Leveraging Image and Data Processing Techniques to Investigate Ionomer Distribution within Catalyst Layers of PEMFCs and PEMWEs

Advanced materials characterization tools such as scanning transmission electron microscopy and energy dispersive spectroscopy (STEM/EDS) offer a surfeit of information, only a fraction of which is comprehensible and evident to the human eye. This is especially true for complex, multi-component electrochemical systems such as polymer electrolyte membrane fuel cells (PEMFC) and water electrolyzers (PEMWE) where understanding component interactions within membrane electrode assembly (MEA) is a challenging task [1]. This understanding is crucial to optimize the electrodes in MEAs and achieve a high performance and durability of these devices. Therefore, to aid deeper analysis ranging from better visualization to quantification of such microstructural features, the utilization of advanced data science algorithms and image processing tools and techniques, in conjunction with advanced imaging and spectroscopy techniques, become categorically essential [2].<br/><br/>In this study, an in-house developed framework built on Python is utilized to study ionomer distribution and its interaction with the catalyst within the catalyst layer for PEMFCs and PEMWEs synthesized/fabricated via different manufacturing processes. Ionomer-catalyst interaction affects the protonic conduction, mass transport, and catalyst utilization but is often difficult to visualize and quantify without optimized imaging and specialized data processing which is made possible via the developed framework [3]. Sensitivity analysis was carried out to optimize the framework’s performance to accurately reflect the changes in microstructure due to variations in synthesis/fabrication parameters. Not only did the framework help explore ionomer connectivity but also measured the degree of ionomer-catalyst coverage through rapid analysis requiring minimal user interaction. Furthermore, the applicability and implications of using the developed framework to form structure-performance correlations were evaluated yielding valuable insights about ionomer behavior in inks and electrodes. The introduced framework exhibits great potential which allows it to extend its utility to other clean energy applications with minimal adjustments.<br/><br/><b>References</b><br/>[1] Y. Guo <i>et al.</i>, “The Controllable Design of Catalyst Inks to Enhance PEMFC Performance: A Review,” <i>Electrochem. Energy Rev.</i>, vol. 4, no. 1, pp. 67–100, 2021, doi: 10.1007/s41918-020-00083-2.<br/>[2] J. I. Goldstein, “Quantitative microanalysis with high spatial resolution,” <i>Met. Soc.</i>, vol. 5, 1981.<br/>[3] A. Suzuki <i>et al.</i>, “Ionomer content in the catalyst layer of polymer electrolyte membrane fuel cell (PEMFC): Effects on diffusion and performance,” <i>Int. J. Hydrogen Energy</i>, vol. 36, no. 3, pp. 2221–2229, 2011, doi: https://doi.org/10.1016/j.ijhydene.2010.11.076.