A multi-scale analysis method for catalyst layers in PEMFC catalyst coated membranes: Correlation of catalyst layer microstructure with dispersion recipes and hot-press pressure
DOI:
https://doi.org/10.31224/4228Keywords:
proton exchange membrane fuel cell, microstructure, catalyst layerAbstract
This study introduces a comprehensive multi-scale framework for analyzing and optimizing the microstructural properties of catalyst-coated membranes (CCMs) in polymer electrolyte membrane fuel cells (PEMFCs). The approach integrates mercury intrusion porosimetry (MIP), multiple microscopy techniques, and non-destructive focused ion beam scanning electron microscopy (FIB-SEM), enabling detailed characterization across microstructural scales, from tens of nanometers to hundreds of micrometers. A custom MATLAB-based image-processing algorithm was developed to extract pore attributes—including size, porosity, and geometry—from FIB-SEM cross-sections, providing unprecedented insights into microstructural variations under varying fabrication conditions. By employing this multi-scale analysis, the study revealed significant correlations between process parameters (such as hot-pressing) and dispersion formulations with the resulting microstructural properties and electrochemical performance. The study revealed previously hidden dependencies, demonstrating that catalyst layers produced from different dispersions exhibit distinct responses to hot-pressing and further elucidating the critical interplay between pore structure, internal resistance, and adhesion in achieving CCMs with optimal performance. This novel approach provides a scalable, cost-effective tool for probing CCM structure-property-performance correlations. By enabling the characterization of both surface and internal features within catalyst layers, our method lays the foundation for rational, data-driven design of CCMs and other electrochemical systems, such as proton exchange membrane electrolyzers.
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Copyright (c) 2024 Ahammed Suhail Odungat, Lars Grebener, Oliver Pasdag, Thai Binh Nguyen, Yawen Zhu, Sebastian Kohsakowski, Ivan Radev, Fatih Özcan, Doris Segets

This work is licensed under a Creative Commons Attribution 4.0 International License.