Multi‐Atom Catalysts: Structural Design, Electronic Modulation, and Synergistic Catalysis

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Authors: Gege Yang, Hairui Cai, Zhimao Yang, Bin Wang, Chao Liang, Shengchun Yang
Cited by: 3
References: 327

Abstract

Multi-Atom catalysts (MACs) have emerged as a pioneering domain in heterogeneous catalysis, distinguished by their exceptional intrinsic characteristics. These catalysts not only preserve the atomic dispersion and superior atomic utilization efficiency characteristic of single-atom catalysts, but also address their inherent limitations through enhanced metal loading capacity. Crucially, the interatomic synergy within the cluster facilitates the formation of more intricate and adaptable active sites, thereby potentially elevating catalytic performance and expanding reaction scope to more complex chemical processes. This comprehensive review systematically examines three fundamental aspects: (1) The architectural diversity of atom cluster catalysts; (2) Advanced strategies for electronic structure modulation, encompassing atomic interface engineering, coordination environment optimization, and substrate-mediated regulation; (3) Synergistic mechanisms that transcend conventional linear scaling relationships and enable precise control over critical catalytic parameters. We further consolidate contemporary synthesis methodologies and cutting-edge characterization techniques specifically tailored for these catalytic systems. Particular emphasis is placed on their transformative applications across electrocatalytic, photocatalytic, and thermocatalytic domains. The work concludes by outlining persistent challenges and future research directions in catalyst design principles, mechanistic elucidation through advanced characterizations, and practical implementation strategies. This systematic analysis provides theoretical guidance and methodological references for developing next-generation high-performance catalysts.

Fields of Study & Subjects

Electrocatalysts for Energy Conversion CO2 Reduction Techniques and Catalysts Machine Learning in Materials Science

Publication details

Journal / source
Small Methods
Publisher
Wiley
Volume
10
Issue
4
Pages
e02368–e02368
Publication date
1 February 2026
Language
EN