锐钛矿
过渡金属
还原(数学)
电催化剂
兴奋剂
材料科学
氮气
金属
无机化学
纳米技术
化学
电极
催化作用
物理化学
电化学
光催化
光电子学
冶金
有机化学
数学
几何学
作者
Yongfei Ji,Paiyong Liu,Yungan Huang
摘要
The electrocatalytic nitrogen reduction reaction (eNRR) has been attracting intensive scientific attention as a potential alternative to the industrial Haber-Bosch process for ammonia production. Although many materials have been investigated, optimal catalysts for the reaction remain to be found. In this work, we performed the theoretical screening of 3d-5d transition metal doped anatase TiO2 for the eNRR. The most favorable doping site of each transition metal on the (101) surface was located. We found that the doping of transition metals promotes the formation of oxygen vacancies which are beneficial for the reaction. The scaling relations between the energies of the key intermediates were investigated. Using a machine learning algorithm (SVM), we identified two adsorption modes for the end-on adsorbed *HNN, which exhibited different scaling relations with *NH2. From a two-step process, we screened out several candidates, among which Au and Ta were proposed to be the most efficient dopants. Electronic structure analysis reveals that they can efficiently lower the energy of the intermediates. These results should be helpful for the design of more efficient TiO2-based catalysts for the eNRR.
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