IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v231y2021ics0360544221013505.html
   My bibliography  Save this article

Biomass-derived N/S dual-doped porous hard-carbon as high-capacity anodes for lithium/sodium ions batteries

Author

Listed:
  • Wan, Hongri
  • Shen, Xiran
  • Jiang, Hao
  • Zhang, Cheng
  • Jiang, Kaile
  • Chen, Teng
  • Shi, Liluo
  • Dong, Liming
  • He, Changchun
  • Xu, Yan
  • Li, Jing
  • Chen, Yan

Abstract

The N/S dual-doped porous hard-carbon (N/S-PC) material is obtained by simple heat treatment. The N/S-PC material has the characteristics of hierarchical pore structure, high SSA and porous channels. These features can provide more active sites and space for lithium/sodium ion storage, which is conducive to lithium/sodium ion transfer. The N/S-PC material is characterized by instrument and equipment (SEM, TEM, XPS, Raman, XRD). The N1S1 has excellent rate performance and cycle stability. For LIBs anode materials, the capacity remains at 1060 mAh g-1 after 50 cycles, additionally, it has a capacity of 546 mAh g-1 at 5 A g-1. When N1S1 used as SIBs anode, the capacity can reach 801mAh g-1 at 0.1 A g-1 and 402 mAh g-1 at 5 A g-1, it also shows the same cycle stability and electrochemical performance.

Suggested Citation

  • Wan, Hongri & Shen, Xiran & Jiang, Hao & Zhang, Cheng & Jiang, Kaile & Chen, Teng & Shi, Liluo & Dong, Liming & He, Changchun & Xu, Yan & Li, Jing & Chen, Yan, 2021. "Biomass-derived N/S dual-doped porous hard-carbon as high-capacity anodes for lithium/sodium ions batteries," Energy, Elsevier, vol. 231(C).
  • Handle: RePEc:eee:energy:v:231:y:2021:i:c:s0360544221013505
    DOI: 10.1016/j.energy.2021.121102
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221013505
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.121102?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jilte, Ravindra & Afzal, Asif & Panchal, Satyam, 2021. "A novel battery thermal management system using nano-enhanced phase change materials," Energy, Elsevier, vol. 219(C).
    2. Wu, Hongfei & Zhang, Xingjuan & Cao, Renfeng & Yang, Chunxin, 2021. "An investigation on electrical and thermal characteristics of cylindrical lithium-ion batteries at low temperatures," Energy, Elsevier, vol. 225(C).
    3. Wei, Jingwen & Chen, Chunlin, 2021. "A multi-timescale framework for state monitoring and lifetime prognosis of lithium-ion batteries," Energy, Elsevier, vol. 229(C).
    4. Xiao, Feiyu & Xing, Bobin & Kong, Lingzhao & Xia, Yong, 2021. "Impedance-based diagnosis of internal mechanical damage for large-format lithium-ion batteries," Energy, Elsevier, vol. 230(C).
    5. Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).
    6. Wang, Haimin & Shi, Weijie & Hu, Feng & Wang, Yufei & Hu, Xuebin & Li, Huanqi, 2021. "Over-heating triggered thermal runaway behavior for lithium-ion battery with high nickel content in positive electrode," Energy, Elsevier, vol. 224(C).
    7. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    8. Ma, Yan & Mou, Hongyuan & Zhao, Haiyan, 2020. "Cooling optimization strategy for lithium-ion batteries based on triple-step nonlinear method," Energy, Elsevier, vol. 201(C).
    9. Zhou, Di & Zheng, Wenbin & Chen, Shaohui & Fu, Ping & Zhu, Hongyu & Song, Bai & Qu, Xisong & Wang, Tiancheng, 2021. "Research on state of health prediction model for lithium batteries based on actual diverse data," Energy, Elsevier, vol. 230(C).
    10. Xu, Meng & Wang, Xia & Zhang, Liwen & Zhao, Peng, 2021. "Comparison of the effect of linear and two-step fast charging protocols on degradation of lithium ion batteries," Energy, Elsevier, vol. 227(C).
    11. Li, Changlong & Cui, Naxin & Wang, Chunyu & Zhang, Chenghui, 2021. "Reduced-order electrochemical model for lithium-ion battery with domain decomposition and polynomial approximation methods," Energy, Elsevier, vol. 221(C).
    12. Jiang, Zhibin & Chen, Ling & Zhang, Wenguang & Chen, Shiyu & Jian, Xiying & Liu, Xiang & Chen, Hongyu & Guo, Chunlei & Li, Weishan, 2021. "Sandwich-like NOCC@S8/rGO composite as cathode for high energy lithium-sulfur batteries," Energy, Elsevier, vol. 220(C).
    13. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    14. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Yi-Jie & Gao, Yi-Jun & Wang, Xiaoge & Ye, Qin & Zhang, Ya & Wu, Yu & Chen, Shu-Han & Ruan, Bo & Shi, Dean & Jiang, Tao & Tsai, Fang-Chang & Ma, Ning, 2022. "MoTe2 on metal-organic framework derived MoO2/N-doped carbon rods for enhanced sodium-ion storage properties," Energy, Elsevier, vol. 243(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    2. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    3. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    4. Huang, Zhelin & Xu, Fan & Yang, Fangfang, 2023. "State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model," Energy, Elsevier, vol. 262(PB).
    5. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(C).
    6. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    7. Wu, Ji & Fang, Leichao & Dong, Guangzhong & Lin, Mingqiang, 2023. "State of health estimation of lithium-ion battery with improved radial basis function neural network," Energy, Elsevier, vol. 262(PB).
    8. Wei, Meng & Ye, Min & Zhang, Chuanwei & Li, Yan & Zhang, Jiale & Wang, Qiao, 2023. "A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling," Energy, Elsevier, vol. 283(C).
    9. Zha, Yunfei & Meng, Xianfeng & Qin, Shuaishuai & Hou, Nairen & He, Shunquan & Huang, Caiyuan & Zuo, Hongyan & Zhao, Xiaohuan, 2023. "Performance evaluation with orthogonal experiment method of drop contact heat dissipation effects on electric vehicle lithium-ion battery," Energy, Elsevier, vol. 271(C).
    10. Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
    11. Wang, Huaibin & Wang, Shuyu & Feng, Xuning & Zhang, Xuan & Dai, Kangwei & Sheng, Jun & Zhao, Zhenyang & Du, Zhiming & Zhang, Zelin & Shen, Kai & Xu, Chengshan & Wang, Qinzheng & Sun, Xiaoyu & Li, Yanl, 2021. "An experimental study on the thermal characteristics of the Cell-To-Pack system," Energy, Elsevier, vol. 227(C).
    12. Chen, Zhang & Shen, Wenjing & Chen, Liqun & Wang, Shuqiang, 2022. "Adaptive online capacity prediction based on transfer learning for fast charging lithium-ion batteries," Energy, Elsevier, vol. 248(C).
    13. Weng, Jingwen & Xiao, Changren & Ouyang, Dongxu & Yang, Xiaoqing & Chen, Mingyi & Zhang, Guoqing & Yuen, Richard Kwok Kit & Wang, Jian, 2022. "Mitigation effects on thermal runaway propagation of structure-enhanced phase change material modules with flame retardant additives," Energy, Elsevier, vol. 239(PC).
    14. Wu, Lifeng & Zhang, Yu, 2023. "Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery," Energy, Elsevier, vol. 268(C).
    15. Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
    16. Chen, Dongfang & Pan, Lyuming & Pei, Pucheng & Huang, Shangwei & Ren, Peng & Song, Xin, 2021. "Carbon-coated oxygen vacancies-rich Co3O4 nanoarrays grow on nickel foam as efficient bifunctional electrocatalysts for rechargeable zinc-air batteries," Energy, Elsevier, vol. 224(C).
    17. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    18. Tian, Xiaohui & Che, Lukang & Cheng, Yunnian & Liu, Mengdie & Selabi, Naomie Beolle Songwe & Zhou, Yingke, 2024. "Remarkable chemical adsorption and catalysis of monodisperse metallic cobalt sulfide nanoparticles enable long-cycling Li–S battery with high areal capacity and low shuttle constant," Energy, Elsevier, vol. 288(C).
    19. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    20. Dai, Houde & Wang, Jiaxin & Huang, Yiyang & Lai, Yuan & Zhu, Liqi, 2024. "Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization," Renewable Energy, Elsevier, vol. 222(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:231:y:2021:i:c:s0360544221013505. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.