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Cited by 3,837

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Graphene-based composites

Open access
Huang, Xiao, Qi, Xiaoying, Boey, Freddy, Zhang, Hua·2012·Chem. Soc. Rev.

Abstract

Graphene has attracted tremendous research interest in recent years, owing to its exceptional properties. The scaled-up and reliable production of graphene derivatives, such as graphene oxide (GO) and reduced graphene oxide (rGO), offers a wide range of possibilities to synthesize graphene-based functional materials for various applications. This critical review presents and discusses the current development of graphene-based composites. After introduction of the synthesis methods for graphene and its derivatives as well as their properties, we focus on the description of various methods to synthesize graphene-based composites, especially those with functional polymers and inorganic nanostructures. Particular emphasis is placed on strategies for the optimization of composite properties. Lastly, the advantages of graphene-based composites in applications such as the Li-ion batteries, supercapacitors, fuel cells, photovoltaic devices, photocatalysis, as well as Raman enhancement are described (279 references).

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Crossref · 10.1039/c1cs15078b

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Metadata

Authors
Huang, X., Qi, X., Boey, F., & Zhang, H.
Venue
Chem. Soc. Rev.
Published
2012
Volume
41
Issue
2
Pages
666-686
ISSN
  • 0306-0012 (ISSN-L)
  • 1460-4744
OpenAlex
W2118609172
Cited by
3,837
References
12
FWCI
11.65
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Graphene-based composites. https://app.cochat.ai/papers/10.1039/c1cs15078b
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Huang, X., Qi, X., Boey, F., & Zhang, H. (2012). Graphene-based composites. Chem. Soc. Rev. https://doi.org/10.1039/c1cs15078b

Data sources

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.