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Three-Dimensional Hierarchical Ternary Nanostructures for High-Performance Li-Ion Battery Anodes

Liu, Borui, Soares, Paulo, Checkles, Constantine, Zhao, Yu, Yu, Guihua·2013·Nano Letters

Abstract

Silicon is considered one of the most promising anode materials for high-performance Li-ion batteries due to its 4200 mAh/g theoretical specific capacity, relative abundance, low cost, and environmental benignity. However, silicon experiences a dramatic volume change (∼300%) during full charge/discharge cycling, leading to severe capacity decay and poor cycling stability. Here, we report a three-dimensional (3D) ternary silicon nanoparticles/conducting polymer/carbon nanotubes hybrid anode material for Li-ion batteries. The hierarchical conductive hydrogel framework with carbon nanotubes as the electronic fortifier offers a continuous electron transport network and high porosity to accommodate the volume expansion of Si particles. By 3D wrapping of silicon nanoparticles/single-wall carbon nanotubes with conducting polymer nanostructures, a greatly improved cycling performance is achieved with reversible discharge capacity over 1600 mAh/g and 86% capacity retention over 1000 cycles at the current rate of 3.3 A/g. Our findings represent a new direction for fabricating robust, high-performance lithium-ion batteries and related energy storage applications with advanced nanostructured materials.

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Metadata

Authors
Liu, B., Soares, P., Checkles, C., Zhao, Y., & Yu, G.
Venue
Nano Letters
Published
2013-06-20
Volume
13
Issue
7
Pages
3414-3419
ISSN
  • 1530-6984 (ISSN-L)
  • 1530-6992
OpenAlex
W2328725273
Cited by
317
References
0
FWCI
25.96
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Three-Dimensional Hierarchical Ternary Nanostructures for High-Performance Li-Ion Battery Anodes. https://app.cochat.ai/papers/10.1021/nl401880v
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Liu, B., Soares, P., Checkles, C., Zhao, Y., & Yu, G. (2013). Three-Dimensional Hierarchical Ternary Nanostructures for High-Performance Li-Ion Battery Anodes. Nano Letters. https://doi.org/10.1021/nl401880v

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.