Evaluation of Automatic Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Data Processing Framework

Computer-aided data acquisition, analysis and interpretation have been employed in various research areas generating useful information. Among these, image processing is most often implemented for post-processing of material characterization data. However, to tackle ambiguity of multi-component materials analysis, spectral data analysis can be used instead. The current study introduces a unique Python-based data processing method for in-depth analysis of energy dispersive spectroscopy (EDS) elemental maps to analyze agglomerate size distribution, average area of each component and their overlap. The framework developed in this study is applied to examine interaction of Cerium (Ce) and Palladium (Pd) particles in membrane electrode assembly (MEA) of Anion-Exchange Membrane Fuel Cell (AEMFC) and investigate if this approach can be utilized to predict the fuel cell's electrochemical behavior. The study concludes that the developed framework is a promising method for automatic data extraction and can be beneficial for use in a variety of clean energy applications.