Hexagonal MBenes-Supported Single Atom as Electrocatalysts for the Nitrogen Reduction Reaction  被引量:1

在线阅读下载全文

作  者:Ya Gao Erpeng Wang Yazhuo Zheng Jian Zhou Zhimei Sun 

机构地区:[1]School of Materials Science and Engineering,Beihang University,Beijing 100191,China

出  处:《Energy Material Advances》2023年第1期331-343,共13页能源材料前沿(英文)

基  金:National Key Research and Development Program of China(2022YFB3807200).

摘  要:The electrocatalytic nitrogen reduction reaction(NRR)is currently constrained by sluggish reaction kinetics and poor selectivity because of the difficulties in activating inert N≡N triple bonds and the existence of competing hydrogen evolution reaction(HER).Therefore,electrocatalysts with high activity,selectivity,and stability are highly desired.Herein,by means of first-principles calculations,we investigated the electrocatalytic NRR performance of a series of transition metal atoms(e.g.,3d,4d,and 5d)embedded in defective hexagonal MBene nanosheets[h-Zr(Hf)_(2)B_(2)O_(2)]and identified that h-Zr(Hf)_(2)B_(2)O_(2) could be an excellent platform for electrocatalytic NRR.On the basis of our proposed screening criteria,16 candidates are efficiently selected out from 50 systems,among which,Zr_(2)B_(2)O_(2)-Cr stands out with high selectivity to NRR against HER and the ultralow limiting potential(−0.10 V).The value is much lower than that of the well-established stepped Ru(0001)surface(−0.43 V).The origin of the high activity toward NRR is attributed to the synergistic effect of the single atom(SA)and the M atoms in the substrate.More impressively,a composition descriptor is further proposed on the basis of the inherent characteristics of the catalysts[number of valence electrons of SA and electronegativity of the SA and Zr(Hf)atoms],which helps to better predict the catalytic performance.Our work not only contributes to the development of highly efficient NRR electrocatalysts but also extend the application of h-MBenes in electrocatalysis.

关 键 词:SELECTIVITY BONDS kinetics 

分 类 号:O643.36[理学—物理化学]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象