Our work is the first report on the as-designed electrocatalyst with planar hypercoordinate motifs for CO reduction to C₂ products, which not only widens the potential application of hypercoordinate 2D materials, but also opens up a new and promising avenue for converting abundant industrial CO to value-added ethanol.
More importantly, this catalyst could effectively reduce CO to ethanol through the “carbene” mechanism with a low limiting potential (−0.59 V) and a small barrier for C–C coupling (0.41 eV). Our results reveal that the predicted Cu₂B₂ monolayer exhibits superior thermal and dynamic stability and holds promise for experimental realization. This is notably true for transition metal dichalcogenides (TMDs) where single layers can.
Herein, by means of comprehensive swarm-intelligence structure search and first-principles computations, we identify an ideal electrocatalyst for COR, i.e., a metallic Cu₂B₂ monolayer with planar heptacoordinate copper and planar pentacoordinate boron. Many of the most exquisite properties of two-dimensional (2D) materials only occur in their monolayer form. However, the COR process suffers from the low activity and poor selectivity of the currently employed electrocatalysts, greatly hampering its large-scale application. 9607-9615 ISSN: 2050-7496 Subject: Fischer-Tropsch reaction, boron, carbon monoxide, catalysts, chemical reduction, copper, electrochemistry, energy, ethanol, fuels Abstract: Sustainable production of high-value carbon-based fuels and chemicals through electrochemical CO reduction (COR) under mild conditions is a promising alternative to the traditional Fischer–Tropsch process that requires high energy input and large-scale reactors. A Cu₂B₂ monolayer with planar hypercoordinate motifs: an efficient catalyst for CO electroreduction to ethanol Author: Jingjing Jia, Haijun Zhang, Zhongxu Wang, Jingxiang Zhao, Zhen Zhou Source: Journal of materials chemistry A 2020 v.8 no.19 pp. Mingliang Li, Yaping Zhao, Jia Guo, Xiangqian Qin, Qin Zhang, Chengbo Tian, Peng Xu, Yuqing Li, Wanjia Tian, Xiaojia Zheng, Guichuan Xing, WenHua Zhang.
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