Machine Learning-Guided Gradient Dual-Proton Conducting Catalytic Layers for High Temperature Proton Exchange Membrane Fuel Cells in Aviation

Xin Wang; Jian Yao; Zhenguo Zhang; Jialin Zhang; Baohua Liu; Wen Liu; Wen Li; Shanfu Lu; Yan Xiang; Haining Wang; San Ping Jiang; Jin Zhang

doi:10.1016/j.eng.2025.10.031

Engineering ›› :202510031 DOI: 10.1016/j.eng.2025.10.031

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Machine Learning-Guided Gradient Dual-Proton Conducting Catalytic Layers for High Temperature Proton Exchange Membrane Fuel Cells in Aviation

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Abstract

State-of-the-art proton exchange membrane fuel cell (PEMFC) for aviation application requires large radiator to avoid overheating. High temperature PEMFCs (HT-PEMFCs) using phosphoric acid (PA) as a proton conductor enable operating over 160 °C and anhydrous conditions, solving the heat rejection problems. Nevertheless, the dynamic PA redistribution in catalytic layers severely compromises their electrochemical performance. This study, using machine learning, clarifies that the PA volume concentration within the catalytic layers is the dominant performance factor, contributing 25.5% of fuel cell performance. Furthermore, multiphysics simulations for the catalytic layers reveal a detrimental PA gradient formation from membrane to the gas diffusion layer, causing progress deactivation of Pt catalyst by 50% in membrane-distal electrode regions. That has also been confirmed by electrochemical performance of gradient electrode layers with a 111.5% loss of Pt utilization in PA-deficient zones. To address this critical challenge, we propose an innovative dual proton conductor system combining ethylenediamine tetramethylene with PA (EDTMPA). The strong hydrogen bonding interactions between the phosphonic groups of EDTMPA and PA molecules create continuous proton conduction pathways and limit PA migration. This modification achieves a record peak power density of 2.16 W cm⁻² at 160 °C, significantly advancing HT-PEMFCs for aviation application.

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Machine learning / Multiphysics simulation / Dual proton conductors / Gradient catalytic layer / HT-PEMFC

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Xin Wang, Jian Yao, Zhenguo Zhang, Jialin Zhang, Baohua Liu, Wen Liu, Wen Li, Shanfu Lu, Yan Xiang, Haining Wang, San Ping Jiang, Jin Zhang. Machine Learning-Guided Gradient Dual-Proton Conducting Catalytic Layers for High Temperature Proton Exchange Membrane Fuel Cells in Aviation. Engineering 202510031 DOI:10.1016/j.eng.2025.10.031

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