2026-10-14T18:43:16Z https://www.scidb.cn/oai

10.57760/sciencedb.cjcatal.00007 2026-07-16T09:47:33Z

2026-07-16 Grain boundary density effect for highly selective C2+ production from CO2 reduction: Polycrystalline Cu electrocatalyst as a paradigm doi:10.57760/sciencedb.cjcatal.00007 en Grain boundaries (GBs) in Cu-based electrocatalysts have been recognized as efficient sites producing multi-carbon chemicals (C2+) from CO2 reduction, yet the effective GB regulation strategy for targeted activity enhancement is still unavailable to date, and consequently their catalytic mechanism remains unclear. Herein, polycrystalline Cu catalysts (p-Cu) with varying GB densities were fabricated by electrochemical reconstruction of mesoporous Cu2O nanocrystals with different primary particle sizes. It is discovered that increasing GB density results in correspondingly decreased coordination number (CN) of Cu sites, which in turn contributes to the progressive enhancement of C2+ selectivity over the p-Cu catalysts. Specifically, the determined optimal GB density of 151 μm–1, corresponding to a Cu CN of 6.36, delivers a remarkably augmented C2+ Faradaic efficiency of up to 88.06 % (70.55 % for C2H4) and a C2+ partial current density as high as 722.7 mA cm–2, rendering the p-Cu ranked among the best state-of-art catalysts. Mechanism explorations disclose that increased GB density leads to lowered CN Cu sites, which is responsible for the largely amplified *CO coverage together with increased *COatop/*CObridge ratio, and enhanced localized alkaline environment, thereby boosting an energy-efficient *CO-*COH coupling pathway to produce C2+. This work presents a facile regulation strategy of GB density in Cu catalysts, and on this basis establishes the correlation among GB density, the CN of Cu sites, and the selectivity of C2+ products. Electrocatalytic CO2 reduction reaction; Mesoporous nanocrystal; polycrystalline Cu; grain boundary; coordination number; multi-carbon chemicals Yizhu Qiao Xixiong Jin Bohan A Zixuan Wei Min Wang Weiren Chen Xi huang Lingxia Zhang Jianlin Shi PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset Science Data Bank