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Cited by 499

The DOI matches its Crossref record; 210 of the 210 references it cites were found in a registry or an index.

Conversion Reaction‐Based Oxide Nanomaterials for Lithium Ion Battery Anodes

Yu, Seung‐Ho, Lee, Soo Hong, Lee, Dong Jun, Sung, Yung‐Eun, Hyeon, Taeghwan·2016·Small

Abstract

Developing high‐energy‐density electrodes for lithium ion batteries (LIBs) is of primary importance to meet the challenges in electronics and automobile industries in the near future. Conversion reaction‐based transition metal oxides are attractive candidates for LIB anodes because of their high theoretical capacities. This review summarizes recent advances on the development of nanostructured transition metal oxides for use in lithium ion battery anodes based on conversion reactions. The oxide materials covered in this review include oxides of iron, manganese, cobalt, copper, nickel, molybdenum, zinc, ruthenium, chromium, and tungsten, and mixed metal oxides. Various kinds of nanostructured materials including nanowires, nanosheets, hollow structures, porous structures, and oxide/carbon nanocomposites are discussed in terms of their LIB anode applications.

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Crossref · 10.1002/smll.201502299

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Metadata

Authors
Yu, S., Lee, S. H., Lee, D. J., Sung, Y., & Hyeon, T.
Venue
Small
Publisher
Wiley
Published
2015-12-02
Volume
12
Issue
16
Pages
2146-2172
ISSN
  • 1613-6810 (ISSN-L)
  • 1613-6829
OpenAlex
W2183318011
Cited by
499
References
210
FWCI
20.85
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Conversion Reaction‐Based Oxide Nanomaterials for Lithium Ion Battery Anodes. https://app.cochat.ai/papers/10.1002/smll.201502299
The paper
Yu, S., Lee, S. H., Lee, D. J., Sung, Y., & Hyeon, T. (2016). Conversion Reaction‐Based Oxide Nanomaterials for Lithium Ion Battery Anodes. Small. https://doi.org/10.1002/smll.201502299

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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.