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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 (CeO x ) 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. 

In this work we investigate the effect of platinum loading and layer thickness on cathode catalyst degradation by a comprehensive in situ and STEM-EDS characterization. To decouple the effect of the platinum loading and layer thickness from each other, the experiments were categorized in two sets, each with cathode loadings varying between 0.1 and 0.4 mg_Ptcm⁻²: (i) Samples with a constant Pt/C ratio and thus varying layer thickness, and (ii) samples with varying Pt/C ratios, achieved by dilution with bare carbon, to maintain a constant layer thickness at different platinum loadings. Every MEA was subjected to an accelerated stress test, where the cell was operated for 45,000 cycles between 0.6 and 0.95 V. Regardless of the Pt/C ratio, a higher relative loss in electrochemically active surface area was measured for lower Pt loadings. STEM-EDS measurements showed that Pt was mainly lost close to the cathode—membrane interface by the concentration driven Pt²⁺ion flux into the membrane. The size of this Pt-depletion zone has shown to be independent on the overall Pt loading and layer thickness, hence causing higher relative Pt loss in low thickness electrodes, as the depletion zone accounts for a larger fraction of the catalyst layer. 

The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often ad hoc , indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. A dataset of 40 high-resolution TEM images at different magnification levels, from 10 to 100 nm scales, was annotated manually. This dataset was used to train the U-Net model, with the StarDist formulation for the loss function, for the nanoparticle segmentation task. StarDist reached a precision of 86%, recall of 85%, and an F1-score of 85% by training on datasets as small as thirty images. The segmentation maps outperform models reported in the literature for a similar problem, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost.