External Li supply reshapes Li deficiency and lifetime limit of batteries

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Data availability

The data that support the findings of this study are available from the corresponding author upon request. Source data are provided with this paper.

Code availability

The code, instruction and SMILES structures for the hierarchical clustering approach are available on GitHub (https://github.com/WangGroupFDU/HierarchicalCluster_Li_Solubility_AnodeLimit). The code and instruction for obtaining functional molecules from reference, along with the 20 types of induced redox-active centres are available on GitHub (https://github.com/Peng-Gaoresearchgroup/External_Li_supply).

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Acknowledgements

We thank B. Yan and Z. Li for the discussion on machine learning experiments. This work was supported by the National Key Research and Development Programme of China (grant no. 2022YFB2402300), Ministry of Science and Technology (grant no. 2022YFA1203000), the National Natural Science Foundation of China (grant nos 22109027, 52222310, T2222005, T2321003 and 22335003), the Science and Technology Commission of Shanghai Municipality (grant no. 21ZR1404000) and the Fundamental Research Funds for the Central Universities (grant no. 20720220010). J.W. and Y.W. acknowledge support from the China National Natural Science Fund Major Programme (grant no. 92372126).

Author information

Authors and Affiliations

  1. State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Institute of Fiber Materials and Devices, Fudan University, Shanghai, China

    Shu Chen, Guanbin Wu, Haibo Jiang, Jifeng Wang, Tiantian Chen, Chenyang Han, Wenwen Wang, Rongchen Yang, Xiaocheng Gong, Chuanfa Li, Mengyao Zhu, Kun Zhang, Yifei Xu, Ying Wang, Peining Chen, Bingjie Wang, Huisheng Peng & Yue Gao

  2. Research Center of AI for Polymer Science, Laboratory of Advanced Materials, Collaborative Innovation Center of Chemistry for Energy Materials, Fudan University, Shanghai, China

    Shu Chen, Guanbin Wu, Haibo Jiang, Tiantian Chen, Chenyang Han, Wenwen Wang, Rongchen Yang, Xiaocheng Gong, Chuanfa Li, Mengyao Zhu, Kun Zhang, Peining Chen, Bingjie Wang, Huisheng Peng & Yue Gao

  3. Key Laboratory of Advanced Energy Materials Chemistry, Renewable Energy Conversion and Storage Center, College of Chemistry, Nankai University, Tianjin, China

    Jiahua Zhao & Kai Zhang

  4. Hunan Institute of Engineering, Xiangtan, China

    Zhihang Tang

  5. Guangdong Provincial Key Laboratory of Service Safety for New Energy Materials, College of Materials Science and Engineering, Shenzhen University, Shenzhen, China

    Zhe Hu

  6. Department of Chemistry and Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Institute of New Energy, Fudan University, Shanghai, China

    Yongyao Xia

Authors

  1. Shu Chen
  2. Guanbin Wu
  3. Haibo Jiang
  4. Jifeng Wang
  5. Tiantian Chen
  6. Chenyang Han
  7. Wenwen Wang
  8. Rongchen Yang
  9. Jiahua Zhao
  10. Zhihang Tang
  11. Xiaocheng Gong
  12. Chuanfa Li
  13. Mengyao Zhu
  14. Kun Zhang
  15. Yifei Xu
  16. Ying Wang
  17. Zhe Hu
  18. Peining Chen
  19. Bingjie Wang
  20. Kai Zhang
  21. Yongyao Xia
  22. Huisheng Peng
  23. Yue Gao

Contributions

Y.G. and H.P. conceived the idea and designed the experiments. S.C. synthesized the organic Li salts. G.W. and R.Y. conducted electrochemical tests of the molecules. J.W. and Y.W. calculated the molecular descriptor and conceived the machine learning workflow. G.W., T.C., S.C., W.W. and Z.T. conducted the collection of functional molecules from references and patents, the induction of redox-active centres and ligands, and the identification of recommended molecule structures. J.Z., Z.H. and K.Z. conducted the fabrication of sulfurized polyacrylonitrile pouch cells. H.J., X.G., C.L., P.C. and B.W. conducted the assembly and testing of fibre batteries. C.H., W.W. and M.Z. conducted material characterization experiments. T.C. and Y.X. conducted the TEM test. All authors discussed and analysed the data. Y.G. and H.P. prepared the manuscript with inputs from all other co-authors.

Corresponding authors

Correspondence to Huisheng Peng or Yue Gao.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Jinjin Li, Min-Sik Park and Pengjian Zuo for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Reaction pathways for the discovery of the organic Li salts through machine learning.

a, A set of 20 redox-active centres, selected for their potential in organic electrochemistry, were identified as candidates capable of anodic oxidation and subsequent Li-ion release during battery charging. b, 12 organic ligands that were selected based on electron drawing and donating effects. The redox-active centres were algorithmically paired with these organic ligands to construct a comprehensive library. This library served as the dataset for machine learning algorithms, which were employed to predict and prioritize candidates with optimal electrochemical properties.

Extended Data Fig. 2 A molecular library for machine learning, consisting of 240 molecules.

The combination of the redox-active centres and organic ligands in Extended Data Fig. 1 led to these 240 molecules. We employed quantum chemical methods to simulate the decomposition potentials and solubilities of 30 selected molecules (labelled in blue).

Extended Data Fig. 3 Hierarchical clustering analysis based on anodic limit.

a, A heat map displaying feature vectors (28 molecular descriptors detailed in Supplementary Discussion 2) for 240 molecules. The colour bar represents the values of the descriptors. Distinct features were observed within black dotted boxes. b, A hierarchical cluster dendrogram based on anodic limit. A partition line (dashed black line) was chosen, classifying 240 molecules into 19 groups (G1 to G19). The numbers of the 30 calculated representative molecules are labelled in blue, and their structures are illustrated in Extended Data Fig. 2. c, A statistical graph of the number of molecules per group and the percentage of molecules with theoretically favorable properties. Three groups (G3, G4, and G6) were identified as the favorable molecules were concentrated in them30,31. d, Violin plots illustrating the anodic limits of the calculated molecules across all groups (blue curve), in the identified groups (red curve), and in the remaining groups (gray curve). The median decomposition potential for the molecules in the identified groups was 4.2 V, significantly lower than that of in all groups and in the remaining groups.

Extended Data Fig. 4 Hierarchical clustering analysis based on solubility energy.

a, A heat map displaying feature vectors (28 molecular descriptors detailed in Supplementary Discussion 2) for 240 molecules. The colour bar represents the values of the descriptors. Distinct features were observed within black dotted boxes. b, A hierarchical cluster dendrogram based on solubility energy. A partition line (dashed black line) was chosen, classifying 240 molecules into 25 groups (G1 to G25). The numbers of the 30 calculated representative molecules are labelled in blue, and their structures are illustrated in Extended Data Fig. 2. c, A statistical graph of the number of molecules per group and the percentage of molecules with theoretically favorable properties. Two groups (G8 and G10) were identified as the favorable molecules were concentrated in them30,31. d, Violin plots illustrating the solubility energies of the calculated molecules across all groups (blue curve), in the identified groups (red curve), and in the remaining groups (gray curve). The median solubility energy for the molecules in the identified groups was −1.5 kJ mol−1, lower than that of in all groups and in the remaining groups.

Extended Data Fig. 5 LiSO2CF3 discovery leveraging machine learning and organic electrochemistry.

a, Voltage profiles of lithium sulfinates with various organic ligands. They notably possess decomposition potentials under 4.2 V. b, Voltage profiles of LiSO2CF3 charged at a current density of 382 mA g−1 and 45 °C. c, Solubilities of LiSO2CF3 in different electrolytes including 1.0 M lithium Bis(trifluoromethanesulfonyl)imide (LiTFSI) in dioxolane (DOL)/dimethoxyethane (DME) (1:1, v/v) (i), 0.5 M LiPF6 in tetramethylene sulfone (ii), 1.0 M LiPF6 in EC/ethyl methyl carbonate (EMC) (3:7, v/v) (iii), 1.0 M LiPF6 in EC/diethyl carbonate (DEC)/DMC (1:1:1, v/v) (iv), 1.0 M LiPF6 in EC/DEC/EMC (1:1:1, v/v) (v), 1.0 M LiClO4 in EC/DEC/EMC (1:1:1, v/v) (vi), 1.0 M LiPF6 in EC/DEC (3:7, v/v) (vii), 1.0 M LiPF6 in EC/DEC (1:1, v/v) (viii), and 1.0 M LiPF6 in EC/DMC (3:7, v/v) (ix). d, Solubilities of various lithium sulfinates in a 1.0 M LiPF6 in EC/EMC (3:7, v/v) electrolyte. e, Raman spectra of LiSO2CF3 before and after being exposed to the air. f, Voltage profile of LiSO2CF3 that was stored in the ambient environment for one month. The specific capacity and plateau were consistent with the fresh sample in Fig. 2c. g,h, Voltage profiles of LiSO3CF3 (g) and LiCO2CF3 (h). No capacities were achieved during the charge to 4.3 V, which is the upper limit of the decomposition limit.

Extended Data Fig. 6 A residue-free and non-destructive Li supply.

a, Mass spectra of the C2F6 derived from LiSO2CF3. b,c, 13C (b) and 1H (c) NMR spectra of the electrolyte incorporating LiSO2CF3 before and after Li supply. d, High-resolution XPS spectra of the CEI layers on LiFePO4 before (brown curves) and after (green curves) Li supply. The compositions are consistent. e,f, TEM images and the corresponding FFT images of the LiFePO4 surface before (e) and after (f) Li supply. The nanostructures at the surface are maintained before and after Li supply. g, Voltage profiles of Li2C4O4. At a current density of 42 mA g−1, the decomposition of Li2C4O4 cannot be completed in a single cycle even charged to 4.3 V.

Extended Data Fig. 7 Comparative analysis of Li supply approach and conventional prelithiation techniques.

a,b, Electrochemical performance and photograph of the LiFePO4 cathode with 10.0 wt.% of Li2NiO2. The use of massive strongly basic Li2NiO2 causes slurry agglomerations, electrode breakdown, and reduced capacity. c, XPS spectra of the regular LiFePO4 cathode (brown curve) and the electrode in the presence of 10.0 wt.% Li2NiO2 (blue curve). The dominant salts on the regular surface were Li-CO2- (the peak centred at 55.2 eV in the Li 1 s spectrum) that are derived from carbonate electrolyte, while the introduction of Li2NiO2 resulted in massive LiOH (the peak centred at 55.0 eV in the Li 1 s spectrum) on the electrode surface. d-g, Electrochemical performance and photograph of the LiFePO4 cathode with 10.0 wt.% of Li5FeO4 (d,e) and Li2O (f,g). The use of massive Li5FeO4 and Li2O showed slurry agglomerations, electrode breakdown, and incomplete decomposition in the cell formation cycle. h, Photograph of the LiFePO4 electrodes in contact with a LiSO2CF3-infused carbonate electrolyte. The electrode maintained structural integrity, indicating compatibility with the LiSO2CF3-infused carbonate electrolyte. i, Photograph of the LiFePO4 electrodes in contact with a solvated electron solution of Li metal and biphenyl. The electrode exhibited signs of degradation, attributed to the decomposition of polyvinylidene difluoride binder and corrosion of the aluminum current collector in the presence of the highly alkaline and reductive solution.

Extended Data Fig. 8 External Li supply for Li-deficient batteries incorporating metal oxide cathodes.

a, X-ray diffraction patterns of the as-prepared Cr8O21 materials. b,c, SEM images of the Cr8O21 cathode before (b) and after (c) the Li supply. No visible damage was observed. d,e, Cycling performance of an anode-less Cr8O21 cell and a Li|Cr8O21 cell. The consistent cycle lives (d) and voltage profiles (e) verify that the external Li supply causes no side effects. f, Photograph of the anode-less Cr8O21 cell. g,h, Cycling performance (g) and voltage profiles (h) of 13.7 Ah, 352 Wh kg−1 SiO|LiNi0.8Mn0.1Co0.1O2 pouch cells. The use of LiSO2CF3 successfully supplies the 30.1% of the active Li ions of the cell in the formation cycle. The cell delivered an 84.2% capacity retention over 1,220 cycles. i,j, SEM images of the LiNi0.8Mn0.1Co0.1O2 cathode (i) and SiO anode (j) before and after Li supply. No damages were found on the electrodes, further verifying residue-free and non-destructive features of external Li supply.

Extended Data Fig. 9 Fabrication and performance of the sulfurized polyacrylonitrile pouch cells.

a-d, Photographs of the roll-to-roll coating (a) and drying (b), compressing (c), and slitting (d) of the organic sulfurized polyacrylonitrile electrodes. e-h, SEM images of the organic cathode before (e and f) and after (g and h) cycling. The electrode structures are stable at an areal capacity of 8.7 mAh cm−2. i-l, SEM images of Li deposition on the CNT current collector. The pristine CNT film is porous (i and j). Dense and dendrite-free Li@CNT electrodes were captured after Li supply (k and l). m,n, Raman spectrum (m) and the elemental analysis (n) of the as-prepared organic sulfurized polyacrylonitrile material. o,p, Cycling performance (o) and voltage profiles (p) of anode-less organic sulfurized polyacrylonitrile cells with different Li supply amounts. A 200% Li supply is optimal, as less Li supply resulted in a shorter cycle life, while more Li supply did not increase the cycle life. q, Voltage profiles and the photograph of LiSO2CF3 incorporated in the cell as powders. The charging process was conducted at an elevated temperature of 45 °C and took 102 h to deliver a full capacity of 189.3 mAh g−1, close to the theoretical value of 191.5 mAh g−1.

Extended Data Fig. 10 Rejuvenation of cycled Li-ion batteries.

a, Impedance spectra of the cycled graphite|LiFePO4 cell before and after Li supply. b, Rate capability of the graphite|LiFePO4 cells with and without Li supply. c, Cycling performance of a control cell undergoing additional electrolyte supply after 1,747 cycles. No capacity recovery was observed. d, Illustration of the cell connected with polytetrafluoroethylene tubes for electrolyte filling and removal. e, Voltage profiles of the cycled cylindrical cell undergoing Li supply. After charging (green curve), the discharge capacity was increased to 156.3 mAh g−1 (blue curve) from the original value of 126.6 mAh g−1 (brown curve). f, CT image of the cylindrical cell before Li supply. g,h, Cycling performance (g) and voltage profiles (h) of a graphite LiCoO2 fibre battery before and after Li supply. The capacity was recovered from 135.2 to 145.1 mAh g−1. i, Photograph of the convenient Li supply process for a fibre battery that powers a robot arm. j, Voltage profiles of the Li supply for a graphite LiCoO2 thin-film battery. The capacity was recovered from 117.7 to 130.7 mAh g−1. k, Voltage profiles of anode-less air battery with Li supply. Li can be provided by the air-stable LiSO2CF3, instead of Li metal.

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Chen, S., Wu, G., Jiang, H. et al. External Li supply reshapes Li deficiency and lifetime limit of batteries. Nature 638, 676–683 (2025). https://doi.org/10.1038/s41586-024-08465-y

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