Bringing into play automated electron microscopy data processing for understanding nanoparticulate electrocatalysts’ structure–property relationships
Source abstract
Abstract
Observing and quantifying information at the atomic scale plays an essential role in assessing the structure–property relationships in electrocatalysis. Particularly, when studying supported nanoparticulate fuel cell and electrolyzer electrocatalysts, resolving nanoparticles’ structural features at the atomic scale and their evolution as a response to external stimuli is of great relevance. Atomically resolved electron micrographs of identical locations before and after induced changes are a still vastly unexplored resource of quantifiable data that can be used to elucidate structure–activity and–stability relationships of studied materials. In this short review, we highlight the recent approaches and opportunities in processing electron microscopy images and the development of their analysis algorithms enabling the acquirement of unprecedented structural information, focusing on systems of metallic nanoparticles.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Electron Microscopy Techniques and Applications · Electrochemical Analysis and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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