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Updated: Oct 3, 2026

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode
Published on: June 4, 2021
Computational screening of M-N-C single-atom sensors for multicomponent VOC detection
Kai Ma1,2, Mengni Zhang1,2, Di Zheng3
1Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, Hebei, China. yangwj@ncepu.edu.cn.
Abstract:
Volatile organic compounds (VOCs) pose serious risks to environmental safety and human health, yet their multicomponent detection remains limited by insufficient sensitivity, selectivity, and reusability. In this work, first-principles calculations were used to screen graphene-supported transition-metal single-atom M1-NX-C materials for detecting five representative VOCs, including CB, T, o-DCB, m-DCB, and p-DCB. Forty candidates were constructed by combining ten transition-metal centres with four nitrogen coordination environments. A multidimensional screening framework was established by integrating adsorption energy, Bader charge transfer, band gap variation, TranSIESTA transport response, and recovery time. The results show that nitrogen coordination generally enhances VOC adsorption, while the metal centre and local coordination environment jointly regulate adsorption stability, charge transfer, electronic perturbation, and desorption behaviour. Cu1-N3-C exhibits detectable responses toward all five VOCs and shows strong potential as a universal sensing material, whereas Ni1-N3-C responds to four VOCs. Co1-N2-C and Mn1-N1-C achieve faster recovery with clear electrical responses, making them suitable for repeated sensing. This work establishes a structure-property-guided screening strategy for M-N-C single-atom sensors and provides a theoretical foundation for the rational design of reusable, selective, and multicomponent VOC detection materials.
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