Gao Qiang, Pillai Hemanth Somarajan, Huang Yang, Liu Shikai, Mu Qingmin, Han Xue, Yan Zihao, Zhou Hua, He Qian, Xin Hongliang, Zhu Huiyuan
Department of Chemical Engineering, Virginia Polytechnic Institute and State University, 635 Prices Fork Rd., Blacksburg, VA, 24061, USA.
Department of Materials Science and Engineering, National University of Singapore, 9 Engineering Drive 1, 117575, Singapore, Singapore.
Nat Commun. 2022 Apr 29;13(1):2338. doi: 10.1038/s41467-022-29926-w.
The electrochemical nitrate reduction reaction (NORR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NORR to ammonia with a Faradaic efficiency of 92.5% at -0.5 V and a yield rate of 6.25 mol h g at -0.6 V. This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.
电化学硝酸盐还原反应（NORR）生成氨是恢复全球氮循环紊乱的关键一步。在寻找高效电催化剂的过程中，通过在随机合金中利用配体和应变效应来定制催化位点是一种常见方法，但由于普遍存在的能量标度关系，其效果仍然有限。借助可解释的机器学习，我们揭示了一种通过金属d态与吸附质前沿轨道的位点特异性泡利排斥相互作用来打破吸附能标度关系的机制。这种非标度行为可以在有序B2金属间化合物的（100）型位点上实现，其中空心N与次表面金属原子之间的轨道重叠显著，而桥式双齿NO不受直接影响。在预测的那些金属间化合物中，我们合成了单分散的有序B2 CuPd纳米立方体，其在NORR生成氨反应中表现出高性能，在-0.5 V时法拉第效率为92.5%，在-0.6 V时产率为6.25 mol h g 。这项研究除了提供d带中心指标外，还给出了机器学习设计规则，为超越线性标度限制的数据驱动催化材料发现铺平了道路。
