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Multi-electron reactions for the synthesis of a vanadium-based amorphous material as lithium-ion battery cathode with high specific capacity

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  • Kong, Fanhou
  • Liang, Xue
  • Yi, Lanlin
  • Fang, Xiaohui
  • Yin, Zhongbin
  • Wang, Yulong
  • Zhang, Ruixiang
  • Liu, Longyang
  • Chen, Qing
  • Li, Minghan
  • Li, Changjiu
  • Jiang, Hong
  • Chen, Yongjun

Abstract

The vanadium-based amorphous electrode material can realize the valence state conversion and increase its specific capacity through the multi-electron reaction. We have obtained V2O5–Li3PO4 glass with a strong reducing agent CaC2, realizing a multi-electron reaction of V5+ to V4+ and then to V3+ in the model system. Moreover, we have explored the relationship between valence state, crystallinity, and conductivity limit to compare the cycle performances of amorphous glass batteries. The CaC2 content of 20%, V4+ presented the dominating valence state with a content of 77.5%. VP-C20% exhibited a maximum specific capacity of 319.3 mAh g−1, and the specific capacity after 100 cycles was 280.3 mAh g−1, corresponding to a retention capacity of 87.8%. The electrochemical performance of amorphous vanadium oxide decreased with the increase of the LiV2O5’s nanocrystallinity. Crystallinity and the controllable multi-electron reaction could provide an important reference for designing other new electrode materials with high capacity and long cycle life.

Suggested Citation

  • Kong, Fanhou & Liang, Xue & Yi, Lanlin & Fang, Xiaohui & Yin, Zhongbin & Wang, Yulong & Zhang, Ruixiang & Liu, Longyang & Chen, Qing & Li, Minghan & Li, Changjiu & Jiang, Hong & Chen, Yongjun, 2021. "Multi-electron reactions for the synthesis of a vanadium-based amorphous material as lithium-ion battery cathode with high specific capacity," Energy, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:energy:v:219:y:2021:i:c:s0360544220326207
    DOI: 10.1016/j.energy.2020.119513
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    References listed on IDEAS

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    1. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Ma, Junpeng & Luo, Guangzhao & Teodorescu, Remus, 2020. "An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system," Energy, Elsevier, vol. 206(C).
    2. Chang, Chia-Chin & Huang, Sin-Yi & Chen, Wei-Hsin, 2019. "Thermal and solid electrolyte interphase characterization of lithium-ion battery," Energy, Elsevier, vol. 174(C), pages 999-1011.
    3. Huang, Zonghou & Zhao, Chunpeng & Li, Huang & Peng, Wen & Zhang, Zheng & Wang, Qingsong, 2020. "Experimental study on thermal runaway and its propagation in the large format lithium ion battery module with two electrical connection modes," Energy, Elsevier, vol. 205(C).
    4. Qi Liu & Zhe-Fei Li & Yadong Liu & Hangyu Zhang & Yang Ren & Cheng-Jun Sun & Wenquan Lu & Yun Zhou & Lia Stanciu & Eric A. Stach & Jian Xie, 2015. "Graphene-modified nanostructured vanadium pentoxide hybrids with extraordinary electrochemical performance for Li-ion batteries," Nature Communications, Nature, vol. 6(1), pages 1-10, May.
    5. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    6. Pei, Pucheng & Zhou, Qibin & Wu, Lei & Wu, Ziyao & Hua, Jianfeng & Fan, Huimin, 2020. "Capacity estimation for lithium-ion battery using experimental feature interval approach," Energy, Elsevier, vol. 203(C).
    7. Tian, Jiaqiang & Wang, Yujie & Liu, Chang & Chen, Zonghai, 2020. "Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles," Energy, Elsevier, vol. 194(C).
    8. Jeffrey G. Smith & Donald J. Siegel, 2020. "Low-temperature paddlewheel effect in glassy solid electrolytes," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    9. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
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    2. Wu, Yaqin & Wang, Feiyue & Fan, Zhupu & Wang, Zihang & Yang, Wenying & Ju, Wenqin & Lei, Weixin & Zou, Youlan & Ma, Zengsheng, 2022. "Internally enhanced conductive 3D porous hierarchical biochar framework for lithium sulfur battery," Energy, Elsevier, vol. 255(C).

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