Use of digital image processing of microscopic images and multivariate analysis for quantitative correlation of morphology, activity and durability of electrocatalysts

Building structure-to-property relationships is one of the most often attempted research tasks in today's material chemistry. In this report, we present a universal methodology for building structure-to-property relationship models based on statistical correlations between image parameters extracted from microscopic images and the property of interest. The methodology presented consists of conversion of SEM images into useful quantitative morphological descriptors, such as roughness and texture, by digital image processing, separating images into high-and low-frequency components reflecting roughness in meso- and macro-regimes, and applying principal component analysis (PCA) to correlate morphological parameters with other performance characteristics used to evaluate electrocatalysts' catalytic activity and durability.