Machine learning-assisted X-ray absorption analysis of bimetallic catalysts
Abstract
Bimetallic nanoparticles have attracted increasing scientific and technological interest as modules for creating nanoscale materials with unique magnetic, electronic, and chemical properties. The properties of bimetallic NPs are functions of composition, size, shape, stoichiometry, and possibly internal structure (alloy or core-shell-like). Bimetallic nanoparticles have superior properties for catalytic applications. However, it is challenging to understand and control the size, shape, composition, and activity of these nanomaterials. The internal atomic structure of these materials needs to be precisely characterized to understand the structure-function relationship. X-ray absorption fine structure (XAFS) spectroscopy has been a premier tool for analyzing the compositional and structural motifs in bimetallic nanoparticles for several decades. In this review, we discuss the limitations in the ability of XAFS to detect catalytically relevant surface species and focus on recent developments in machine learning-assisted XAFS analysis aimed at overcoming these limitations.
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