Identical-location electron microscopy for the discovery of heterogeneous catalysts' dynamics at the single-particle level.

Journal: Chemical Society reviews
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Abstract

Understanding how heterogeneous catalysts evolve at the single-particle level is central to advancing modern industrial chemistry and energy conversion technologies. However, conventional characterisation techniques provide only ensemble-averaged information, obscuring the structural diversity and dynamic behaviour that govern catalytic performance. In particular, individual nanoparticles can follow distinct evolution pathways under reaction conditions, making it challenging to establish clear structure-property relationships. Identical-location electron microscopy (IL-EM) has emerged as a powerful approach to overcome these limitations by enabling the direct tracking of the same catalyst particle across different stages of a reaction. By preserving spatiotemporal continuity, IL-EM provides particle-resolved insights into single-particle events such as coalescence, dissolution, restructuring, and compositional changes, while avoiding radiolysis damage during the reaction stage. This capability allows reconstruction of catalyst evolution pathways with atomic-scale precision under realistic operating conditions, without the need for specialised EM equipment or software. This review critically examines recent advances in IL-EM methodologies, including experimental workflows, correlative spectroscopy, and applications in thermal and electrochemical catalysis, as well as emerging applications for photocatalysis. Particular emphasis is placed on how IL-EM reveals mechanistic insights that are inaccessible through conventional ensemble-averaging techniques, and workflows correlating IL-EM with other local-probe and macroscale characterisation methods. Finally, we discuss current challenges, including limited time resolution, experimental labour intensity, and statistical considerations, alongside emerging opportunities, such as automation, artificial intelligence (AI)-assisted analysis, and multimodal characterisation. These developments are expected to transform IL-EM into a high-throughput and widely accessible tool. As such, IL-EM is poised to play a key role in establishing quantitative design principles for next-generation heterogeneous catalysts.

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