Advancing Porous Electrode Design for PEM Fuel Cells: From Physics to Artificial Intelligence

Guofu Ren; Zhiguo Qu; Zhiqiang Niu; Yun Wang

doi:10.1007/s41918-025-00243-2

Electrochemical Energy Reviews ›› 2025, Vol. 8 ›› Issue (1) :6 DOI: 10.1007/s41918-025-00243-2

Review Article

review-article

Advancing Porous Electrode Design for PEM Fuel Cells: From Physics to Artificial Intelligence

Guofu Ren ¹^,²
, Zhiguo Qu ¹^,^a
, Zhiqiang Niu ³
, Yun Wang ²^,^b

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¹MOE Key Laboratory of Thermo-Fluid Science and Engineering, School of Energy and Power Engineering, Xi’an Jiaotong University, 710049, Xi’an, Shaanxi, China

²Renewable Energy Resources Lab (RERL), Department of Mechanical and Aerospace Engineering, University of California, Irvine, 92697-3975, Irvine, CA, USA

³Department of Aeronautical and Automotive Engineering, Loughborough University, LE11 3TU, Loughborough, UK

^a zgqu@mail.xjtu.edu.cn

^b yunw@uci.edu

Guofu Ren

Guofu Ren received his Bachelor’s degree from Shandong University in 2017. He is currently pursuing a Ph.D. degree at the School of Energy and Power Engineering, Xi’an Jiaotong University, under the supervision of Professor Zhiguo Qu. In 2023, he was a visiting scholar at the University of California, Irvine (UCI). His research interests are primarily focused on the regulation of pore structure and the gas–liquid transport mechanisms in porous electrodes of proton exchange membrane fuel cells.

Zhiguo Qu

Zhiguo Qu is a professor of Energy & Power Engineering at Xi’an Jiaotong University. Dr. Qu obtained the Ph.D. degree in engineering thermophysics from Xi’an Jiaotong University in 2005. He worked as a visiting scholar at Advanced Heat Transfer, LLC, USA, and Pennsylvania State University in 2006 and 2013, respectively. His main research interests include multiscale transport phenomena, energy conversion and storage, micro-nanofluidics, and electronic thermal management. Dr. Qu is a recipient of China National Funds for Distinguished Young Scientists, and Young Scholar Fund from Fok Ying Tung Education Foundation of China.

Zhiqiang Niu

Zhiqiang Niu is an assistant professor in sustainable transport technologies at the Department of Aeronautical and Automotive Engineering at Loughborough University, UK. He earned both his Bachelor’s and Ph.D. degrees from Tianjin University in 2014 and 2020, respectively. Dr. Niu’s research centers on sustainable energy solutions, particularly in the realms of fuel cells and batteries. His innovative approach leverages the development of customized artificial intelligence tools and computational methodologies to drive advancements in these technologies. He has been awarded the prestigious Royal Society-K.C. Wong International Fellowship and the Kan Tong Po International Fellowship.

Yun Wang

Yun Wang received his B.S. and M.S. degrees in mechanics and engineering science from Peking University in 1998 and 2001, respectively. He went to the Pennsylvania State University for a Ph.D degree in 2006 and then joined as a faculty member in Mechanical and Aerospace Engineering of the UC Irvine (UCI). Dr. Wang has produced about 100 publications in the areas of PEM fuel cells, PEM electrolysis cells, Li-air batteries, and other energy systems. Several of his seminal works are highly cited in major energy journals. Dr. Wang is currently a professor at UCI and an RSC and ASME fellow.

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Abstract

Proton exchange membrane (PEM) fuel cells play a pivotal role in a sustainable society through the direct conversion of hydrogen energy to electricity. Porous electrode materials, including porous media flow fields, gas diffusion layers, microporous layers, and catalyst layers, are essential for fuel cell operation, efficiency, and durability, in which complex multiphysics transport (e.g., hydrogen/oxygen transport, electron/proton conduction, heat transfer, and liquid water flow) and electrochemical reactions (e.g., the oxygen reduction reaction at the cathode and the hydrogen oxidation reaction at the anode) occur, as revealed by both experiments and multiphysics modeling. In recent years, artificial intelligence (AI) has demonstrated significant efficacy in the research and development (R&D) of electrode materials. Artificial neural networks (ANNs), convolutional neural networks (CNNs), deep neural networks (DNNs), generative adversarial neural networks (GANs), support vector machines (SVMs), and genetic algorithms (GAs) have been applied to design and optimize porous structures, compositions, materials, and surface properties for PEM fuel cells, demonstrating reliable and fast optimization and prediction capabilities. This article reviews the main physics and explores AI to advance porous electrode design for PEM fuel cells. Unlike traditional experimental and simulation-based approaches, AI provides superior computational efficiency, enabling faster and more cost-effective exploration of complex design parameters. In the end, future R&D directions for next-generation highly effective electrodes are discussed.

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Keywords

Fuel cells / Porous electrode / Physics / Artificial intelligence / Review

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Guofu Ren, Zhiguo Qu, Zhiqiang Niu, Yun Wang. Advancing Porous Electrode Design for PEM Fuel Cells: From Physics to Artificial Intelligence. Electrochemical Energy Reviews, 2025, 8(1): 6 DOI:10.1007/s41918-025-00243-2

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References

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[1]	Zhang GB, Qu ZG, Tao WQet al. . Porous flow field for next-generation proton exchange membrane fuel cells: materials, characterization, design, and challenges. Chem. Rev.. 2023, 123: 989-1039.

[2]	Zhang GB, Qu ZG, Tao WQet al. . Advancing next-generation proton-exchange membrane fuel cell development in multi-physics transfer. Joule. 2024, 8: 45-63.

[3]	Ren GF, Qu ZG, Wang XLet al. . Liquid water transport and mechanical performance of electrospun gas diffusion layers. Int. J. Green Energy. 2022, 19: 210-218.

[4]	Ren GF, Qu ZG, Wang XLet al. . Enhancing the performance of proton exchange membrane fuel cell using nanostructure gas diffusion layers with gradient pore structures. Int. J. Hydrog. Energy. 2024, 52: 1161-1172.

[5]	Park JE, Hwang W, Lim MSet al. . Achieving breakthrough performance caused by optimized metal foam flow field in fuel cells. Int. J. Hydrog. Energy. 2019, 44: 22074-22084.

[6]	Awin Y, Dukhan N. Experimental performance assessment of metal-foam flow fields for proton exchange membrane fuel cells. Appl. Energy. 2019, 252. 113458

[7]	Shin DK, Yoo JH, Kang DGet al. . Effect of cell size in metal foam inserted to the air channel of polymer electrolyte membrane fuel cell for high performance. Renew. Energy. 2018, 115: 663-675.

[8]	Tseng CJ, Tsai BT, Liu ZSet al. . A PEM fuel cell with metal foam as flow distributor. Energy Convers. Manag.. 2012, 62: 14-21.

[9]	Kang DG, Lee DK, Choi JMet al. . Study on the metal foam flow field with porosity gradient in the polymer electrolyte membrane fuel cell. Renew. Energy. 2020, 156: 931-941.

[10]	Kim J, Cunningham N. Development of porous carbon foam polymer electrolyte membrane fuel cell. J. Power. Sources. 2010, 195: 2291-2300.

[11]	Park JE, Lim J, Lim MSet al. . Gas diffusion layer/flow-field unified membrane-electrode assembly in fuel cell using graphene foam. Electrochim. Acta. 2019, 323. 134808

[12]	Yoshizawa K, Ikezoe K, Tasaki Yet al. . Analysis of gas diffusion layer and flow-field design in a PEMFC using neutron radiography. J. Electrochem. Soc.. 2008, 155: B223.

[13]	Wang H, Yang GG, Li SAet al. . Numerical study on permeability of gas diffusion layer with porosity gradient using lattice Boltzmann method. Int. J. Hydrog. Energy. 2021, 4622107-22121.

[14]	Ren GF, Qu ZG, Wang XLet al. . Electrospun fabrication and experimental characterization of highly porous microporous layers for PEM fuel cells. Int. J. Hydrog. Energy. 2024, 55: 455-463.

[15]	Chen YC, Karageorgiou C, Eller Jet al. . Determination of the porosity and its heterogeneity of fuel cell microporous layers by X-ray tomographic microscopy. J. Power. Sources. 2022, 539. 231612

[16]	Neehall ND, Ismail MS, Hughes KJet al. . Effect of composition and structure of gas diffusion layer and microporous layer on the through-plane gas permeability of PEFC porous media. Int. J. Energy Res.. 2021, 45: 20988-21005.

[17]	Orogbemi OM, Ingham DB, Ismail MSet al. . Through-plane gas permeability of gas diffusion layers and microporous layer: effects of carbon loading and sintering. J. Energy Inst.. 2018, 91: 270-278.

[18]	Orogbemi OM, Ingham DB, Ismail MSet al. . On the gas permeability of the microporous layer used in polymer electrolyte fuel cells. J. Energy Inst.. 2018, 91: 894-901.

[19]	Malekian A, Salari S, Stumper Jet al. . Effect of compression on pore size distribution and porosity of PEM fuel cell catalyst layers. Int. J. Hydrog. Energy. 2019, 44: 23396-23405.

[20]	Sabharwal M, Secanell M. Understanding the effect of porosity and pore size distribution on low loading catalyst layers. Electrochim. Acta. 2022, 419. 140410

[21]	Zhao J, Shahgaldi S, Alaefour Iet al. . Gas permeability of catalyzed electrodes in polymer electrolyte membrane fuel cells. Appl. Energy. 2018, 209: 203-210.

[22]	Zheng ZJ, Wang CH, Chen CDet al. . Numerical study of hydrothermal and flow characteristics of PEMFC folded porous cathode flow field. Int. J. Hydrog. Energy. 2024, 72: 861-877.

[23]	Li QF, Suo MS, Sun Ket al. . Experimental study on the performance of proton exchange membrane fuel cell with foam/channel composite flow fields. J. Power. Sources. 2024, 620. 235244

[24]	Wang Y, Chen KS. Effect of spatially-varying GDL properties and land compression on water distribution in PEM fuel cells. J. Electrochem. Soc.. 2011, 158: B1292.

[25]	Ren GF, Lai T, Qu ZGet al. . Electrospun gas diffusion layers with reverse gradient pore structures for proton exchange membrane fuel cells under low humidity. Appl. Therm. Eng.. 2024, 239. 122109

[26]	Pan ZF, Wu LZ, Xie FJet al. . Engineered wettability-gradient porous structure enabling efficient water manipulation in regenerative fuel cells. Energy AI. 2024, 17. 100400

[27]	Lou MY, Chen L, Lu Ket al. . An experimental study on PEMFC water management based on the synergistic interaction of flow field and MPL. Int. J. Hydrog. Energy. 2024, 691246-1254.

[28]	Zhao CF, Yuan S, Cheng XJet al. . The effect of catalyst layer design on catalyst utilization in PEMFC studied via stochastic reconstruction method. Energy AI. 2023, 13. 100245

[29]	Sassin MB, Garsany Y, Atkinson RWIIIet al. . Understanding the interplay between cathode catalyst layer porosity and thickness on transport limitations en route to high-performance PEMFCs. Int. J. Hydrog. Energy. 2019, 44: 16944-16955.

[30]	Ma XY, Zhang WK, Yin Yet al. . Multi-objective optimization of lithium-ion battery designs considering the dilemma between energy density and rate capability. Energy AI. 2024, 18. 100416

[31]	Wu LZ, Pan ZF, Yuan Set al. . Optimization of dual-layer flow field in a water electrolyzer using a data-driven surrogate model. Energy AI. 2024, 18. 100411

[32]	Pang YH, Wang Y. Water spatial distribution in polymer electrolyte membrane fuel cell: convolutional neural network analysis of neutron radiography. Energy AI. 2023, 14. 100265

[33]	Qiu YB, Wu P, Miao TWet al. . An intelligent approach for contact pressure optimization of PEM fuel cell gas diffusion layers. Appl. Sci.. 2020, 10: 4194.

[34]	Santamaria AD, Mortazavi M, Chauhan Vet al. . Machine learning applications of two-phase flow data in polymer electrolyte fuel cell reactant channels. J. Electrochem. Soc.. 2021, 168. 054505

[35]	Chauhan V, Mortazavi M, Benner JZet al. . Two-phase flow characterization in PEM fuel cells using machine learning. Energy Rep.. 2020, 62713-2719.

[36]	Wang Y, Seo B, Wang BWet al. . Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology. Energy AI. 2020, 1. 100014

[37]	Zhao J, Li XG, Shum Cet al. . A review of physics-based and data-driven models for real-time control of polymer electrolyte membrane fuel cells. Energy AI. 2021, 6. 100114

[38]	Yang F, Xu XM, Li YHet al. . A review on mass transfer in multiscale porous media in proton exchange membrane fuel cells: mechanism, modeling, and parameter identification. Energies. 2023, 16: 3547.

[39]	Brus G. Overcoming a recent impasse in the application of artificial neural networks as solid oxide fuel cells simulator with computational topology. Energy AI. 2023, 14. 100291

[40]	Ding R, Zhang SQ, Chen YWet al. . Application of machine learning in optimizing proton exchange membrane fuel cells: a review. Energy AI. 2022, 9. 100170

[41]	Wang Y. Porous-media flow fields for polymer electrolyte fuel cells. J. Electrochem. Soc.. 2009, 156B1124.

[42]	Zheng B, Wang Z, Wang Y. Porous media flow field for polymer electrolyte membrane fuel cell: depression of gas diffusion layer intrusion, deformation, and delamination. Int. J. Energy Res.. 2022, 4620039-20049.

[43]	Das PK, Jiao K, Wang Yet al. . Fuel Cells for Transportation: Fundamental Principles and Applications. 2023, Amsterdam, Elsevier.

[44]	Tsai BT, Tseng CJ, Liu ZSet al. . Effects of flow field design on the performance of a PEM fuel cell with metal foam as the flow distributor. Int. J. Hydrog. Energy. 2012, 37: 13060-13066.

[45]	Wan ZM, Sun Y, Yang Cet al. . Experimental performance investigation on the arrangement of metal foam as flow distributors in proton exchange membrane fuel cell. Energy Convers. Manag.. 2021, 231. 113846

[46]	Mao XY, Li YF, Hu XFet al. . Expanded graphite (EG)/Ni@melamine foam (MF)/EG sandwich-structured flexible bipolar plate with excellent electrical conductivity, mechanical properties, and gas permeability. Appl. Energy. 2023, 338. 120929

[47]	Park JE, Lim J, Kim Set al. . Enhancement of mass transport in fuel cells using three-dimensional graphene foam as flow field. Electrochim. Acta. 2018, 265: 488-496.

[48]	Li ZJ, Chen WS, Hao H. Mechanical properties of carbon foams under quasi-static and dynamic loading. Int. J. Mech. Sci.. 2019, 1612: 105039.

[49]	Zhang JC, Luo RY, Xiang Qet al. . Compressive fracture behavior of 3D needle-punched carbon/carbon composites. Mater. Sci. Eng. A. 2011, 5285002-5006.

[50]	Lee H, Kim H, Kim H. Application of carbon felt as a flow distributor for polymer electrolyte membrane fuel cells. J. Electrochem. Soc.. 2019, 166: F74-F78.

[51]	Carton JG, Olabi AG. Representative model and flow characteristics of open pore cellular foam and potential use in proton exchange membrane fuel cells. Int. J. Hydrog. Energy. 2015, 40: 5726-5738.

[52]	Jo A, Ju H. Numerical study on applicability of metal foam as flow distributor in polymer electrolyte fuel cells (PEFCs). Int. J. Hydrog. Energy. 2018, 43: 14012-14026.

[53]	Kim J, Luo G, Wang CY. Modeling liquid water re-distributions in bi-porous layer flow-fields of proton exchange membrane fuel cells. J. Power. Sources. 2018, 400: 284-295.

[54]	Bao ZM, Niu ZQ, Jiao K. Numerical simulation for metal foam two-phase flow field of proton exchange membrane fuel cell. Int. J. Hydrog. Energy. 2019, 44: 6229-6244.

[55]	Li ZX, Bai F, He Pet al. . Three-dimensional performance simulation of PEMFC of metal foam flow plate reconstructed with improved full morphology. Int. J. Hydrog. Energy. 2023, 48: 27778-27789.

[56]	Zhang GB, Bao ZM, Xie Bet al. . Three-dimensional multi-phase simulation of PEM fuel cell considering the full morphology of metal foam flow field. Int. J. Hydrog. Energy. 2021, 46: 2978-2989.

[57]	Amini S, Mohaghegh S. Application of machine learning and artificial intelligence in proxy modeling for fluid flow in porous media. Fluids. 2019, 4: 126.

[58]	Tian JW, Qi CC, Sun YFet al. . Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods. Eng. Comput.. 2021, 373455-3471.

[59]	Alizadeh R, Abad JMN, Ameri Aet al. . A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems—heat transfer in a hybrid nanofluid flow in porous media. J. Taiwan Inst. Chem. Eng.. 2021, 124: 290-306.

[60]	Zhang GB, Wu LZ, Jiao Ket al. . Optimization of porous media flow field for proton exchange membrane fuel cell using a data-driven surrogate model. Energy Convers. Manag.. 2020, 226. 113513

[61]	Park S, Popov BN. Effect of a GDL based on carbon paper or carbon cloth on PEM fuel cell performance. Fuel. 2011, 90: 436-440.

[62]	Mortazavi M, Santamaria AD, Chauhan Vet al. . Effect of PEM fuel cell porous media compression on in-plane transport phenomena. J. Power Sources Adv.. 2020, 1. 100001

[63]	Wang XL, Wang WK, Qu ZGet al. . Surface roughness dominated wettability of carbon fiber in gas diffusion layer materials revealed by molecular dynamics simulations. Int. J. Hydrog. Energy. 2021, 46: 26489-26498.

[64]	Wang XL, Qu ZG, Lai Tet al. . Enhancing water transport performance of gas diffusion layers through coupling manipulation of pore structure and hydrophobicity. J. Power. Sources. 2022, 525. 231121

[65]	Wang XL, Qu ZG, Ren GF. Collective enhancement in hydrophobicity and electrical conductivity of gas diffusion layer and the electrochemical performance of PEMFCs. J. Power. Sources. 2023, 575. 233077

[66]	Ko D, Doh S, Park HSet al. . The effect of through plane pore gradient GDL on the water distribution of PEMFC. Int. J. Hydrog. Energy. 2018, 43: 2369-2380.

[67]	Kong IM, Jung A, Kim MS. Investigations on the double gas diffusion backing layer for performance improvement of self-humidified proton exchange membrane fuel cells. Appl. Energy. 2016, 176: 149-156.

[68]	Kong IM, Choi JW, Kim SIet al. . Experimental study on the self-humidification effect in proton exchange membrane fuel cells containing double gas diffusion backing layer. Appl. Energy. 2015, 145: 345-353.

[69]	Fu XW, Wen QL, Han JLet al. . One-step to prepare high-performance gas diffusion layer (GDL) with three different functional layers for proton exchange membrane fuel cells (PEMFCs). Int. J. Hydrog. Energy. 2022, 47: 25769-25779.

[70]	Oh H, Park J, Min Ket al. . Effects of pore size gradient in the substrate of a gas diffusion layer on the performance of a proton exchange membrane fuel cell. Appl. Energy. 2015, 149: 186-193.

[71]	Gerteisen D, Heilmann T, Ziegler C. Enhancing liquid water transport by laser perforation of a GDL in a PEM fuel cell. J. Power. Sources. 2008, 177: 348-354.

[72]	Gerteisen D, Sadeler C. Stability and performance improvement of a polymer electrolyte membrane fuel cell stack by laser perforation of gas diffusion layers. J. Power. Sources. 2010, 195: 5252-5257.

[73]	Alink R, Gerteisen D, Mérida W. Investigating the water transport in porous media for PEMFCs by liquid water visualization in ESEM. Fuel Cells. 2011, 11: 481-488.

[74]	Markötter H, Alink R, Haußmann Jet al. . Visualization of the water distribution in perforated gas diffusion layers by means of synchrotron X-ray radiography. Int. J. Hydrog. Energy. 2012, 37: 7757-7761.

[75]	Alink R, Haußmann J, Markötter Het al. . The influence of porous transport layer modifications on the water management in polymer electrolyte membrane fuel cells. J. Power. Sources. 2013, 233: 358-368.

[76]	Okuhata G, Tonoike T, Nishida Ket al. . Effect of perforation structure of cathode GDL on liquid water removal in PEFC. ECS Trans.. 2013, 58: 1047-1057.

[77]	Haußmann J, Markötter H, Alink Ret al. . Synchrotron radiography and tomography of water transport in perforated gas diffusion media. J. Power. Sources. 2013, 239: 611-622.

[78]	Manahan MP, Hatzell MC, Kumbur ECet al. . Laser perforated fuel cell diffusion media. Part I: Related changes in performance and water content. J. Power. Sources. 2011, 196: 5573-5582.

[79]	Manahan M, Mench M. Increased performance of PEFCs with engineered mass-transport pathways. ECS Trans.. 2011, 41: 569-581.

[80]	Manahan MP, Mench MM. Laser perforated fuel cell diffusion media: engineered interfaces for improved ionic and oxygen transport. J. Electrochem. Soc.. 2012, 159: F322-F330.

[81]	Yang TF, Hsueh CY, Chen BLet al. . Effects of fluorinated ethylene propylene contents in a novel gas diffusion layer on cell performance of a proton exchange membrane fuel cell. Int. J. Energy Res.. 2022, 46: 1553-1564.

[82]	Lim C, Wang CY. Effects of hydrophobic polymer content in GDL on power performance of a PEM fuel cell. Electrochim. Acta. 2004, 49: 4149-4156.

[83]	Jia JK, Liu XF, Liu Fet al. . Designing independent water transport channels to improve water flooding in ultra-thin nanoporous film cathodes for PEMFCs. Int. J. Hydrog. Energy. 2022, 47: 21261-21272.

[84]	Forner-Cuenca A, Biesdorf J, Gubler Let al. . Engineered water highways in fuel cells: radiation grafting of gas diffusion layers. Adv. Mater.. 2015, 27: 6317-6322.

[85]	Jang S, Her M, Kim Set al. . Membrane/electrode interface design for effective water management in alkaline membrane fuel cells. ACS Appl. Mater. Interfaces. 2019, 11: 34805-34811.

[86]	Chun H, Kim Y, Chae Het al. . Facile airbrush fabrication of gas diffusion layers comprising fine-patterned hydrophobic double-layer and hydrophilic channel for improved water removal in polymer electrolyte membrane fuel cells. Int. J. Precis. Eng. Manuf.-Green Tech.. 2021, 8: 1461-1469.

[87]	Forner-Cuenca A, Manzi-Orezzoli V, Kristiansen PMet al. . Mask-assisted electron radiation grafting for localized through-volume modification of porous substrates: influence of electron energy on spatial resolution. Radiat. Phys. Chem.. 2017, 135: 133-141.

[88]	Forner-Cuenca A, Manzi-Orezzoli V, Biesdorf Jet al. . Advanced water management in PEFCs: diffusion layers with patterned wettability. I. Synthetic routes, wettability tuning and thermal stability. J. Electrochem. Soc.. 2016, 163: F788.

[89]	Forner-Cuenca A, Biesdorf J, Lamibrac Aet al. . Advanced water management in PEFCs: diffusion layers with patterned wettability. II. Measurement of capillary pressure characteristic with neutron and synchrotron imaging. J. Electrochem. Soc.. 2016, 163F1038-F1048.

[90]	Forner-Cuenca A, Biesdorf J, Manzi-Orezzoli Vet al. . Advanced water management in PEFCs: diffusion layers with patterned wettability. III. Operando characterization with neutron imaging. J. Electrochem. Soc.. 2016, 163: F1389-F1398.

[91]	Calili-Cankir F, Can EM, Ingham DBet al. . Patterned hydrophobic gas diffusion layers for enhanced water management in polymer electrolyte fuel cells. Chem. Eng. J.. 2024, 484. 149711

[92]	Yan XH, Lin C, Zheng ZFet al. . Effect of clamping pressure on liquid-cooled PEMFC stack performance considering inhomogeneous gas diffusion layer compression. Appl. Energy. 2020, 258. 114073

[93]	Zhou X, Wu LZ, Niu ZQet al. . Effects of surface wettability on two-phase flow in the compressed gas diffusion layer microstructures. Int. J. Heat Mass Transf.. 2020, 151. 119370

[94]	Sinha PK, Wang CY. Pore-network modeling of liquid water transport in gas diffusion layer of a polymer electrolyte fuel cell. Electrochim. Acta. 2007, 52: 7936-7945.

[95]	Chen W, Jiang FM. Impact of PTFE content and distribution on liquid-gas flow in PEMFC carbon paper gas distribution layer: 3D lattice Boltzmann simulations. Int. J. Hydrog. Energy. 2016, 41: 8550-8562.

[96]	Huang YX, Cheng CH, Wang XDet al. . Effects of porosity gradient in gas diffusion layers on performance of proton exchange membrane fuel cells. Energy. 2010, 35: 4786-4794.

[97]	Kong IM, Jung A, Kim YSet al. . Numerical investigation on double gas diffusion backing layer functionalized on water removal in a proton exchange membrane fuel cell. Energy. 2017, 120: 478-487.

[98]	Li WZ, Yang WW, Zhang WYet al. . Three-dimensional modeling of a PEMFC with serpentine flow field incorporating the impacts of electrode inhomogeneous compression deformation. Int. J. Hydrog. Energy. 2019, 44: 22194-22209.

[99]	Chippar P, Kyeongmin O, Kang Ket al. . A numerical investigation of the effects of GDL compression and intrusion in polymer electrolyte fuel cells (PEFCs). Int. J. Hydrog. Energy. 2012, 37: 6326-6338.

[100]

Zhou X, Niu ZQ, Bao ZMet al. . Two-phase flow in compressed gas diffusion layer: finite element and volume of fluid modeling. J. Power. Sources. 2019, 437. 226933

[101]

Zhou X, Niu ZQ, Li YNet al. . Investigation of two-phase flow in the compressed gas diffusion layer microstructures. Int. J. Hydrog. Energy. 2019, 44: 26498-26516.

[102]

Jiao DK, Jiao K, Zhong SHet al. . Investigations on heat and mass transfer in gas diffusion layers of PEMFC with a gas-liquid-solid coupled model. Appl. Energy. 2022, 316. 118996

[103]

Jiao DK, Jiao K, Du Q. Numerical investigations of vapor condensation and water transport in gas diffusion layers of PEMFC. Int. J. Heat Mass Transf.. 2021, 177. 121543

[104]

Niu ZQ, Wu JT, Bao ZMet al. . Two-phase flow and oxygen transport in the perforated gas diffusion layer of proton exchange membrane fuel cell. Int. J. Heat Mass Transf.. 2019, 139: 58-68.

[105]

Niu ZQ, Bao ZM, Wu JTet al. . Two-phase flow in the mixed-wettability gas diffusion layer of proton exchange membrane fuel cells. Appl. Energy. 2018, 232: 443-450.

[106]

Niu ZQ, Wang Y, Jiao Ket al. . Two-phase flow dynamics in the gas diffusion layer of proton exchange membrane fuel cells: volume of fluid modeling and comparison with experiment. J. Electrochem. Soc.. 2018, 165: F613-F620.

[107]

Fazeli M, Hinebaugh J, Fishman Zet al. . Pore network modeling to explore the effects of compression on multiphase transport in polymer electrolyte membrane fuel cell gas diffusion layers. J. Power. Sources. 2016, 335: 162-171.

[108]

Hinebaugh J, Bazylak A. Condensation in PEM fuel cell gas diffusion layers: a pore network modeling approach. J. Electrochem. Soc.. 2010, 157: B1382.

[109]

Straubhaar B, Pauchet J, Prat M. Pore network modelling of condensation in gas diffusion layers of proton exchange membrane fuel cells. Int. J. Heat Mass Transf.. 2016, 102891-901.

[110]

Carrère P, Prat M. Impact of non-uniform wettability in the condensation and condensation-liquid water intrusion regimes in the cathode gas diffusion layer of proton exchange membrane fuel cell. Int. J. Therm. Sci.. 2019, 145. 106045

[111]

Aghighi M, Gostick J. Pore network modeling of phase change in PEM fuel cell fibrous cathode. J. Appl. Electrochem.. 2017, 47: 1323-1338.

[112]

Belgacem N, Prat M, Pauchet J. Coupled continuum and condensation–evaporation pore network model of the cathode in polymer-electrolyte fuel cell. Int. J. Hydrog. Energy. 2017, 42: 8150-8165.

[113]

Jeon DH, Kim H. Effect of compression on water transport in gas diffusion layer of polymer electrolyte membrane fuel cell using lattice Boltzmann method. J. Power. Sources. 2015, 294: 393-405.

[114]

Satjaritanun P, Weidner JW, Hirano Set al. . Micro-scale analysis of liquid water breakthrough inside gas diffusion layer for PEMFC using X-ray computed tomography and lattice Boltzmann method. J. Electrochem. Soc.. 2017, 164: E3359-E3371.

[115]

Molaeimanesh GR, Akbari MH. Impact of PTFE distribution on the removal of liquid water from a PEMFC electrode by lattice Boltzmann method. Int. J. Hydrog. Energy. 2014, 39: 8401-8409.

[116]

Molaeimanesh GR, Akbari MH. Role of wettability and water droplet size during water removal from a PEMFC GDL by lattice Boltzmann method. Int. J. Hydrog. Energy. 2016, 41: 14872-14884.

[117]

Sakaida S, Tabe Y, Chikahisa T. Study on gas diffusion layer structure tolerant to flooding in PEFC by scale model experiment and LBM simulation. ECS Trans.. 2017, 80: 123-131.

[118]

Sakaida S, Tabe Y, Chikahisa Tet al. . Study on PEFC gas diffusion layer with designed wettability pattern tolerant to flooding. ECS Trans.. 2018, 86: 111-118.

[119]

Shakerinejad E, Kayhani MH, Nazari Met al. . Increasing the performance of gas diffusion layer by insertion of small hydrophilic layer in proton-exchange membrane fuel cells. Int. J. Hydrog. Energy. 2018, 43: 2410-2428.

[120]

Jinuntuya F, Whiteley M, Chen Ret al. . The effects of gas diffusion layers structure on water transportation using X-ray computed tomography based Lattice Boltzmann method. J. Power. Sources. 2018, 378: 53-65.

[121]

Sadeghi R, Shadloo MS, Hopp-Hirschler Met al. . Three-dimensional lattice Boltzmann simulations of high density ratio two-phase flows in porous media. Comput. Math. Appl.. 2018, 75: 2445-2465.

[122]

Kumbur EC, Sharp KV, Mench MM. A design tool for predicting the capillary transport characteristics of fuel cell diffusion media using an artificial neural network. J. Power. Sources. 2008, 176: 191-199.

[123]

Cawte T, Bazylak A. Accurately predicting transport properties of porous fibrous materials by machine learning methods. Electrochem. Sci. Adv.. 2023, 3. e2100185

[124]

Cawte T, Bazylak A. A 3D convolutional neural network accurately predicts the permeability of gas diffusion layer materials directly from image data. Curr. Opin. Electrochem.. 2022, 35. 101101

[125]

Cawte TM, Bazylak A. Predicting through-plane porosity profiles of fibrous porous media from 2D images with a single-input multi-output convolutional neural network. Meet. Abstr.. 2021, MA2021-1: 966.

[126]

Yu Y, Chen S, Wu YH. Predicting gas diffusion layer flow information in proton exchange membrane fuel cells from cross-sectional data using deep learning methods. Energy. 2023, 282. 128778

[127]

Yu Y, Chen S. Numerical study and prediction of water transfer in gas diffusion layer of proton exchange membrane fuel cells under vibrating conditions. Int. J. Energy Res.. 2022, 4618781-18795.

[128]

Tang KN, Meyer Q, White Ret al. . Deep learning for full-feature X-ray microcomputed tomography segmentation of proton electron membrane fuel cells. Comput. Chem. Eng.. 2022, 161. 107768

[129]

Wang YD, Meyer Q, Tang KNet al. . Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning. Nat. Commun.. 2023, 14: 745.

[130]

Shum AD, Liu CP, Lim WHet al. . Using machine learning algorithms for water segmentation in gas diffusion layers of polymer electrolyte fuel cells. Transp. Porous Medium.. 2022, 144715-737.

[131]

Briceno-Mena LA, Arges CG, Romagnoli JA. Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells. Comput. Chem. Eng.. 2023, 171. 108159

[132]

Wang BW, Zhang GB, Wang HZet al. . Multi-physics-resolved digital twin of proton exchange membrane fuel cells with a data-driven surrogate model. Energy AI. 2020, 1. 100004

[133]

Li HW, Qiao BX, Liu JNet al. . A data-driven framework for performance prediction and parameter optimization of a proton exchange membrane fuel cell. Energy Convers. Manag.. 2022, 271. 116338

[134]

Wang JK, Jiang H, Chen GJet al. . Integration of multi-physics and machine learning-based surrogate modelling approaches for multi-objective optimization of deformed GDL of PEM fuel cells. Energy AI. 2023, 14. 100261

[135]

Wang XL, Zhang HM, Zhang JLet al. . A bi-functional micro-porous layer with composite carbon black for PEM fuel cells. J. Power. Sources. 2006, 162: 474-479.

[136]

Wang XL, Zhang HM, Zhang JLet al. . Micro-porous layer with composite carbon black for PEM fuel cells. Electrochim. Acta. 2006, 51: 4909-4915.

[137]

Lin R, Tang SH, Diao XYet al. . Detailed optimization of multiwall carbon nanotubes doped microporous layer in polymer electrolyte membrane fuel cells for enhanced performance. Appl. Energy. 2020, 274. 115214

[138]

Yu SC, Li XJ, Hao JKet al. . Preparation of microporous layer for proton exchange membrane fuel cell by using polyvinylpyrrolidone aqueous solution. Int. J. Hydrog. Energy. 2014, 39: 15681-15686.

[139]

Lin R, Yu XT, Chen Let al. . Structure majorization on the surface of microporous layer in polymer electrolyte membrane fuel cells to optimize performance and durability. Energy Convers. Manag.. 2021, 243. 114319

[140]

Chen L, Lin R, Chen XDet al. . Microporous layers with different decorative patterns for polymer electrolyte membrane fuel cells. ACS Appl. Mater. Interfaces. 2020, 12: 24048-24058.

[141]

Yan WM, Wu DK, Wang XDet al. . Optimal microporous layer for proton exchange membrane fuel cell. J. Power. Sources. 2010, 195: 5731-5734.

[142]

Chen J, Xu HF, Zhang HMet al. . Facilitating mass transport in gas diffusion layer of PEMFC by fabricating micro-porous layer with dry layer preparation. J. Power. Sources. 2008, 182: 531-539.

[143]

Chun JH, Park KT, Jo DHet al. . Determination of the pore size distribution of micro porous layer in PEMFC using pore forming agents under various drying conditions. Int. J. Hydrog. Energy. 2010, 35: 11148-11153.

[144]

Tseng CJ, Lo SK. Effects of microstructure characteristics of gas diffusion layer and microporous layer on the performance of PEMFC. Energy Convers. Manag.. 2010, 51: 677-684.

[145]

Simon C, Kartouzian D, Müller Det al. . Impact of microporous layer pore properties on liquid water transport in PEM fuel cells: carbon black type and perforation. J. Electrochem. Soc.. 2017, 164: F1697-F1711.

[146]

Simon C, Endres J, Nefzger-Loders Bet al. . Interaction of pore size and hydrophobicity/hydrophilicity for improved oxygen and water transport through microporous layers. J. Electrochem. Soc.. 2019, 166: F1022-F1035.

[147]

Yoshimune W, Kato S, Yamaguchi Set al. . Managing the pore morphologies of microporous layers for polymer electrolyte fuel cells with a solvent-free coating technique. ACS Sustain. Chem. Eng.. 2021, 9: 7922-7929.

[148]

Swamy T, Kumbur EC, Mench MM. Characterization of interfacial structure in PEFCs: water storage and contact resistance model. J. Electrochem. Soc.. 2010, 157: B77.

[149]

Kalidindi AR, Taspinar R, Litster Set al. . A two-phase model for studying the role of microporous layer and catalyst layer interface on polymer electrolyte fuel cell performance. Int. J. Hydrog. Energy. 2013, 389297-9309.

[150]

Schweiss R, Steeb M, Wilde PMet al. . Enhancement of proton exchange membrane fuel cell performance by doping microporous layers of gas diffusion layers with multiwall carbon nanotubes. J. Power. Sources. 2012, 220: 79-83.

[151]

Lee J, Banerjee R, George MGet al. . Multiwall carbon nanotube-based microporous layers for polymer electrolyte membrane fuel cells. J. Electrochem. Soc.. 2017, 164: F1149-F1157.

[152]

Yu JR, Islam MN, Matsuura Tet al. . Improving the performance of a PEMFC with ketjenblack EC-600JD carbon black as the material of the microporous layer. Electrochem. Solid-State Lett.. 2005, 8: A320.

[153]

Park S, Lee JW, Popov BN. Effect of carbon loading in microporous layer on PEM fuel cell performance. J. Power. Sources. 2006, 163357-363.

[154]

Duan QJ, Wang B, Wang Jet al. . Fabrication of a carbon nanofiber sheet as a micro-porous layer for proton exchange membrane fuel cells. J. Power. Sources. 2010, 195: 8189-8193.

[155]

Li CM, Si DC, Liu YDet al. . Water management characteristics of electrospun micro-porous layer in PEMFC under normal temperature and cold start conditions. Int. J. Hydrog. Energy. 2021, 46: 11150-11159.

[156]

Nagai Y, Eller J, Hatanaka Tet al. . Improving water management in fuel cells through microporous layer modifications: fast operando tomographic imaging of liquid water. J. Power. Sources. 2019, 435. 226809

[157]

Athanasaki G, Wang Q, Shi Xet al. . Design and development of gas diffusion layers with pore forming agent for proton exchange membrane fuel cells at various relative humidity conditions. Int. J. Hydrog. Energy. 2021, 46: 6835-6844.

[158]

Tang HL, Wang SL, Pan Met al. . Porosity-graded micro-porous layers for polymer electrolyte membrane fuel cells. J. Power. Sources. 2007, 166: 41-46.

[159]

Mohseninia A, Kartouzian D, Eppler Met al. . Influence of structural modification of micro-porous layer and catalyst layer on performance and water management of PEM fuel cells: hydrophobicity and porosity. Fuel Cells. 2020, 20: 469-476.

[160]

Kattamanchi S, Palakurthi K, Haridoss P. Effect of pore former on carbon black-polytetrafluoroethylene-based monolithic gas diffusion media for proton exchange membrane fuel cells. Energy Technol.. 2020, 8: 2000119.

[161]

Chun JH, Jo DH, Kim SGet al. . Development of a porosity-graded micro porous layer using thermal expandable graphite for proton exchange membrane fuel cells. Renew. Energy. 2013, 5828-33.

[162]

Lin GY, Liu SY, Yu BQet al. . Preparation of graded microporous layers for enhanced water management in fuel cells. J. Appl. Polym. Sci.. 2020, 137: 49564.

[163]

Ahn M, Cho YH, Cho YHet al. . Influence of hydrophilicity in micro-porous layer for polymer electrolyte membrane fuel cells. Electrochim. Acta. 2011, 562450-2457.

[164]

Kitahara T, Nakajima H. Microporous layer-coated gas diffusion layer to reduce oxygen transport resistance in a polymer electrolyte fuel cell under high humidity conditions. Int. J. Hydrog. Energy. 2016, 41: 9547-9555.

[165]

Chen L, Lin R, Yu XTet al. . Microporous layer containing CeO₂-doped 3D graphene foam for proton exchange membrane fuel cells at varying operating conditions. ACS Appl. Mater. Interfaces. 2021, 13: 20201-20212.

[166]

Liu ZC, Zhou L, Gao YYet al. . A novel hydrophilic-modified gas diffusion layer for proton exchange membrane fuel cells operating in low humidification. Int. J. Energy Res.. 2021, 45: 16874-16883.

[167]

Hou SY, Ye YK, Liao SJet al. . Enhanced low-humidity performance in a proton exchange membrane fuel cell by developing a novel hydrophilic gas diffusion layer. Int. J. Hydrog. Energy. 2020, 45: 937-944.

[168]

Nanadegani FS, Lay EN, Sunden B. Effects of an MPL on water and thermal management in a PEMFC. Int. J. Energy Res.. 2019, 43: 274-296.

[169]

Shi X, Jiao DK, Bao ZMet al. . Liquid transport in gas diffusion layer of proton exchange membrane fuel cells: effects of micro-porous layer cracks. Int. J. Hydrog. Energy. 2022, 47: 6247-6258.

[170]

El Hannach M, Singh R, Djilali Net al. . Micro-porous layer stochastic reconstruction and transport parameter determination. J. Power. Sources. 2015, 282: 58-64.

[171]

Nanjundappa A, Alavijeh AS, El Hannach Met al. . A customized framework for 3-D morphological characterization of microporous layers. Electrochim. Acta. 2013, 110: 349-357.

[172]

Sepe M, Satjaritanun P, Zenyuk IVet al. . The impact of micro porous layer on liquid water evolution inside PEMFC using lattice Boltzmann method. J. Electrochem. Soc.. 2021, 168. 074507

[173]

Ostadi H, Rama P, Liu Yet al. . 3D reconstruction of a gas diffusion layer and a microporous layer. J. Membr. Sci.. 2010, 351: 69-74.

[174]

Andisheh-Tadbir M, Kjeang E, Bahrami M. Thermal conductivity of microporous layers: analytical modeling and experimental validation. J. Power. Sources. 2015, 296: 344-351.

[175]

Lan SB, Lin R, Dong MCet al. . Image recognition of cracks and the effect in the microporous layer of proton exchange membrane fuel cells on performance. Energy. 2023, 266. 126340

[176]

Yi BL. Fuel cells: principles, technologies, and applications. 2003, Beijing, Chemical Industry Press

[177]

Tang JY, Su C, Shao ZP. Advanced membrane-based electrode engineering toward efficient and durable water electrolysis and cost-effective seawater electrolysis in membrane electrolyzers. Exploration. 2024, 4: 20220112.

[178]

Yu X, Yuan JL, Sundén B. Review on the properties of nano-/microstructures in the catalyst layer of PEMFC. J. Fuel Cell Sci. Technol.. 2011, 8. 034001

[179]

Zhang SS, Yuan XZ, Hin JNCet al. . A review of platinum-based catalyst layer degradation in proton exchange membrane fuel cells. J. Power. Sources. 2009, 194588-600.

[180]

Sohn YJ, Yim SD, Park GGet al. . PEMFC modeling based on characterization of effective diffusivity in simulated cathode catalyst layer. Int. J. Hydrog. Energy. 2017, 42: 13226-13233.

[181]

Pharoah JG, Peppley B, Atiyeh Het al. . Investigating the role of a microporous layer on the water transport and performance of a PEMFC. ECS Trans.. 2006, 3: 1227-1237.

[182]

Uchida M, Aoyama Y, Eda Net al. . Investigation of the microstructure in the catalyst layer and effects of both perfluorosulfonate ionomer and PTFE-loaded carbon on the catalyst layer of polymer electrolyte fuel cells. J. Electrochem. Soc.. 1995, 142: 4143-4149.

[183]

Du L, Shao YY, Sun JMet al. . Advanced catalyst supports for PEM fuel cell cathodes. Nano Energy. 2016, 29: 314-322.

[184]

Lori O, Elbaz L. Advances in ceramic supports for polymer electrolyte fuel cells. Catalysts. 2015, 5: 1445-1464.

[185]

Perini L, Durante C, Favaro Met al. . Metal-support interaction in platinum and palladium nanoparticles loaded on nitrogen-doped mesoporous carbon for oxygen reduction reaction. ACS Appl. Mater. Interfaces. 2015, 7: 1170-1179.

[186]

Ye SY, Vijh AK, Dao LH. A new fuel cell electrocatalyst based on highly porous carbonized polyacrylonitrile foam with very low platinum loading. J. Electrochem. Soc.. 1996, 143: L7-L9.

[187]

Kusoglu A, Weber AZ. New insights into perfluorinated sulfonic-acid ionomers. Chem. Rev.. 2017, 117: 987-1104.

[188]

Qi ZG, Lefebvre MC, Pickup PG. Electron and proton transport in gas diffusion electrodes containing electronically conductive proton-exchange polymers. J. Electroanal. Chem.. 1998, 459: 9-14.

[189]

Wang KL, Zhou TT, Cao Zet al. . Advanced 3D ordered electrodes for PEMFC applications: from structural features and fabrication methods to the controllable design of catalyst layers. Green Energy Environ.. 2024, 9: 1336-1365.

[190]

Deng RY, Xia ZX, Sun RLet al. . Nanostructured ultrathin catalyst layer with ordered platinum nanotube arrays for polymer electrolyte membrane fuel cells. J. Energy Chem.. 2020, 43: 33-39.

[191]

Chisaka M, Daiguji H. Design of ordered-catalyst layers for polymer electrolyte membrane fuel cell cathodes. Electrochem. Commun.. 2006, 8: 1304-1308.

[192]

Hussain MM, Song D, Liu ZSet al. . Modeling an ordered nanostructured cathode catalyst layer for proton exchange membrane fuel cells. J. Power. Sources. 2011, 196: 4533-4544.

[193]

Ruvinskiy PS, Bonnefont A, Houllé Met al. . Preparation, testing and modeling of three-dimensionally ordered catalytic layers for electrocatalysis of fuel cell reactions. Electrochim. Acta. 2010, 55: 3245-3256.

[194]

Zhang WM, Chen J, Minett AIet al. . Novel ACNT arrays based MEA structure-nano-Pt loaded ACNT/Nafion/ACNT for fuel cell applications. Chem. Commun.. 2010, 46: 4824.

[195]

Zeng YC, Shao ZG, Zhang HJet al. . Nanostructured ultrathin catalyst layer based on open-walled PtCo bimetallic nanotube arrays for proton exchange membrane fuel cells. Nano Energy. 2017, 34344-355.

[196]

Henning S, Ishikawa H, Kühn Let al. . Unsupported Pt-Ni aerogels with enhanced high current performance and durability in fuel cell cathodes. Angew. Chem. Int. Ed.. 2017, 56: 10707-10710.

[197]

Gao JL, Wang HW, Zhang ZHet al. . Semi-ordered catalyst layer with ultra-low Pt loading for proton exchange membrane fuel cells. J. Power. Sources. 2024, 606. 234516

[198]

Zhang HG, Chung HT, Cullen DAet al. . High-performance fuel cell cathodes exclusively containing atomically dispersed iron active sites. Energy Environ. Sci.. 2019, 12: 2548-2558.

[199]

Wang Y, Feng XH. Analysis of reaction rates in the cathode electrode of polymer electrolyte fuel cell. I. Single-layer electrodes. J. Electrochem. Soc.. 2008, 155: B1289.

[200]

Wang Y, Feng XH. Analysis of the reaction rates in the cathode electrode of polymer electrolyte fuel cells. J. Electrochem. Soc.. 2009, 156B403.

[201]

Feng XH, Wang Y. Multi-layer configuration for the cathode electrode of polymer electrolyte fuel cell. Electrochim. Acta. 2010, 554579-4586.

[202]

Taylor AD, Kim EY, Humes VPet al. . Inkjet printing of carbon supported platinum 3-D catalyst layers for use in fuel cells. J. Power. Sources. 2007, 171: 101-106.

[203]

Ye LC, Gao Y, Zhu SYet al. . A Pt content and pore structure gradient distributed catalyst layer to improve the PEMFC performance. Int. J. Hydrog. Energy. 2017, 42: 7241-7245.

[204]

Jain P, Biegler LT, Jhon MS. Optimization of polymer electrolyte fuel cell cathodes. Electrochem. Solid-State Lett.. 2008, 11: B193.

[205]

Xie Z, Navessin T, Shi Ket al. . Functionally graded cathode catalyst layers for polymer electrolyte fuel cells. J. Electrochem. Soc.. 2005, 152A1171.

[206]

Su HN, Liao SJ, Wu YN. Significant improvement in cathode performance for proton exchange membrane fuel cell by a novel double catalyst layer design. J. Power. Sources. 2010, 1953477-3480.

[207]

Qiu YL, Zhang HM, Zhong HXet al. . A novel cathode structure with double catalyst layers and low Pt loading for proton exchange membrane fuel cells. Int. J. Hydrog. Energy. 2013, 38: 5836-5844.

[208]

Liang MC, Liu YM, Xiao BQet al. . An analytical model for the transverse permeability of gas diffusion layer with electrical double layer effects in proton exchange membrane fuel cells. Int. J. Hydrog. Energy. 2018, 43: 17880-17888.

[209]

Liang MC, Fu CG, Xiao BQet al. . A fractal study for the effective electrolyte diffusion through charged porous media. Int. J. Heat Mass Transf.. 2019, 137: 365-371.

[210]

Chen L, Kang QJ, Tao WQ. Pore-scale study of reactive transport processes in catalyst layer agglomerates of proton exchange membrane fuel cells. Electrochim. Acta. 2019, 306: 454-465.

[211]

Feng C, Li Y, Song Ket al. . Structural characteristics, diffusion mechanism and mechanical behaviour of cathode catalyst layer. Comput. Mater. Sci.. 2020, 177. 109572

[212]

Fan LH, Wang Y, Jiao K. Oxygen transport routes in ionomer film on polyhedral platinum nanoparticles. ACS Nano. 2020, 14: 17487-17495.

[213]

Kurihara Y, Mabuchi T, Tokumasu T. Molecular dynamics study of oxygen transport resistance through ionomer thin film on Pt surface. J. Power. Sources. 2019, 414: 263-271.

[214]

Wang WK, Qu ZG, Wang XLet al. . A molecular model of PEMFC catalyst layer: simulation on reactant transport and thermal conduction. Membranes. 2021, 11: 148.

[215]

Kuzmin AE, Kulikova MV, Maximov AL. Mechanism of Fischer-Tropsch synthesis over nanosized catalyst particles: approaches and problems of ab initio calculations. Pet. Chem.. 2019, 59485-497.

[216]

Kim T, Kwon Y, Kwon Set al. . Substrate effect of platinum-decorated carbon on enhanced hydrogen oxidation in PEMFC. ACS Omega. 2020, 5: 26902-26907.

[217]

Dhali S, Karakoti M, Pandey Set al. . Graphene oxide supported Pd-Fe nanohybrid as an efficient electrocatalyst for proton exchange membrane fuel cells. Int. J. Hydrog. Energy. 2020, 45: 18704-18715.

[218]

Li F, Qin Y, Chalgin Aet al. . A non-Pt electronically coupled semiconductor heterojunction for enhanced oxygen reduction electrocatalytic property. ChemistrySelect. 2019, 4: 5264-5268.

[219]

Khajeh-Hosseini-Dalasm N, Ahadian S, Fushinobu Ket al. . Prediction and analysis of the cathode catalyst layer performance of proton exchange membrane fuel cells using artificial neural network and statistical methods. J. Power. Sources. 2011, 196: 3750-3756.

[220]

Lei HH, Xing L, Jiang Het al. . Designing graded fuel cell electrodes for proton exchange membrane (PEM) fuel cells with recurrent neural network (RNN) approaches. Chem. Eng. Sci.. 2023, 267. 118350

[221]

Tai XY, Xing L, Christie SDRet al. . Deep learning design of functionally graded porous electrode of proton exchange membrane fuel cells. Energy. 2023, 283. 128463

[222]

Wang BW, Xie B, Xuan Jet al. . AI-based optimization of PEM fuel cell catalyst layers for maximum power density via data-driven surrogate modeling. Energy Convers. Manag.. 2020, 205. 112460

[223]

Ding R, Yin WJ, Cheng Get al. . Effectively increasing Pt utilization efficiency of the membrane electrode assembly in proton exchange membrane fuel cells through multiparameter optimization guided by machine learning. ACS Appl. Mater. Interfaces. 2022, 14: 8010-8024.

[224]

Ding R, Ding YQ, Zhang HYet al. . Applying machine learning to boost the development of high-performance membrane electrode assembly for proton exchange membrane fuel cells. J. Mater. Chem. A. 2021, 9: 6841-6850.

[225]

Pan MZ, Li C, Pan CJet al. . A novel predicting method on degree of catalytic reaction in fuel cells. Int. J. Energy Res.. 2020, 44: 6860-6872.

[226]

Lou YX, Hao MS, Li YS. Machine-learning-assisted insight into the cathode catalyst layer in proton exchange membrane fuel cells. J. Power. Sources. 2022, 543. 231827

[227]

Jienkulsawad P, Wiranarongkorn K, Chen YSet al. . Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach. Int. J. Hydrog. Energy. 2022, 47: 32303-32314.

[228]

Li GY, Zhu YH, Guo YTet al. . Deep learning to reveal the distribution and diffusion of water molecules in fuel cell catalyst layers. ACS Appl. Mater. Interfaces. 2023, 15: 5099-5108.

[229]

Uenishi T, Imoto R. Optimization of cathode catalyst layer of membrane electrode assembly for polymer electrolyte fuel cells using machine learning. J. Power. Sources. 2023, 573. 233105

[230]

Batchelor TAA, Pedersen JK, Winther SHet al. . High-entropy alloys as a discovery platform for electrocatalysis. Joule. 2019, 3: 834-845.

[231]

Banko L, Krysiak OA, Pedersen JKet al. . Unravelling composition-activity-stability trends in high entropy alloy electrocatalysts by using a data-guided combinatorial synthesis strategy and computational modeling. Adv. Energy Mater.. 2022, 12: 2103312.

[232]

Peng JY, Damewood JK, Karaguesian Jet al. . Navigating multimetallic catalyst space with Bayesian optimization. Joule. 2021, 53069-3071.

[233]

Lu ZL, Chen ZW, Singh CV. Neural network-assisted development of high-entropy alloy catalysts: decoupling ligand and coordination effects. Matter. 2020, 3: 1318-1333.

[234]

Ding R, Chen YW, Chen Pet al. . Machine learning-guided discovery of underlying decisive factors and new mechanisms for the design of nonprecious metal electrocatalysts. ACS Catal.. 2021, 11: 9798-9808.

[235]

Xia W, Hou ZF, Tang Jet al. . Materials informatics-guided superior electrocatalyst: a case of pyrolysis-free single-atom coordinated with N-graphene nanomesh. Nano Energy. 2022, 94. 106868