journal article·2017Open access

Sensitivity and Limitations of Structures from X-ray and Neutron-Based Diffraction Analyses of Transition Metal Oxide Lithium-Battery Electrodes

Hao Liu, Haodong Liu, Saul H. Lapidus, Y. Shirley Meng, Peter J. Chupas, Karena W. Chapman

Abstract

Lithium transition metal oxides are an important class of electrode materials for lithium-ion batteries. Binary or ternary (transition) metal doping brings about new opportunities to improve the electrode's performance and often leads to more complex stoichiometries and atomic structures than the archetypal LiCoO 2 . Rietveld structural analyses of X-ray and neutron diffraction data is a widely-used approach for structural characterization of crystalline materials. However, different structural models and refinement approaches can lead to differing results, and some parameters can be difficult to quantify due to the inherent limitations of the data. Here, through the example of LiNi 0.8 Co 0.15 Al 0.05 O 2 (NCA), we demonstrated the sensitivity of various structural parameters in Rietveld structural analysis to different refinement approaches and structural models, and proposed an approach to reduce refinement uncertainties due to the inexact X-ray scattering factors of the constituent atoms within the lattice. This refinement approach was implemented for electrochemically-cycled NCA samples and yielded accurate structural parameters using only X-ray diffraction data. The present work provides the best practices for performing structural refinement of lithium transition metal oxides.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: X-ray Diffraction in Crystallography · Advancements in Battery Materials · Machine Learning in Materials Science

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

References (31)

Show source-provided references
1. 10.1016/0025-5408(80)90012-410.1016/0025-5408(80)90012-4 ↗
2. 10.1016/0025-5408(83)90138-110.1016/0025-5408(83)90138-1 ↗
3. 10.1016/S0378-7753(00)00407-910.1016/S0378-7753(00)00407-9 ↗
4. 10.1039/b702425h10.1039/b702425h ↗
5. 10.1021/cm702241a10.1021/cm702241a ↗
6. 10.1016/0167-2738(90)90049-W10.1016/0167-2738(90)90049-W ↗
7. 10.1021/ja050697u10.1021/ja050697u ↗
8. 10.1021/cm051176910.1021/cm0511769 ↗
9. 10.1016/j.ssi.2004.07.06610.1016/j.ssi.2004.07.066 ↗
10. 10.1016/j.jallcom.2004.06.05010.1016/j.jallcom.2004.06.050 ↗
11. 10.1038/359519a010.1038/359519a0 ↗
12. 10.1016/j.jpowsour.2003.10.00910.1016/j.jpowsour.2003.10.009 ↗
13. 10.1149/2.025111jes10.1149/2.025111jes ↗
14. 10.1039/C6TA01251E10.1039/C6TA01251E ↗
15. An K. , in “ORNL Report”. Oak Ridge National Laboratory, 2012.
16. Coelho A. , Coelho Software, Brisbane, Australia, 2012.
17. Larson A. C. V. D. , R.B., in “Los Alamos National Laboratory Report LAUR 86–784”, 2004.
18. 10.1107/S002188989800600110.1107/S0021889898006001 ↗
19. 10.1107/S010827018400283310.1107/S0108270184002833 ↗
20. 10.1107/s056773947900044910.1107/s0567739479000449 ↗
21. 10.2183/pjab.55.4310.2183/pjab.55.43 ↗
22. 10.1107/S056773948000190810.1107/S0567739480001908 ↗
23. 10.1016/s0167-2738(02)00697-510.1016/s0167-2738(02)00697-5 ↗
24. 10.1154/1.217980410.1154/1.2179804 ↗
25. 10.1038/nmat369910.1038/nmat3699 ↗
26. 10.1107/S002188989701091110.1107/S0021889897010911 ↗
27. 10.1039/c0jm04126b10.1039/c0jm04126b ↗
28. 10.1117/12.16472910.1117/12.164729 ↗
29. 10.1107/S002188980200577010.1107/S0021889802005770 ↗
30. 10.1107/S010876739401329210.1107/S0108767394013292 ↗
31. 10.1080/1044863920821877010.1080/10448639208218770 ↗