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Cited by 190

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

An overview of the engineered graphene nanostructures and nanocomposites

Zhu, Jiahua, Chen, Minjiao, He, Qingliang, Shao, Lu, Wei, Suying, Guo, Zhanhu·2013·RSC Advances

Abstract

This critical review focuses on the property analysis of graphene and graphene nanocomposites (GNCs) and their demonstrated superior performances in energy storage and conversion, electrochemical- and bio-sensing, environmental remediation and flame retardant application and atomic thickness membrane separation. The performances of graphene and GNCs are strongly dependent on their chemical component, synthetic method, nanoscale morphology, and assembling structure of the hybrids. The current progress in supercapacitor energy storage density, solar cell power conversion efficiency, thermoelectric energy conversion efficiency, electrochemical sensing capability, biosensor sensitivity, heavy metal adsorption capacity and efficiency, photocatalytic degradation rate of organic dye, flame retardant polymer nanocomposites, graphene and porous graphene membranes is discussed with detailed examples through extensive analysis of the literature.

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Metadata

Authors
Zhu, J., Chen, M., He, Q., Shao, L., Wei, S., & Guo, Z.
Venue
RSC Advances
Published
2013
Volume
3
Issue
45
Pages
22790
ISSN
2046-2069 (ISSN-L)
OpenAlex
W2154995476
Cited by
190
References
4
FWCI
18.65
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An overview of the engineered graphene nanostructures and nanocomposites. https://app.cochat.ai/papers/10.1039/c3ra44621b
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Zhu, J., Chen, M., He, Q., Shao, L., Wei, S., & Guo, Z. (2013). An overview of the engineered graphene nanostructures and nanocomposites. RSC Advances. https://doi.org/10.1039/c3ra44621b

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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.