Investigating The Effect of Manufacturing Processes on The Lonomer Network in PEMWE and PEMFC Catalyst Layers using Microscopy and Python-Based Data Processing

With the increasing demand for hydrogen production as a source of clean energy, proton exchange membrane water Eelectrolyzers (PEMWEs) and proton exchange membrane fuel cells (PEMFCs) are crucial components of the clean energy field, offering sustainable alternatives for energy conversion and storage. With the need to significantly increase the production of these devices, scale up manufacturing of their catalyst layers using different methods, such as slot die, gravure, screen printing or rod coating, is becoming a focus of industry and researchers. The aim is to speed up the fabrication, but also enhance the efficiency, durability, and cost-effectiveness of these systems [2].<br/>Quality assessment in terms of electrochemical performance, but also in terms of visualization and quantification of the catalyst layer parameters are essential. Advanced characterization techniques, including microscopy and spectroscopy, are powerful tools to elucidate microstructure, morphology and spatial distribution of elements in these layers [3]. Of special interest is the ionomer network within the catalyst layers, influencing proton conductivity and electrochemical reactions, and playing a vital role in the efficiency and performance of both PEMWEs and PEMFCs [1]. Elucidating and quantifying its distribution is a challenging task.<br/>This work presents our recently developed framework to visualize and quantify ionomer distribution, together with other structural and compositional parameters, for the catalyst layers fabricated using different manufacturing processes. The framework utilizes advanced image processing algorithms to process acquired STEM EDS maps from inks and electrodes [4]. It aims to automatically identify phases, particles, and elemental distributions within the microstructure, to see the ionomer connectivity in inks and their corresponding electrodes via different manufacturing processes such as slot die with different flow rates and gravure with different rod sizes, as well as different mixing times in ink preparations [5]. Moreover, in order to establish correlations between inks and electrodes for different materials systems and microstructural arrangements, the developed data processing protocols are applied in both fuel cells and electrolyzers.<br/><br/><br/><br/><br/><br/><br/><br/><b>References</b><br/>[1] Y. V. Yakovlev <i>et al.</i>, “Ionomer content effect on charge and gas transport in the cathode catalyst layer of proton-exchange membrane fuel cells,” <i>J. Power Sources</i>, vol. 490, p. 229531, Apr. 2021, doi: 10.1016/J.JPOWSOUR.2021.229531.<br/>[2] X. Lyu <i>et al.</i>, “Aging gracefully? Investigating iridium oxide ink’s impact on microstructure, catalyst/ionomer interface, and PEMWE performance,” <i>J. Power Sources</i>, vol. 581, p. 233503, Oct. 2023, doi: 10.1016/J.JPOWSOUR.2023.233503.<br/>[3] A. Peyman Soleymani, M. Reid, J. Jankovic, A. P. Soleymani, J. Jankovic, and M. Reid, “An Epoxy-Free Sample Preparation Approach to Enable Imaging of Ionomer and Carbon in Polymer Electrolyte Membrane Fuel Cells,” <i>Adv. Funct. Mater.</i>, vol. 33, no. 6, p. 2209733, Feb. 2023, doi: 10.1002/ADFM.202209733.<br/>[4] M. Batool, A. O. Godoy, M. Birnbach, D. R. Dekel, and J. Jankovic, “Evaluation of Automatic Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Data Processing Framework,” <i>ECS Trans.</i>, vol. 104, no. 8, pp. 137–153, Oct. 2021, doi: 10.1149/10408.0137ecst.<br/>[5] C. M. Baez-Cotto <i>et al.</i>, “The effect of ink ball milling time on interparticle interactions and ink microstructure and their influence on crack formation in rod-coated catalyst layers,” <i>J. Power Sources</i>, vol. 583, p. 233567, Nov. 2023, doi: 10.1016/J.JPOWSOUR.2023.233567.