Abstract
Lithium-metal batteries are a promising technology to address the emerging demand for high-energy-density storage systems. However, their cycling encounters a low Coulombic efficiency (CE) due to the unceasing electrolyte decomposition. Improving the stability of solid electrolyte interphase (SEI) suppresses the decomposition and increases CE. However, SEI morphology and chemistry alone cannot account for CE, and a full explanation is still lacking. Here we report that in diverse electrolytes, the large shift (>0.6 V) in the Li electrode potential and its association with the Li+ coordination structure influence the CE. Machine learning regression analysis and vibrational spectroscopy revealed that the formation of ion pairs is essential for upshifting the Li electrode potential, that is, for weakening the reducing ability of Li, which would lead to a high CE with diminished electrolyte decomposition. Various electrolytes with enhanced ion-pairing solution structure are designed to enable a significantly improved CE (>99%).
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Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
Nature Communications Open Access 15 May 2023
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References
Lin, D., Liu, Y. & Cui, Y. Reviving the lithium metal anode for high-energy batteries. Nat. Nanotechnol. 12, 194–206 (2017).
Liu, B., Zhang, J.-G. & Xu, W. Advancing lithium metal batteries. Joule 2, 833–845 (2018).
Zhang, X., Yang, Y. & Zhou, Z. Towards practical lithium-metal anodes. Chem. Soc. Rev. 49, 3040–3071 (2020).
Zhang, Y. et al. Towards better Li metal anodes: challenges and strategies. Mater. Today 33, 56–74 (2020).
Cheng, X.-B. et al. A review of solid electrolyte interphases on lithium metal anode. Adv. Sci. 3, 1500213 (2016).
Tikekar, M. D., Choudhury, S., Tu, Z. & Archer, L. A. Design principles for electrolytes and interfaces for stable lithium-metal batteries. Nat. Energy 1, 16114 (2016).
Aurbach, D., Ein‐Ely, Y. & Zaban, A. The surface chemistry of lithium electrodes in alkyl carbonate solutions. J. Electrochem. Soc. 141, L1–L3 (1994).
Ue, M. & Uosaki, K. Recent progress in liquid electrolytes for lithium metal batteries. Curr. Opin. Electrochem. 17, 106–113 (2019).
Yu, Z. et al. Molecular design for electrolyte solvents enabling energy-dense and long-cycling lithium metal batteries. Nat. Energy 5, 526–533 (2020).
Suo, L., Hu, Y.-S., Li, H., Armand, M. & Chen, L. A new class of solvent-in-salt electrolyte for high-energy rechargeable metallic lithium batteries. Nat. Commun. 4, 1481 (2013).
Gao, X., Chen, Y., Johnson, L. & Bruce, P. G. Promoting solution phase discharge in Li–O2 batteries containing weakly solvating electrolyte solutions. Nat. Mater. 15, 882–888 (2016).
Jiao, S. et al. Stable cycling of high-voltage lithium metal batteries in ether electrolytes. Nat. Energy 3, 739–746 (2018).
Fan, X. et al. Highly fluorinated interphases enable high-voltage Li-metal batteries. Chem 4, 174–185 (2018).
Chen, M. et al. Marrying ester group with lithium salt: cellulose-acetate-enabled LiF-enriched interface for stable lithium metal anodes. Adv. Funct. Mater. 31, 2102228 (2021).
Ko, J. & Yoon, Y. S. Recent progress in LiF materials for safe lithium metal anode of rechargeable batteries: is LiF the key to commercializing Li metal batteries? Ceram. Int. 45, 30–49 (2019).
Mozhzhukhina, N. & Calvo, E. J. Perspective—the correct assessment of standard potentials of reference electrodes in non-aqueous solution. J. Electrochem. Soc. 164, A2295–A2297 (2017).
Kim, S. C. et al. Potentiometric measurement to probe solvation energy and its correlation to lithium battery cyclability. J. Am. Chem. Soc. 143, 10301–10308 (2021).
Seh, Z. W., Sun, J., Sun, Y. & Cui, Y. A highly reversible room-temperature sodium metal anode. ACS Cent. Sci. 1, 449–455 (2015).
Doi, K. et al. Reversible sodium metal electrodes: is fluorine an essential interphasial component? Angew. Chem. Int. Ed. 58, 8024–8028 (2019).
Yamada, Y. et al. Hydrate-melt electrolytes for high-energy-density aqueous batteries. Nat. Energy 1, 16129 (2016).
Ko, S. et al. Lithium-salt monohydrate melt: a stable electrolyte for aqueous lithium-ion batteries. Electrochem. Commun. 104, 106488 (2019).
Gagne, R. R., Koval, C. A. & Lisensky, G. C. Ferrocene as an internal standard for electrochemical measurements. Inorg. Chem. 19, 2854–2855 (1980).
Gritzner, G. & Kůta, J. Recommendations on reporting electrode potentials in nonaqueous solvents: IUPC commission on electrochemistry. Electrochim. Acta 29, 869–873 (1984).
Yamada, Y. et al. General observation of lithium intercalation into graphite in ethylene-carbonate-free superconcentrated electrolytes. ACS Appl. Mater. Interfaces 6, 10892–10899 (2014).
Han, S.-D., Borodin, O., Seo, D. M., Zhou, Z.-B. & Henderson, W. A. Electrolyte solvation and ionic association. J. Electrochem. Soc. 161, A2042–A2053 (2014).
Zhang, C. et al. Chelate effects in glyme/lithium bis(trifluoromethanesulfonyl)amide solvate ionic liquids. I. Stability of solvate cations and correlation with electrolyte properties. J. Phys. Chem. B 118, 5144–5153 (2014).
Wiberg, K. B. & Murcko, M. A. Rotational barriers. 4. Dimethoxymethane. The anomeric effect revisited. J. Am. Chem. Soc. 111, 4821–4828 (1989).
Tvaroška, I. & Bleha, T. Lone pair interactions in dimethoxymethane and anomeric effect. Can. J. Chem. 57, 424–435 (1979).
Zhang, Z. et al. Capturing the swelling of solid–electrolyte interphase in lithium metal batteries. Science 375, 66–70 (2022).
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).
Nishihara, S. & Otani, M. Hybrid solvation models for bulk, interface, and membrane: reference interaction site methods coupled with density functional theory. Phys. Rev. B 96, 2–3 (2017).
Giannozzi, P. et al. QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Phys. Condens. Matter 21, 395502 (2009).
Spiegelman, C. H., Bennett, J. F., Vannucci, M., McShane, M. J. & Coté, G. L. A transparent tool for seemingly difficult calibrations: the parallel calibration method. Anal. Chem. 72, 135–140 (2000).
Marini, F., Roncaglioni, A. & Novič, M. Variable selection and interpretation in structure—affinity correlation modeling of estrogen receptor binders. J. Chem. Inf. Model. 45, 1507–1519 (2005).
Jalem, R., Aoyama, T., Nakayama, M. & Nogami, M. Multivariate method-assisted ab initio study of olivine-type LiMXO4 (main group M2+–X5+ and M3+–X4+) compositions as potential solid electrolytes. Chem. Mater. 24, 1357–1364 (2012).
Acknowledgements
This work was supported by the Advanced Low Carbon Technology Research and Development Program (ALCA), Specially Promoted Research for Innovative Next Generation Batteries (SPRING) of the Japan Science and Technology Agency (JST) (JPMJAL1301) to Y.Y.; JSPS KAKENHI Specially Promoted Research (number 15H05701) to A.Y.; and the Ministry of Education, Culture, Sports, Science and Technology (MEXT) Program: Data Creation and Utilization Type Materials Research and Development Project (JPMXP1121467561) to A.Y.
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Y.Y. and A.Y. conceived and directed the projects. T.O., S.K. and Y.Y. proposed the concepts of the electrolyte design and electrode potential control. A.Y., N.T. and M.N. proposed the strategy and direction of the machine learning approach. S.K. and T.O. performed the experiments and analysed the data. T.S., N.T. and M.N. performed the computational and machine learning analyses. S.K., N.T., M.N., Y.Y. and A.Y. wrote the manuscript.
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Nature Energy thanks Venkatasubramanian Viswanathan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Ko, S., Obukata, T., Shimada, T. et al. Electrode potential influences the reversibility of lithium-metal anodes. Nat Energy 7, 1217–1224 (2022). https://doi.org/10.1038/s41560-022-01144-0
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DOI: https://doi.org/10.1038/s41560-022-01144-0
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