Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights.
Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights.
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DOI：
10.1038/s41467-022-29926-w
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发表时间：
2022-04-29
影响因子：
16.6
通讯作者：
Zhu, Huiyuan
中科院分区：
文献类型：
--
作者：
Gao, Qiang;Pillai, Hemanth Somarajan;Huang, Yang;Liu, Shikai;Mu, Qingmin;Han, Xue;Yan, Zihao;Zhou, Hua;He, Qian;Xin, Hongliang;Zhu, Huiyuan
电化学硝酸盐还原反应（NO3 RR）是恢复全球中断的氮循环的重要步骤。在寻找高效的电催化剂，剪裁催化位点的配体和应变效应的随机合金是一种常见的方法，但仍然有限，由于无处不在的能量标度关系。通过可解释的机器学习，我们通过金属d态与吸附物前线轨道的特定位点的泡利排斥相互作用，揭示了打破吸附能量标度关系的机制。在B2金属间化合物的（100）型晶位上可以实现非标度行为，其中空心 *N和次表面金属原子之间的轨道重叠是显著的，而桥双齿 *NO3不受直接影响。在预测的金属间化合物中，我们合成了单分散有序的B2 CuPd纳米立方体，其表现出NO3 RR到氨的高性能，在-0.5 VRHE下的法拉第效率为92.5%，在-0.6 VRHE下的产率为6.25 mol h-1 g-1。除了d波段中心度量之外，这项研究还提供了机器学习的设计规则，为超越线性缩放限制的催化材料的数据驱动发现铺平了道路。机器学习是筛选电催化材料的有力工具。在这里，作者报告了机器学习的物理见解与结构有序的金属间纳米晶体和明确定义的催化位点的控制合成的无缝集成，用于有效地将硝酸盐还原为氨。
The electrochemical nitrate reduction reaction (NO3RR) 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 *NO3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO3RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 VRHE and a yield rate of 6.25 mol h−1 g−1 at −0.6 VRHE. 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. Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical insights with the controlled synthesis of structurally ordered intermetallic nanocrystals and well-defined catalytic sites for efficient nitrate reduction to ammonia.
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