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表面含氧物种对电催化CO_(2)还原的内在促进机制

Mechanism of surface oxygen-containing species promoted electrocatalytic CO_(2)reduction
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摘要 Oxygen-containing species have been demonstrated to play a key role in facilitating electrocatalytic CO_(2) reduction(CO_(2)RR),particularly in enhancing the selectivity towards multi-carbon(C2+)products.However,the underlying promotion mechanism is still under debate,which greatly limits the rational optimization of the catalytic performance of CO_(2)RR.Herein,taking CO_(2) and O_(2) co-electrolysis over Cu as the prototype,we successfully clarified how O_(2) boosts CO_(2)RR from a new perspective by employing comprehensive theoretical simulations.Our results demonstrated that O_(2) in feed gas can be rapidly reduced into^(*)OH,leading to the partial oxidation of Cu surface under reduction conditions.Surface^(*)OH accelerates the formation of quasi-specifically adsorbed K^(+)due to the electrostatic interaction between^(*)OH and K^(+)ions,which significantly increases the concentration of K^(+)near the Cu surface.These quasi-specifically adsorbed K+ions can not only lower the C-C coupling barriers but also promote the hydrogenation of CO_(2) to improve the CO yield rate,which are responsible for the remarkably enhanced efficiency of C^(2+)products.During the whole process,O_(2) co-electrolysis plays an indispensable role in stabilizing surface^(*)OH.This mechanism can be also adopted to understand the effect of high pH of electrolyte and residual O in oxide-derived Cu(OD-Cu)on the catalytic efficiency towards C^(2+)products.Therefore,our work provides new insights into strategies for improving C^(2+)products on the Cu-based catalysts,i.e.,maintaining partial oxidation of surface under reduction conditions.
作者 付战照 欧阳艺昕 吴明亮 凌崇益 王金兰 Zhanzhao Fu;Yixin Ouyang;Mingliang Wu;Chongyi Ling;Jinlan Wang(Key Laboratory of Quantum Materials and Devices of Ministry of Education,School of Physics,Southeast University,Nanjing 21189,China)
出处 《Science Bulletin》 SCIE EI CAS CSCD 2024年第10期1410-1417,共8页 科学通报(英文版)
基金 supported by the National Key Research and Development Program of China(2022YFA1503100 and 2021YFA1500700) the National Natural Science Foundation of China(22033002,92261112,and 22303011) the Basic Research Program of Jiangsu Province(BK20220800 and BK20222007) the Fundamental Research Funds for the Central Universities(2242023R40016) supported by the Big Data Computing Center of Southeast University and National Supercomputing Center of Tianjin.
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