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Cited by 239

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

Microorganism‐Derived Heteroatom‐Doped Carbon Materials for Oxygen Reduction and Supercapacitors

Zhu, Hui, Yin, Jiao, Wang, Xiaolei, Wang, Hongyu, Yang, Xiurong·2013·Advanced Functional Materials

Abstract

Heteroatom‐doped carbon (HDC) has attracted tremendous attention due to its promising application in energy conversion and storage. Herein, due to its abundance high rate of reproduction, the microorganism, Bacillus subtilis, is selected as a precursor. An effective ionothermal process is adopted to produce the HDCs. Using acid activation, the obtained sample exhibits excellent electrocatalytic activity, long‐term stability, and excellent resistance to crossover effects in oxygen reduction. Additionally, the base‐treated sample exhibits superior performance in capacitors to most commercially available carbon materials. Even at a high current density, a relatively high capacitance is retained, indicating a great potential for direct application in energy storage.

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Crossref · 10.1002/adfm.201201643

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Metadata

Authors
Zhu, H., Yin, J., Wang, X., Wang, H., & Yang, X.
Venue
Advanced Functional Materials
Publisher
Wiley
Published
2012-10-12
Volume
23
Issue
10
Pages
1305-1312
ISSN
  • 1616-301X (ISSN-L)
  • 1616-3028
OpenAlex
W2052634846
Cited by
239
References
4
FWCI
5.10
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Microorganism‐Derived Heteroatom‐Doped Carbon Materials for Oxygen Reduction and Supercapacitors. https://app.cochat.ai/papers/10.1002/adfm.201201643
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Zhu, H., Yin, J., Wang, X., Wang, H., & Yang, X. (2013). Microorganism‐Derived Heteroatom‐Doped Carbon Materials for Oxygen Reduction and Supercapacitors. Advanced Functional Materials. https://doi.org/10.1002/adfm.201201643

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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.