Chen Siyu, Gao Yongqi, Wang Wugang, Prezhdo Oleg V, Xu Lai
Institute of Functional Nano & Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, 215123Jiangsu, P.R. China.
Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, 215123Jiangsu, P.R. China.
ACS Nano. 2023 Jan 6. doi: 10.1021/acsnano.2c10607.
In the electrocatalytic nitrogen reduction reaction (NRR), nitrogen (N) is chemically inert, it is difficult to break the triple bond, and the subsequent protonation step is very challenging. Suitable catalysts with high selectivity and high activity are needed to promote the electrocatalytic NRR. We screen a large number of clusters composed of three metal atoms embedded into a two-dimensional metal nitride, WN, with a N vacancy, and calculate the reaction energetics. The VNiCu cluster has the best catalytic activity among all the catalysts proposed so far. The Fe and FeCo clusters are excellent catalysts as well. In all cases, spin polarization is needed to observe the catalytic effect. We establish the optimal NRR path and confirm that it remains unchanged in the presence of a solvent. We find three groups of descriptors that can well predict the materials' properties and exhibit linear relationships with the NRR limiting potential.
在电催化氮还原反应(NRR)中,氮气(N)化学性质不活泼,难以打破三键,且后续的质子化步骤极具挑战性。需要合适的具有高选择性和高活性的催化剂来促进电催化NRR。我们筛选了大量由嵌入二维金属氮化物WN且带有一个N空位的三个金属原子组成的团簇,并计算了反应能量学。VNiCu团簇在迄今为止提出的所有催化剂中具有最佳的催化活性。Fe和FeCo团簇也是优异的催化剂。在所有情况下,都需要自旋极化来观察催化效果。我们建立了最优的NRR路径,并证实其在有溶剂存在时保持不变。我们发现三组描述符能够很好地预测材料的性质,并与NRR极限电位呈现线性关系。