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Cited by 292

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

Self‐Assembled Fe–N‐Doped Carbon Nanotube Aerogels with Single‐Atom Catalyst Feature as High‐Efficiency Oxygen Reduction Electrocatalysts

Open access
Zhu, Chengzhou, Fu, Shaofang, Song, Junhua, Shi, Qiurong, Su, Dong, Engelhard, Mark H., Li, Xiaolin, Xiao, Dongdong, Li, Dongsheng, Estevez, Luis, Du, Dan, Lin, Yuehe·2017·Small

Abstract

Self-assembled M-N-doped carbon nanotube aerogels with single-atom catalyst feature are for the first time reported through one-step hydrothermal route and subsequent facile annealing treatment. By taking advantage of the porous nanostructures, 1D nanotubes as well as single-atom catalyst feature, the resultant Fe-N-doped carbon nanotube aerogels exhibit excellent oxygen reduction reaction electrocatalytic performance even better than commercial Pt/C in alkaline solution.

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Crossref · 10.1002/smll.201603407

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Metadata

Authors
Zhu, C., Fu, S., Song, J., Shi, Q., Su, D., Engelhard, M. H., Li, X., Xiao, D., Li, D., Estevez, L., Du, D., & Lin, Y.
Venue
Small
Publisher
Wiley
Published
2017-02-06
Volume
13
Issue
15
ISSN
  • 1613-6810 (ISSN-L)
  • 1613-6829
OpenAlex
W2586090543
Cited by
292
References
44
FWCI
12.03
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Self‐Assembled Fe–N‐Doped Carbon Nanotube Aerogels with Single‐Atom Catalyst Feature as High‐Efficiency Oxygen Reduction Electrocatalysts. https://app.cochat.ai/papers/10.1002/smll.201603407
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Zhu, C., Fu, S., Song, J., Shi, Q., Su, D., Engelhard, M. H., Li, X., Xiao, D., Li, D., Estevez, L., Du, D., & Lin, Y. (2017). Self‐Assembled Fe–N‐Doped Carbon Nanotube Aerogels with Single‐Atom Catalyst Feature as High‐Efficiency Oxygen Reduction Electrocatalysts. Small. https://doi.org/10.1002/smll.201603407

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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.