Theoretical Calculations and the Catalytic Mechanism of Graphitic Carbon Nitride-based Nanozymes: A Review

Abstract

Graphitic carbon nitride-based(g-C3N4-based) nanozymes, a class of highly promising artificial enzyme-mimicking nanomaterials, have attracted a steadily growing body of research interest, with their importance in the field of nanozymology becoming increasingly prominent. Most notably, these nanomaterials exhibit outstanding advantages, including tunable catalytic activity, high stability and low cost. Against this backdrop, this review provides a comprehensive overview of the current research progress on g-C3N4-based nanozymes, encompassing a systematic summary of their classification, activity modulation strategies, catalytic mechanisms and interdisciplinary applications. Theoretical calculations, particularly density functional theory (DFT) calculations, are conducted on g-C3N4-based nanozymes to elucidate their catalytic mechanisms and guide their practical applications in biosensing, environmental remediation and biomedical therapy. This paper highlights the pivotal role of theoretical calculations in deepening our understanding of the structure‒property relationships of these nanozymes. Specifically, DFT calculations can reveal the electronic structure, active site characteristics and reaction pathways of materials at the atomic level, thereby laying a solid theoretical foundation for the rational design of high-performance nanozymes. Finally, this review outlines the current challenges in the research of g-C3N4-based nanozymes and suggests future development directions, indicating that the in-depth integration of theoretical calculations and experimental studies will drive the field toward further advancement.

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Article information

Article type
Review Article
Submitted
07 Jan 2026
Accepted
10 Jun 2026
First published
19 Jun 2026

Nanoscale, 2026, Accepted Manuscript

Theoretical Calculations and the Catalytic Mechanism of Graphitic Carbon Nitride-based Nanozymes: A Review

M. Zheng, X. Gao, F. Ma, W. Li, Y. Shao, M. Guo, J. Du, X. Wang and S. Guo, Nanoscale, 2026, Accepted Manuscript , DOI: 10.1039/D6NR00079G

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