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Cited by 974

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Recent Advances in Nanozyme Research

Wang, Hui, Wan, Kaiwei, Shi, Xinghua·2019·Advanced Materials

Abstract

As a new generation of artificial enzymes, nanozymes have the advantages of high catalytic activity, good stability, low cost, and other unique properties of nanomaterials. Due to their wide range of potential applications, they have become an emerging field bridging nanotechnology and biology, attracting researchers in various fields to design and synthesize highly catalytically active nanozymes. However, the thorough understanding of experimental phenomena and the mechanisms beneath practical applications of nanozymes limits their rapid development. Herein, the progress of experimental and computational research of nanozymes on two issues over the past decade is briefly reviewed: (1) experimental development of new nanozymes mimicking different types of enzymes. This covers their structures and applications ranging from biosensing and bioimaging to therapeutics and environmental protection. (2) The catalytic mechanism proposed by experimental and theoretical study. The challenges and future directions of computational research in this field are also discussed.

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Metadata

Authors
Wang, H., Wan, K., & Shi, X.
Venue
Advanced Materials
Publisher
Wiley
Published
2018-12-27
Volume
31
Issue
45
Pages
e1805368-e1805368
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
OpenAlex
W2907279092
Cited by
974
References
7
FWCI
20.51
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Recent Advances in Nanozyme Research. https://app.cochat.ai/papers/10.1002/adma.201805368
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Wang, H., Wan, K., & Shi, X. (2019). Recent Advances in Nanozyme Research. Advanced Materials. https://doi.org/10.1002/adma.201805368

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.