journal article·2021-09-28Open access

Quantification of Efficiency in Lithium Metal Negative Electrodes via Operando X-ray Diffraction

Natalie R. Geise, Robert M. Kasse, Johanna Nelson Weker, Hans-Georg Steinrück, Michael F. Toney

Chemistry of Materials· Volume 33· Issue 18

Abstract

Lithium metal negative electrodes provide a pathway to high specific energy density electrochemical energy storage, particularly attractive for use in electric vehicles. One significant limitation to the implementation of Li negative electrodes is Coulombic inefficiency, namely the loss of capacity to irreversible processes. Multiple degradation pathways, which include electrolyte reduction, lithium corrosion, and the formation of dead lithium, contribute to these losses, but few methods of characterization can distinguish the various modes of degradation. Here, a method of quantifying the amount of lithium metal in the electrode via operando synchrotron X-ray diffraction (XRD) is developed. Through this method, how much lithium actively cycles and how much is lost to the three degradation mechanisms noted above can be differentiated. Additionally, the operando synchrotron XRD provides a unique view of lithium metal corrosion, whereby the corrosion current of electrodeposited lithium metal can be measured, which reveals that lithium corrodes when electrically connected to the copper current collector as well as in the form of dead Li metal. Observing the corrosion of both electrically connected and disconnected lithium provides new insights into corrosion mechanisms in lithium metal batteries. This approach addresses the lack of quantification methods for capacity losses and provides a more complete understanding of Li corrosion, both of which can aid in the design of long-lasting Li metal negative electrodes.
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Research context

OpenAlex topics: Advancements in Battery Materials · Advanced Battery Materials and Technologies · Advanced Battery Technologies Research

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

Affiliations

Department of Chemistry, Stanford University, Stanford, California 94305, United States

SLAC National Accelerator Laboratory, Stanford Synchrotron Radiation Lightsource, Menlo Park, California 94025, United States

Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States

Department Chemie, Universität Paderborn, Paderborn 33098, Germany

Department of Chemical and Biological Engineering, University of Colorado, Boulder, Boulder, Colorado 80303, United States

Funding

Basic Energy Sciences · Vehicle Technologies Program · Office of Naval Research · National Science Foundation

References (50)

Show source-provided references
1. 10.1126/science.aax070410.1126/science.aax0704 ↗
2. 10.1073/PNAS.182167211710.1073/PNAS.1821672117 ↗
3. 10.1038/s41560-019-0338-x10.1038/s41560-019-0338-x ↗
4. 10.1016/j.trechm.2019.02.01510.1016/j.trechm.2019.02.015 ↗
5. 10.1002/advs.20150021310.1002/advs.201500213 ↗
6. 10.1149/2.1441707jes10.1149/2.1441707jes ↗
7. 10.1021/cr500003w10.1021/cr500003w ↗
8. 10.1002/advs.20150021310.1002/advs.201500213 ↗
9. 10.1149/2.0231904jes10.1149/2.0231904jes ↗
10. 10.1021/acscentsci.6b0026010.1021/acscentsci.6b00260 ↗
11. 10.1039/C6EE01674J10.1039/C6EE01674J ↗
12. 10.1021/acsenergylett.9b0266810.1021/acsenergylett.9b02668 ↗
13. 10.1002/aenm.20200001710.1002/aenm.202000017 ↗
14. 10.1016/j.coelec.2018.10.01510.1016/j.coelec.2018.10.015 ↗
15. 10.1038/s41560-021-00787-910.1038/s41560-021-00787-9 ↗
16. 10.1016/0022-0728(93)80046-K10.1016/0022-0728(93)80046-K ↗
17. 10.1038/s41557-018-0203-810.1038/s41557-018-0203-8 ↗
18. 10.1039/c9cs00838a10.1039/c9cs00838a ↗
19. 10.1002/aenm.20200080410.1002/aenm.202000804 ↗
20. 10.1038/s41560-020-0648-z10.1038/s41560-020-0648-z ↗
21. 10.1002/aenm.20170209710.1002/aenm.201702097 ↗
22. 10.1149/2.0641814jes10.1149/2.0641814jes ↗
23. 10.1021/acsenergylett.0c0021510.1021/acsenergylett.0c00215 ↗
24. 10.1038/s41467-018-04394-310.1038/s41467-018-04394-3 ↗
25. 10.1002/anie.20190010510.1002/anie.201900105 ↗
26. 10.1021/acs.jpcc.5b0339610.1021/acs.jpcc.5b03396 ↗
27. 10.1038/nmat276410.1038/nmat2764 ↗
28. 10.1039/c4ee02730b10.1039/c4ee02730b ↗
29. 10.1021/acs.nanolett.5b0017510.1021/acs.nanolett.5b00175 ↗
30. 10.1038/s41586-019-1481-z10.1038/s41586-019-1481-z ↗
31. 10.1021/jacs.0c1025810.1021/jacs.0c10258 ↗
32. 10.1016/j.xcrp.2020.10013910.1016/j.xcrp.2020.100139 ↗
33. 10.1021/acsenergylett.8b0238110.1021/acsenergylett.8b02381 ↗
34. Warren B. E. · 1969
35. 10.1149/1.211412810.1149/1.2114128 ↗
36. 10.1149/1.211428810.1149/1.2114288 ↗
37. 10.1073/pnas.170822411410.1073/pnas.1708224114 ↗
38. 10.1021/jacs.9b1177410.1021/jacs.9b11774 ↗
39. 10.1038/s41598-017-03004-410.1038/s41598-017-03004-4 ↗
40. 10.1021/acs.chemrev.7b0000710.1021/acs.chemrev.7b00007 ↗
41. 10.1002/adma.20200340410.1002/adma.202003404 ↗
42. 10.1039/d0ta06020h10.1039/d0ta06020h ↗
43. 10.1063/1.514264310.1063/1.5142643 ↗
44. Perez N. · 200410.1007/b118420 ↗
45. 10.1149/2.1921712jes10.1149/2.1921712jes ↗
46. 10.1021/acsami.6b0302910.1021/acsami.6b03029 ↗
47. 10.1038/nmat304710.1038/nmat3047 ↗
48. 10.1107/S160057671500430610.1107/S1600576715004306 ↗
49. Newville, M.; Stensitzki, T.; Allen, D. B.; Ingargiola, A. lmFit: Non-Linear Least-Square Minimization and Curve-Fitting for Python. https://zenodo.org/record/11813 (accessed Apr 17, 2019).
50. Jekel, C. F.; Venter, G. pwlf: A Python Library for Fitting 1D Continuous Piecewise Linear Functions. https://github.com/cjekel/piecewise_linear_fit_py (accessed Mar 25, 2021).