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Cited by 194

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

Nanostructured conducting polymer hydrogels for energy storage applications

Shi, Ye, Peng, Lele, Yu, Guihua·2015·Nanoscale

Abstract

Conducting polymer hydrogels are emerging as a promising class of polymeric materials for various technological applications, especially for energy storage devices due to their unique combination of advantageous features of conventional polymers and organic conductors. To overcome the drawbacks of conventional synthesis, new synthetic routes in which acid molecules are adopted as both crosslinkers and dopants have been developed for conducting polymer hydrogels with unique 3D hierarchical porous nanostructures, resulting in high electrical conductivity, large surface area, structural tunability and hierarchical porosity for rapid mass/charge transport. The newly developed conducting polymer hydrogels exhibit high performance when applied as active electrode materials for electrochemical capacitors or as functional binder materials for high-energy lithium-ion batteries. This feature article summarizes the synthesis of conducting polymer hydrogels, presents their applications in energy storage, and discusses further opportunities and challenges.

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Metadata

Authors
Shi, Y., Peng, L., & Yu, G.
Venue
Nanoscale
Published
2015
Volume
7
Issue
30
Pages
12796-12806
ISSN
  • 2040-3364 (ISSN-L)
  • 2040-3372
OpenAlex
W1955261065
Cited by
194
References
3
FWCI
13.32
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Nanostructured conducting polymer hydrogels for energy storage applications. https://app.cochat.ai/papers/10.1039/c5nr03403e
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Shi, Y., Peng, L., & Yu, G. (2015). Nanostructured conducting polymer hydrogels for energy storage applications. Nanoscale. https://doi.org/10.1039/c5nr03403e

Data sources

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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.