The Effects of Constriction Factor and Geometric Tortuosity on Li‐Ion Transport in Porous Solid‐State Li‐Ion Electrolytes
Source abstract
Abstract
Abstract 3D focused ion beam tomography is used to analyze the microstructures of Li‐ion conducting Li6.75La2.75Ca0.25Zr1.5Nb0.5O12 (LLCZN) garnet porous electrolytes with different levels of porosity and the theoretical effective bulk conductivities of the electrolyte are calculated based on LLCZN volume fraction, constriction factor, geometric tortuosity, and percolation factor. The experimentally measured effective bulk conductivities are consistently lower than the theoretical values when assuming constant bulk conductivity, suggesting the bulk conductivity of the LLCZN decreased with increasing porosity. This work highlights the importance of understanding the full effects of altering the microstructure of solid‐state electrolytes, as this will play a key role in advancing Li‐ion battery technology to higher energy and power densities.
Source: crossref · Quality: baseline
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 Materials Science and Engineering University of Maryland – College Park (UMD) 1206 Engineering Laboratory Building College Park MD 20742 USA
Ion Storage Systems 5000 College Avenue, Suite 3122 College Park MD 20740 USA
Funding
Advanced Research Projects Agency - Energy
References (67)
Show source-provided references
1. 10.1038/nmat166810.1038/nmat1668 ↗
2. 10.1149/1.279467210.1149/1.2794672 ↗
3. 10.1149/1.289506710.1149/1.2895067 ↗
4. 10.1016/j.ces.2009.05.03810.1016/j.ces.2009.05.038 ↗
5. 10.1016/j.ssi.2008.10.01710.1016/j.ssi.2008.10.017 ↗
6. 10.1016/j.jpowsour.2009.09.00510.1016/j.jpowsour.2009.09.005 ↗
7. 10.1111/j.1551-2916.2010.04111.x10.1111/j.1551-2916.2010.04111.x ↗
8. 10.1016/j.jpowsour.2010.08.01710.1016/j.jpowsour.2010.08.017 ↗
9. 10.1016/j.jpowsour.2013.01.06010.1016/j.jpowsour.2013.01.060 ↗
10. 10.1149/2.0031607jes10.1149/2.0031607jes ↗
11. 10.1149/1.185105510.1149/1.1851055 ↗
12. 10.1021/nn204476h10.1021/nn204476h ↗
13. 10.1038/nenergy.2015.410.1038/nenergy.2015.4 ↗
14. 10.1149/2.1111702jes10.1149/2.1111702jes ↗
15. 10.1016/j.electacta.2012.10.10410.1016/j.electacta.2012.10.104 ↗
16. 10.1016/j.commatsci.2015.06.02510.1016/j.commatsci.2015.06.025 ↗
17. 10.1016/j.jpowsour.2015.12.13410.1016/j.jpowsour.2015.12.134 ↗
18. 10.1016/j.elecom.2009.12.03810.1016/j.elecom.2009.12.038 ↗
19. 10.1016/j.elecom.2010.12.00410.1016/j.elecom.2010.12.004 ↗
20. 10.1016/j.jpowsour.2010.04.06610.1016/j.jpowsour.2010.04.066 ↗
21. 10.1126/science.124188210.1126/science.1241882 ↗
22. 10.1016/j.elecom.2012.12.02110.1016/j.elecom.2012.12.021 ↗
23. 10.1016/j.jpowsour.2015.02.08610.1016/j.jpowsour.2015.02.086 ↗
24. 10.1557/mrs2007.6410.1557/mrs2007.64 ↗
25. 10.1016/S1369-7021(07)70304-810.1016/S1369-7021(07)70304-8 ↗
26. Holzer L. · 2012 · 410
27. 10.1179/174328008X27780310.1179/174328008X277803 ↗
28. 10.1179/1743280413Y.000000002310.1179/1743280413Y.0000000023 ↗
29. 10.1146/annurev-matsci-070616-12395710.1146/annurev-matsci-070616-123957 ↗
30. 10.1016/j.eurpolymj.2005.09.01710.1016/j.eurpolymj.2005.09.017 ↗
31. 10.1002/anie.20070114410.1002/anie.200701144 ↗
32. 10.1016/j.jpowsour.2010.01.07610.1016/j.jpowsour.2010.01.076 ↗
33. 10.1016/j.jascer.2013.03.00510.1016/j.jascer.2013.03.005 ↗
34. 10.1039/c4cs00020j10.1039/c4cs00020j ↗
35. 10.1016/j.mattod.2018.04.00410.1016/j.mattod.2018.04.004 ↗
36. 10.1039/C7EE01004D10.1039/C7EE01004D ↗
37. 10.1073/pnas.171975811510.1073/pnas.1719758115 ↗
38. 10.1021/acs.nanolett.8b0129510.1021/acs.nanolett.8b01295 ↗
39. 10.1073/pnas.160042211310.1073/pnas.1600422113 ↗
40. 10.1016/j.mattod.2018.01.00110.1016/j.mattod.2018.01.001 ↗
41. 10.1002/adma.20170713210.1002/adma.201707132 ↗
42. 10.1016/0017-9310(74)90190-210.1016/0017-9310(74)90190-2 ↗
43. 10.1016/j.jpowsour.2013.05.04710.1016/j.jpowsour.2013.05.047 ↗
44. Tjaden B. · Int. Mater. Rev. · 2016 · 6608 · 1
45. 10.1016/j.jpowsour.2011.03.06010.1016/j.jpowsour.2011.03.060 ↗
46. 10.1002/aic.1516010.1002/aic.15160 ↗
47. 10.1038/nmeth.201910.1038/nmeth.2019 ↗
48. 10.1016/j.ultramic.2017.07.01710.1016/j.ultramic.2017.07.017 ↗
49. 10.1109/36.71886010.1109/36.718860 ↗
50. Buades A. · Comput. Vis. Pattern Recognit., 2005. CVPR 2005. IEEE Comput. Soc. Conf. · 2005 · 2 · 60
51. 10.1093/bioinformatics/btx18010.1093/bioinformatics/btx180 ↗
52. 10.1111/j.1551-2916.2008.02736.x10.1111/j.1551-2916.2008.02736.x ↗
53. 10.1016/S0008-8846(00)00370-710.1016/S0008-8846(00)00370-7 ↗
54. Meijster A. · 2002 · 33110.1007/0-306-47025-X_36 ↗
55. Garman R. M. · 1996
56. Rahaman M. N. · 2003
57. 10.1016/j.ces.2017.03.05110.1016/j.ces.2017.03.051 ↗
58. 10.1016/j.jpowsour.2013.04.15610.1016/j.jpowsour.2013.04.156 ↗
59. 10.1149/2.013405jes10.1149/2.013405jes ↗
60. 10.3724/SP.J.1077.2013.1342810.3724/SP.J.1077.2013.13428 ↗
61. 10.1002/aenm.20150009610.1002/aenm.201500096 ↗
62. 10.1021/acs.inorgchem.5b0189510.1021/acs.inorgchem.5b01895 ↗
63. 10.1016/j.ssi.2015.11.01410.1016/j.ssi.2015.11.014 ↗
64. 10.1016/j.ssi.2011.10.00310.1016/j.ssi.2011.10.003 ↗
65. 10.1016/j.ssi.2011.10.02210.1016/j.ssi.2011.10.022 ↗
66. 10.1039/c2jm31413d10.1039/c2jm31413d ↗
67. 10.1038/srep1805310.1038/srep18053 ↗