity f a The weight of β is the conductivity f b The weight of δ is the mechanical strength f c The weight of γ is the self-healing effect f d The weight of . S5. The extracted feature data is transformed nonlinearly and combined through the dense connection layer, and the key features are focused through the attention mechanism layer. Finally, the output layer regresses the extracted feature data to output a comprehensive score that can integrate all performance indicators. The regression output is: F Score =W o ·F attended +b o (4) In formula (4), W o is the weight matrix of the output layer, F attended is the feature vector after attention mechanism processing, b o is the bias of the output layer; S6. Use the training set for back propagation, reduce the loss function by gradient descent during the iteration process, and output the performance evaluation model of the anion exchange membrane when the loss function is less than the set threshold; S7. Input the received test results of ion exchange capacity, conductivity, mechanical strength and self-healing effect of the anion exchange membrane into the performance evaluation model, and output the comprehensive performance score of the anion exchange membrane through the performance evaluation model to evaluate the performance of the anion exchange membrane.

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