Activating γ-graphyne nanoribbons as bifunctional electrocatalysts toward oxygen reduction and hydrogen evolution reactions by edge termination and nitrogen doping

被引:25
|
作者
Lv, Yipin [1 ,2 ]
Kang, Baotao [1 ,2 ]
Yuan, Yuan [1 ]
Chen, Guozhu [1 ]
Lee, Jin Yong [2 ]
机构
[1] Univ Jinan, Sch Chem & Chem Engn, Jinan 250022, Shandong, Peoples R China
[2] Sungkyunkwan Univ, Dept Chem, Suwon 16419, South Korea
基金
新加坡国家研究基金会; 中国国家自然科学基金;
关键词
Metal-free electrocatalysis; Density functional theory; Scaling relationship; Machine learning; Feature importance; DENSITY-FUNCTIONAL THEORY; METAL-FREE CATALYST; DOPED GRAPHDIYNE; ACTIVITY ORIGIN; GRAPHENE; DEFECT; DESIGN;
D O I
10.1016/j.cej.2021.133126
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Carbon-based metal free materials (CMFCs) as electrocatalysts have been a hot issue and are receiving growing attention. In this paper, we applied intensive density functional theory (DFT) calculations to study the electrocatalytic activities of gamma-graphyne nanoribbons (gamma GyNRs) toward the oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER). Our results revealed gamma GyNRs to be excellent bifunctional CMFCs after functionalization by termination or N-doping at the edge to introduce proper spin densities. The scaling relationship analysis enhanced our earlier understanding that the binding strength of H atom (Delta EH) is a general descriptor for designing bifunctional electrocatalysts for HER and ORR. Further study revealed that the light gradient boosting machine is a promising machine learning algorithm to accurately predict the electrocatalytic activities of gamma GyNRs. Feature importance analysis suggested that the surrounding environment of the active site plays a more significant role than we previously anticipated. Overall, we suggest promising gamma GyNRs as HER and ORR bifunctional electrocatalysts for the first time and provide deeper insight into the reaction mechanism, which is beneficial for the rational design of novel CMFCs.
引用
收藏
页数:9
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