空位缺陷
催化作用
对偶(语法数字)
氮气
还原(数学)
材料科学
双重角色
化学
组合化学
结晶学
有机化学
数学
艺术
几何学
文学类
作者
Cuiping Shao,Wenjie Wang,Yuwen Cheng
标识
DOI:10.1016/j.apsusc.2024.160295
摘要
Electrocatalytic nitrogen reduction reaction (NRR) is considered as a promising method for ammonia synthesis under ambient conditions. However, its practical applications have been hindered due to the high activation energy of inert N≡N triple bond and the competitive hydrogen evolution reaction (HER). Herein, employing density-functional theory (DFT) method, the effect of different vacancies on V2CO2 (VO, VM, VM+O, and 2VO-V2CO2) and dual-metals anchored on defective V2CO2 (TM1TM2@V2CO2-x, TM = Ti, V, Cr, Mn, Fe, Co, Ni, and Mo) stability, NRR activity and selectivity are investigated. Results show that 2Vo-V2CO2 delivers the highest NRR activity via enzymatic pathway with the corresponding UL = −0.25 V among the four vacancy systems. In addition, six electrocatalysts exhibit excellent performance for NRR and inhibition of competitive HER among the studied DACs materials, with limiting potentials (UL) ranging from −0.13 to −0.41 V, in which FeNi@V2CO2-x (UL = −0.13 V) via distal pathway and TiTi@V2CO2-x (UL = −0.41 V) via enzymatic pathway show particularly low UL among heteronuclear and homonuclear catalysts, respectively. The outstanding catalytic activity is attributed to the "pull-pull effect" of transition-metal dimer, which plays a significant role in regulating the binding strength between the substrate and intermediate *NNH. The charge density difference and density of states results indicate that the vacancies and dual-metals can efficiently modulate electronic properties of MXenes and reduce the activation energy of N2. 2Vo-V2CO2 and FeNi@V2CO2-x are predicted thermodynamically stable at reaction temperature by the AIMD simulations. This study indicates that dual-metals anchored on defective V2CO2 not only enhance its original NRR activity, but also provide a novel strategy for the future design of NRR electrocatalysts.
科研通智能强力驱动
Strongly Powered by AbleSci AI