Adverse climatic conditions caused by greenhouse gas (GHG) emissions from non-renewables have led to increased focus on utilization of clean alternative fuels such as hydrogen (H₂). Clean H₂ can be obtained via electrochemical reaction of water splitting through proton exchange membrane water electrolyzers (PEMWEs). However, challenges to its widespread commercialization lie with the high H₂ production cost, a large portion of which comes from use of expensive anodic catalyst materials. To improve anodic catalyst utilization to attain better performance at reduced cost, a thorough understanding of anodic catalyst layer microstructure is needed. In-depth characterization through scanning transmission electron microscopy with energy dispersive spectroscopy (STEM/EDS) can provide a comprehensive dataset, which can be processed further to extract structural parameters and correlate to electrochemical performance. However, extracting relevant information from these datasets can be very challenging and time-consuming.

In this study, an automatic python-based image processing framework is implemented to quantify compositional and microstructural parameters for different IrO₂ based catalyst inks and their corresponding catalyst layers. The code can provide areal density of catalyst materials, pore/agglomerate size distribution, connectivity of phases, coverage of catalyst particles by ionomer, and its content percentage and distribution. Quantified parameters are then correlated with the electrochemical performance of the PEMWEs. The results provide adequate pointers to not only help capturing microstructural details of catalyst layers but also provide a basis to predict electrochemical behavior and performance of the inks and catalyst layers being studied.