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Amorphous SnO2/graphene aerogel nanocomposites harvesting superior anode performance for lithium energy storage

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  • Fan, Linlin
  • Li, Xifei
  • Yan, Bo
  • Li, Xiaojia
  • Xiong, Dongbin
  • Li, Dejun
  • Xu, Hui
  • Zhang, Xianfa
  • Sun, Xueliang

Abstract

The Sn-based materialshave been hindered from practical use for lithium ion batteries due to the inherent volume change leading to poor cycling performance. To mitigate this challenge, in this study, amorphous SnO2/graphene aerogel nanocomposites are fabricated via asimple hydrothermal approach.The amorphous nature of SnO2 is clearly determined in detail by transmission electron microscopy, aberration-corrected scanning transmission electron microscopy, and X-ray diffraction measurement. The as-prepared material shows satisfying reversible capacity and significant cyclic stability. For instance,it delivers an excellent discharge capacity of 700.1mAhg−1 in 80th cycle at a current density of 100mAg−1, in accordance with a high retention capacity of 97.6% compared to that of the sixth cycles, which is much better than crystalline SnO2/graphene aerogel. The enhanced electrochemical performance can be ascribed to the intrinsic isotropic nature, smaller size, and high electrochemical reaction kinetics of amorphous SnO2, together with the graphene aerogels matrix. Therefore, this study may provide an effortless, economic, and environmental friendly strategy to fabricate high volume change electrode materials for lithium ion batteries.

Suggested Citation

  • Fan, Linlin & Li, Xifei & Yan, Bo & Li, Xiaojia & Xiong, Dongbin & Li, Dejun & Xu, Hui & Zhang, Xianfa & Sun, Xueliang, 2016. "Amorphous SnO2/graphene aerogel nanocomposites harvesting superior anode performance for lithium energy storage," Applied Energy, Elsevier, vol. 175(C), pages 529-535.
    • Handle: RePEc:eee:appene:v:175:y:2016:i:c:p:529-535
    DOI: 10.1016/j.apenergy.2016.02.094
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    References listed on IDEAS

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    1. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    2. Burgos-Mellado, Claudio & Orchard, Marcos E. & Kazerani, Mehrdad & Cárdenas, Roberto & Sáez, Doris, 2016. "Particle-filtering-based estimation of maximum available power state in Lithium-Ion batteries," Applied Energy, Elsevier, vol. 161(C), pages 349-363.
    3. Abdel Monem, Mohamed & Trad, Khiem & Omar, Noshin & Hegazy, Omar & Mantels, Bart & Mulder, Grietus & Van den Bossche, Peter & Van Mierlo, Joeri, 2015. "Lithium-ion batteries: Evaluation study of different charging methodologies based on aging process," Applied Energy, Elsevier, vol. 152(C), pages 143-155.
    4. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
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    1. Wang, Bin & Wang, Shifeng & Tang, Yuanyuan & Tsang, Chi-Wing & Dai, Jinchuan & Leung, Michael K.H. & Lu, Xiao-Ying, 2019. "Micro/nanostructured MnCo2O4.5 anodes with high reversible capacity and excellent rate capability for next generation lithium-ion batteries," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
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