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Cited by 648

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Carbon Nanozymes: Enzymatic Properties, Catalytic Mechanism, and Applications

Sun, Hanjun, Zhou, Ya, Ren, Jinsong, Qu, Xiaogang·2018·Angewandte Chemie International Edition

Abstract

Nanozymes have advantages over natural enzymes, such as facile production on large scale, long storage time, low costs, and high stability in harsh environments. Carbon nanomaterials (CNMs), including fullerenes, carbon nanotubes, graphene, carbon quantum dots, and graphene quantum dots, have become a star family in materials science. As a new class of nanozymes, the catalytic activity of CNMs and their hybrids has been extensively reported. In this Minireview, recent progress of CNMs based artificial enzymes, focusing on those with peroxidase‐like activity, has been summarized. The enzymatic properties, catalytic mechanisms, and novel applications of CNM nanozymes in sensing, therapy, and environmental engineering are discussed in detail. Additionally, we also highlight the remaining challenges and unsolved problems. With the fast development of bionanotechnology, the unique enzymatic properties and advantages of CNM nanozymes have received much attention and will continue to be an active and challenging field for the years to come.

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Crossref · 10.1002/anie.201712469

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Metadata

Authors
Sun, H., Zhou, Y., Ren, J., & Qu, X.
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2018-05-07
Volume
57
Issue
30
Pages
9224-9237
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
OpenAlex
W2791731272
Cited by
648
References
3
FWCI
21.78
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Carbon Nanozymes: Enzymatic Properties, Catalytic Mechanism, and Applications. https://app.cochat.ai/papers/10.1002/anie.201712469
The paper
Sun, H., Zhou, Y., Ren, J., & Qu, X. (2018). Carbon Nanozymes: Enzymatic Properties, Catalytic Mechanism, and Applications. Angewandte Chemie International Edition. https://doi.org/10.1002/anie.201712469

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.