journal article·2024-11Open access

Exploring dopant‐enhanced ionic conductivity of AgCl‐doped Li7P3S11 solid electrolytes: Integrating synchrotron Rietveld analysis, DFT, and ANN‐based molecular dynamics approaches

Yong‐Seok Choi, Youngin Lee, Hyuna Ahn, Jiwon Jeong, Kyung Yoon Chung, David O. Scanlon, Jae‐Chul Lee

Carbon Energy· Volume 6· Issue 11

Abstract

Abstract The effectiveness of dual‐doping as a method of improving the conductivity of sulfide solid electrolytes (SEs) is not in doubt; however, the atomic‐level mechanisms underpinning these enhancements remain elusive. In this study, we investigate the atomic mechanisms associated with the high ionic conductivity of the Li7P3S11 (LPS) SE and its response to Ag/Cl dual dopants. Synthesis and electrochemical characterizations show that the 0.2 M AgCl‐doped LPS (Li6.8P3Ag0.1S10.9Cl0.1) exhibited an over 80% improvement in ionic conductivity compared with the undoped LPS. The atomic‐level structures responsible for the enhanced conductivity were generated by a set of experiment and simulation techniques: synchrotron X‐ray diffractometry, Rietveld refinement, density functional theory, and artificial neural network‐based molecular dynamics simulations. This thorough characterization highlights the role of dual dopants in altering the structure and ionic conductivity. We found that the PS4 and P2S7 structural motifs of LPS undergo transformation into various PSx substructures. These changes in the substructures, in conjunction with the paddle‐wheel effect, enable rapid Li migration. The dopant atoms serve to enhance the flexibility of PS4–P2S7 polyhedral frameworks, consequently enhancing the ionic conductivity. Our study elucidates a clear structure–conductivity relationship for the dual‐doped LPS, providing a fundamental guideline for the development of sulfide SEs with superior conductivity.
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Research context

OpenAlex topics: Advanced Battery Materials and Technologies · Advancements in Battery Materials · Thermal Expansion and Ionic Conductivity

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Materials Science and Engineering Dankook University Cheonan Republic of Korea

Department of Materials Science and Engineering Korea University Seoul Republic of Korea

Center for Energy Storage Research Korea Institute of Science and Technology Seoul Republic of Korea

The School of Chemistry University of Birmingham Edgbaston Birmingham UK

Institute of Green Manufacturing Research Korea University Seoul Republic of Korea

Funding

National Research Foundation of Korea · Engineering and Physical Sciences Research Council

References (37)

Show source-provided references
1. 10.1016/j.ensm.2018.02.02010.1016/j.ensm.2018.02.020 ↗
2. 10.1002/aenm.20180093310.1002/aenm.201800933 ↗
3. 10.1016/j.nanoen.2021.10590610.1016/j.nanoen.2021.105906 ↗
4. 10.1016/j.jallcom.2022.16612510.1016/j.jallcom.2022.166125 ↗
5. 10.1038/nenergy.2016.3010.1038/nenergy.2016.30 ↗
6. 10.1039/C7TA01147D10.1039/C7TA01147D ↗
7. 10.1016/j.nanoen.2021.10654210.1016/j.nanoen.2021.106542 ↗
8. 10.1021/acssuschemeng.0c0363410.1021/acssuschemeng.0c03634 ↗
9. 10.1021/acsami.9b0821810.1021/acsami.9b08218 ↗
10. 10.1016/j.actamat.2022.11810610.1016/j.actamat.2022.118106 ↗
11. 10.1021/ja311089510.1021/ja3110895 ↗
12. 10.1039/C3EE41655K10.1039/C3EE41655K ↗
13. 10.1021/acs.est.2c0044010.1021/acs.est.2c00440 ↗
14. 10.1021/acs.jpclett.1c0090110.1021/acs.jpclett.1c00901 ↗
15. 10.1016/j.jpowsour.2008.09.04410.1016/j.jpowsour.2008.09.044 ↗
16. 10.1016/j.ssi.2006.04.01710.1016/j.ssi.2006.04.017 ↗
17. 10.1021/acs.chemmater.8b0300010.1021/acs.chemmater.8b03000 ↗
18. 10.1038/s41467-022-29769-510.1038/s41467-022-29769-5 ↗
19. 10.1002/anie.19911547110.1002/anie.199115471 ↗
20. 10.1038/ncomms1589310.1038/ncomms15893 ↗
21. 10.1016/j.jechem.2022.01.01810.1016/j.jechem.2022.01.018 ↗
22. 10.1021/acsami.0c1168310.1021/acsami.0c11683 ↗
23. 10.1021/cm990545w10.1021/cm990545w ↗
24. 10.1039/C9CP05329H10.1039/C9CP05329H ↗
25. 10.1021/acs.chemmater.5b0098810.1021/acs.chemmater.5b00988 ↗
26. 10.1038/nmat436910.1038/nmat4369 ↗
27. 10.1038/s41578-021-00401-010.1038/s41578-021-00401-0 ↗
28. 10.1039/C8CP01968A10.1039/C8CP01968A ↗
29. 10.1002/aenm.20200033510.1002/aenm.202000335 ↗
30. 10.1039/C7TA06067J10.1039/C7TA06067J ↗
31. 10.1038/s41467-020-15245-510.1038/s41467-020-15245-5 ↗
32. 10.1016/j.matt.2020.04.02710.1016/j.matt.2020.04.027 ↗
33. 10.1002/aenm.20150223710.1002/aenm.201502237 ↗
34. 10.1039/C5EE02941D10.1039/C5EE02941D ↗
35. Accurate estimation of diffusion coefficients and their uncertainties from computer simulation
36. 10.1021/acs.chemmater.9b0025710.1021/acs.chemmater.9b00257 ↗
37. 10.1021/acs.chemmater.1c0234510.1021/acs.chemmater.1c02345 ↗