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Cited by 966

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Carbon-based supercapacitors for efficient energy storage

Open access
Chen, Xuli, Paul, Rajib, Dai, Liming·2017·National Science Review

Abstract

The advancement of modern electronic devices depends strongly on the highly efficient energy sources possessing high energy density and power density. In this regard, supercapacitors show great promise. Due to the unique hierarchical structure, excellent electrical and mechanical properties, and high specific surface area, carbon nanomaterials (particularly, carbon nanotubes, graphene, mesoporous carbon and their hybrids) have been widely investigated as efficient electrode materials in supercapacitors. This review article summarizes progress in high-performance supercapacitors based on carbon nanomaterials with an emphasis on the design and fabrication of electrode structures and elucidation of charge-storage mechanisms. Recent developments on carbon-based flexible and stretchable supercapacitors for various potential applications, including integrated energy sources, self-powered sensors and wearable electronics, are also discussed.

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Crossref · 10.1093/nsr/nwx009

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Metadata

Authors
Chen, X., Paul, R., & Dai, L.
Venue
National Science Review
Published
2017-03-02
Volume
4
Issue
3
Pages
453-489
ISSN
  • 2053-714X (ISSN-L)
  • 2095-5138
OpenAlex
W2592813822
Cited by
966
References
8
FWCI
22.01
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Carbon-based supercapacitors for efficient energy storage. https://app.cochat.ai/papers/10.1093/nsr/nwx009
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Chen, X., Paul, R., & Dai, L. (2017). Carbon-based supercapacitors for efficient energy storage. National Science Review. https://doi.org/10.1093/nsr/nwx009

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.