石墨烯
催化作用
密度泛函理论
氢
材料科学
碳纤维
电催化剂
过渡金属
化学物理
基质(水族馆)
纳米技术
化学
物理化学
计算化学
有机化学
复合数
生物
电极
复合材料
生物化学
电化学
生态学
作者
Victor Fung,Guoxiang Hu,Zili Wu,De‐en Jiang
标识
DOI:10.1021/acs.jpcc.0c04432
摘要
Single-atom catalysts (SACs) are a new research frontier in electrocatalysis such as in the hydrogen evolution reaction (HER). Recent theoretical and experimental studies have demonstrated that certain M–N–C (metal–nitrogen–carbon) based SACs exhibit excellent performance for HER. Here we report a new approach to tune HER activity for SACs by changing the size and dimensionality of the carbon substrate while maintaining the same coordination environment. We screen the 3d, 4d, and 5d transition metal SACs in N-doped 2D graphene and nanographenes of several sizes for HER using first-principles density functional theory (DFT). Nanographenes containing V, Rh, and Ir are predicted to have significantly enhanced HER activity compared to their 2D graphene counterparts. We turn to machine learning to accurately predict the free energy of hydrogen adsorption (ΔGH) based on various descriptors and compressed sensing to identify key descriptors for activity, which can be used to further screen for additional candidates.
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