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Cited by 504

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

A comprehensive review on graphene-based anti-corrosive coatings

Cui, Gan, Bi, Zhenxiao, Zhang, Ruiyu, Liu, Jianguo, Yu, Xin, Li, Zili·2019·Chemical Engineering Journal

Abstract

Abstract Graphene has attracted significant attention for applications in many industrial fields due to its excellent anti-corrosion properties, and a series of satisfactory results have been achieved. In recent years, the scope of research into graphene-based anti-corrosive applications has been extremely diversified and encompasses two aspects: pure graphene coatings and graphene composite coatings. In this study, the application of graphene coatings to metal anti-corrosion is comprehensively reviewed. First, the preparation methods for graphene and pure graphene coatings are described in detail. Second, the corrosion resistance, existing problems, and optimizations of pure graphene coating are presented. Third, methods for improving the dispersibility of graphene, such as chemical modification and surface decoration nanoparticles, are introduced. Next, the corrosion resistance, anti-corrosion mechanism, existing problems, and optimization measures of graphene composite coatings are summarized. Finally, future development directions for graphene anti-corrosive coatings are proposed, and their viability in near-future applications is discussed.

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Metadata

Authors
Cui, G., Bi, Z., Zhang, R., Liu, J., Yu, X., & Li, Z.
Venue
Chemical Engineering Journal
Publisher
Elsevier BV
Published
2019-10
Volume
373
Pages
104-121
ISSN
1385-8947 (ISSN-L)
OpenAlex
W2943914723
Cited by
504
References
3
FWCI
17.63
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A comprehensive review on graphene-based anti-corrosive coatings. https://app.cochat.ai/papers/10.1016/j.cej.2019.05.034
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Cui, G., Bi, Z., Zhang, R., Liu, J., Yu, X., & Li, Z. (2019). A comprehensive review on graphene-based anti-corrosive coatings. Chemical Engineering Journal. https://doi.org/10.1016/j.cej.2019.05.034

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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.