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Single-Atom Metal-Based Electrocatalysts (Fe, Ni, and Co) for CO2-to-CO Conversion: A Comprehensive Review

材料科学 金属 Atom(片上系统) 一氧化碳中毒 纳米技术 无机化学 催化作用 冶金 化学工程 化学 计算机科学 工程类 有机化学 嵌入式系统
作者
Shayan Gul,Fatima Nasim,Muhammad Arif Nadeem
出处
期刊:ACS applied energy materials [American Chemical Society]
卷期号:8 (16): 11786-11812 被引量:14
标识
DOI:10.1021/acsaem.5c01419
摘要

The electrocatalytic carbon dioxide reduction reaction (eCO 2 RR) presents a promising route for sustainable carbon conversion, enabling the production of valuable chemical feedstocks while mitigating CO 2 emissions. Among various electrocatalysts, single-atom catalysts (SACs) have emerged as highly efficient candidates for CO 2 -to-CO conversion due to their well-defined active sites, high atom utilization, and remarkable selectivity. However, the practical implementation of the eCO 2 RR remains hindered by several challenges, including low selectivity, insufficient current densities, and the high cost of catalytic materials. The reaction kinetics are primarily limited by the proton-coupled electron transfer (PCET) process, which involves multiple electron–proton transfers (ranging from 2 to 12 PCET steps) to generate a variety of valuable products. Notably, the two-electron proton transfer pathway yielding CO is of particular interest for syngas production, a key intermediate in industrial chemical synthesis. In this perspective, we provide a comprehensive overview of SACs for eCO 2 RR to CO, covering their synthesis methods, advanced characterization techniques, electrochemical setups, and catalytic performance. By systematically compiling and analyzing reported SACs for CO 2 -to-CO conversion, this study aims to offer valuable insights into the structure–activity relationships governing their catalytic behavior, thereby guiding future research toward the development of highly efficient and scalable electrocatalytic systems. Furthermore, we provide forward-looking perspectives on emerging strategies, including dynamic coordination environments, dual-atom synergy, machine-learning-guided catalyst design, and in situ/operando techniques to accelerate innovation in CO 2 electroreduction.
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