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Updated: Aug 5, 2026

Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction
Published on: April 10, 2018
Single-Atom Supports: Versatile Platforms for Regulating Electrocatalysts at the Atomic Level
Ming Yuan1, Wenjun Guo1, Jiongcan Xiang1
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China.
Abstract:
Atomic-level control of metal-support interfaces is a key lever for electrocatalysis with low energy input, high selectivity, and long-term durability. While single-atom catalysts maximize atom utilization and provide well-defined coordination motifs, their isolated sites can limit cooperativity and compromise stability under the operating conditions. Single-atom support (SAS) overcomes these constraints by embedding atomically dispersed metal centers into the support framework as electronically active modulation units, enabling strong metal-support interactions (SMSI), directional charge migration, and programmable coupling with clusters or nanoparticles. This review summarizes atomic-level regulation strategies for SAS-enabled composite catalysts and extracts structure-function principles across the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), CO2 reduction reaction (CO2RR), and electrocatalytic nitrate reduction reaction (NO3RR). We highlight four regulation modes: coordination engineering (coordination number, heteroatom, and axial ligation), spin-state control, defect engineering (vacancies and edges), and geometric tailoring (confinement and curvature/strain), which jointly tune ligand fields, d-band descriptors, intermediate binding, and interfacial transport. Remaining challenges include predictive design, operando tracking of dynamic active structures, unified descriptor frameworks, and scalable synthesis under industrial conditions. We finally outline a closed-loop roadmap integrating data-driven prediction, stimuli-responsive modulation, multimodal operando characterization, and feedback learning.
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