Evaluation of Semi-Automatic Compositional and Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Image and Data Processing Framework for Fuel Cell Applications

Computer-aided data acquisition, analysis, and interpretation are rapidly gaining traction in numerous facets of research. One of the subsets of this field, image processing, is most often implemented for post-processing material microstructural characterization data to understand better and predict materials' features, properties, and behaviors at multiple scales. However, to tackle the ambiguity of multi-component materials analysis, spectral data can be used in combination with image processing. The current study introduces a novel Python-based image and data processing method for in-depth analysis of energy dispersive spectroscopy (EDS) elemental maps to analyze multi-component agglomerate size distribution, the average area of each component, and their overlap. The framework developed in this study is applied to examine the interaction of Cerium Oxide (CeOx) and Palladium (Pd) particles in the membrane electrode assembly (MEA) of an Anion-Exchange Membrane Fuel Cell (AEMFC) and to investigate if this approach can be correlated to cell performance. The study also performs a sensitivity analysis of several parameters and their effect on the computed results. The developed framework is a promising method for semi-automatic data processing and can be further advanced towards a fully automatic analysis of similar data types in the field of clean energy materials and broader.