同核分子
石墨烯
催化作用
密度泛函理论
材料科学
氨
还原(数学)
法拉第效率
产量(工程)
纳米技术
组合化学
计算化学
化学
电化学
物理化学
分子
有机化学
数学
几何学
电极
冶金
作者
Tiantian Zhao,Kai Chen,Xin Xu,Xinyi Li,Xiao Zhao,Qinghai Cai,Ke Chu,Jingxiang Zhao
标识
DOI:10.1016/j.apcatb.2023.123156
摘要
Electrocatalytic reduction of nitrate (NO3−) to ammonia (NRA) is emerging as an attractive strategy to attain valuable NH3 synthesis and harmful NO3− removal, but developing efficient electrocatalysts remains challenging. Herein, taking homonuclear dual metal catalysts (DACs) embedded on N-doped graphene as the example, we proposed a three-step strategy to theoretically evaluate their NRA catalytic performance by means of density functional theory (DFT) computations, which enables us to rapidly identify Cu2@N3−6 as the most promising NRA catalyst with a low limiting potential (−0.14 V). More importantly, such theoretical prediction was further validated by our proof-of-concept experiment: the NH3 yield rate of ∼18.2 mg h−1 cm−2 at − 0.8 V vs RHE and the Faradaic efficiency of 97.4% were achieved. Our studies may offer a feasible avenue to rapidly and precisely achieve efficient NRA catalysts by using theoretical prediction as a guideline for experimentalists to avoid the traditional “trial and error method”.
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