还原(数学)
铜
氧化还原
化学
无机化学
有机化学
数学
几何学
作者
Yao Yang,Julian Feijóo,Marc Figueras,Chubai Chen,Chuqiao Shi,Maria Fonseca Guzman,Yves Murhabazi Maombi,Shikai Liu,Pulkit Jain,Valentín Briega‐Martos,Zhengxing Peng,Yu Shan,Geonhui Lee,Michael Rebarchik,Lang Xu,Christopher J. Pollock,Jianbo Jin,Nathan E. Soland,Cheng Wang,Miquel Salmerón
出处
期刊:Nature Catalysis
[Nature Portfolio]
日期:2025-06-25
卷期号:8 (6): 579-594
被引量:16
标识
DOI:10.1038/s41929-025-01359-w
摘要
Single crystals and shape-controlled nanocrystals are well known to exhibit facet-dependent catalytic properties. However, few studies have investigated how those nanocrystals evolve and (de)activate during reactions, calling for the development of nanoscale time-resolved operando methods. In this context, we have designed Cu nanocubes as a model system to elucidate the underlying driving force of dynamic nanocatalyst reconstruction during the CO2 reduction reaction (CO2RR). Operando electrochemical liquid-cell scanning transmission electron microscopy (EC-STEM) and synchrotron-based X-ray spectroscopy reveal the size- and potential-dependent complete transformation from (100)-oriented Cu@Cu2O nanocubes to polycrystalline metallic Cu nanograins under CO2RR conditions. In addition, machine learning-assisted operando four-dimensional STEM reveals that large Cu nanograins derived from nanocubes form mainly crystalline domains, while their smaller counterparts are more amorphous due to faster evolution kinetics. In situ Raman spectroscopy and density functional theory calculations suggest that CO drives the ejection of single Cu atoms, resulting in few-nanometre Cu clusters and the surface migration of highly mobile copper carbonyl (Cu–CO) species. Combined, these multimodal operando methods and theoretical approaches pave the way for understanding the complex structural evolution of energy-related nanocatalysts under electrochemical conditions. (Figure presented.)
科研通智能强力驱动
Strongly Powered by AbleSci AI