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Lu et al. J Mater Inf 2024;4:31 Journal of
DOI: 10.20517/jmi.2024.65
Materials Informatics

Research Article Open Access

N-heterocyclic carbene coordinated single atom

catalysts on C N for enhanced nitrogen reduction
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Wenming Lu , Dian Zheng , Daifei Ye, Jiasheng Peng, Xiaxia Gong, Jing Xu, Wei Liu *
Department of Optical Engineering, College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University,
Hangzhou 311300, Zhejiang, China.
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Authors contributed equally.
* Correspondence to: Prof. Wei Liu, Department of Optical Engineering, College of Optical, Mechanical and Electrical Engineering,
Zhejiang A&F University, No. 666, Wusu Street, Lin'an District, Hangzhou 311300, Zhejiang, China, E-mail: weiliu@zafu.edu.cn
How to cite this article: Lu W, Zheng D, Ye D, Peng J, Gong X, Xu J, Liu W. N-heterocyclic carbene coordinated single atom
catalysts on C N for enhanced nitrogen reduction. J Mater Inf 2024;4:31. https://dx.doi.org/10.20517/jmi.2024.65
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Received: 28 Oct 2024 First Decision: 25 Nov 2024 Revised: 6 Dec 2024 Accepted: 14 Dec 2024 Published: 28 Dec 2024

Academic Editor: Ming Hu Copy Editor: Ping Zhang Production Editor: Ping Zhang

Abstract
Single-atom catalysts (SACs) with N-heterocyclic carbene (NHC) coordination provide an effective strategy for
enhancing nitrogen reduction reaction (NRR) performance by modulating the electronic properties of the metal
active sites. In this work, we designed a novel NHC-coordinated SAC by embedding transition metals (TM) into a
two-dimensional C N-based nanomaterial (TM@C N-NCM) and evaluated the NRR catalytic performance using a
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combination of density functional theory and machine learning. A multi-step screening identified eight
high-performance catalysts (TM = Nb, Fe, Mn, W, V, Ta, Zr, Ti), with Nb@C N-NCM showing the best
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performance (limiting potential = -0.29 V). All catalysts demonstrated lower limiting potential values compared to
their TM@graphene-NCM counterparts, revealing the effectiveness of the C N substrate in enhancing catalytic
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activity. Machine learning analysis achieved high predictive accuracy (coefficient of determination = 0.91; mean
absolute error = 0.19) and identified final step protonation (S ), Mendeleev number (N ), and d-electron count (N )
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d
m
as key factors influencing catalytic performance. This study offers valuable insights into the rational design of
NHC-coordinated SACs and highlights the potential of C N-based nanomaterials for advancing high-performance
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NRR electrocatalysts.
Keywords: Nitrogen reduction reaction, single-atom catalysts, N-heterocyclic carbenes, C N, machine learning
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© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
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