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Cited by 304

The DOI matches its Crossref record; 170 of the 170 references it cites were found in a registry or an index.

Carbon Nanomaterials in Different Dimensions for Electrochemical Energy Storage

Ni, Jiangfeng, Li, Yan·2016·Advanced Energy Materials
DOI 10.1002/aenm.201600278
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Abstract

Carbon nanomaterials including fullerenes, carbon nanotubes, graphene, and their assemblies represent a unique type of materials in diverse formats and dimensions. They feature a large surface area, superior conductivity, fast charge transport, and intrinsic stability, which are essentially required for vari­ous electrochemical energy storage (EES) systems such as Li‐ion batteries, supercapacitors, and redox flow cells. The scaled‐up and reliable production and assembly of carbon nanomaterials is a prerequisite for the development of carbon nanomaterial‐based EES devices. In this progress report, the preparation of carbon nanostructures and the state‐of‐the‐art applications of carbon nanomaterials with different dimensions in versatile EES systems are summarized. The importance of the synergetic effect induced by interactions between nanocarbons and active electrode species is highlighted. The main challenges and prospects in this field are also discussed.

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Show all 170 references

Research works printed but not linked: 46

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Metadata

Authors
Ni, J., & Li, Y.
Venue
Advanced Energy Materials
Publisher
Wiley
Published
2016-03-29
Volume
6
Issue
17
ISSN
  • 1614-6832 (ISSN-L)
  • 1614-6840
DOI
10.1002/aenm.201600278
OpenAlex
W2316586673
Cited by
304
References
170
FWCI
9.99
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)

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CoChat. (2026). Citation check: Carbon Nanomaterials in Different Dimensions for Electrochemical Energy Storage. https://app.cochat.ai/papers/10.1002/aenm.201600278
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Ni, J., & Li, Y. (2016). Carbon Nanomaterials in Different Dimensions for Electrochemical Energy Storage. Advanced Energy Materials. https://doi.org/10.1002/aenm.201600278

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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.