Efficient Exploration of Highly Conductive Pyrrolidinium-Based Ionic Plastic Crystals Using Materials Informatics
Source abstract
Abstract
High Resolution Image Download MS PowerPoint Slide Organic ionic plastic crystals (OIPCs), which are soft crystals with plasticity and ionic conductivity, are expected to be applied as solid electrolytes in battery applications. Further improvement of ionic conductivity is necessary for practical use as an electrolyte for energy storage devices. Materials Informatics (MI) is a method of incorporating information science in materials development. In this research, MI is being used to develop OIPCs with high ionic conductivity. By using informatics science in addition to chemical knowledge, this research can be carried out efficiently and innovatively. The synthesis of eight new compounds resulted in six of them being solid at room temperature, while two of them were in a liquid state, namely, ionic liquids. We evaluated the phase transition temperatures and ionic conductivity for each compound. Notably, N -ethyl- N -methylpyrrolidinium trifluoro(trifluoromethyl)borate ([C 2 mpyr][CF 3 BF 3 ]) exhibited a high ionic conductivity of 1.75 × 10 –4 S cm –1 at 25°C, which is one of the highest values reported among OIPCs to date. The combination of an experimental and MI based approach revealed an improved understanding of the relationship between ion size and ionic conductivity for a series of pyrrolidinium-based OIPCs, and it is expected that further improvements to this approach will yield greater understanding of structure–property relationships.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ionic liquids properties and applications · Advanced Battery Materials and Technologies · Conducting polymers and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Materials and Life Sciences, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554, Japan
Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 2-12-1 Oookayama, Meguro-ku, Tokyo 152-8550, Japan
Funding
Japan Society for the Promotion of Science · Japan Science and Technology Agency · Sophia University
References (62)
Show source-provided references
1. 10.1016/j.jechem.2020.10.01710.1016/j.jechem.2020.10.017 ↗
2. 10.1016/j.joule.2018.07.00910.1016/j.joule.2018.07.009 ↗
3. 10.1016/j.progpolymsci.2017.12.00410.1016/j.progpolymsci.2017.12.004 ↗
4. 10.1038/s41560-020-0565-110.1038/s41560-020-0565-1 ↗
5. 10.1039/C5TA03471J10.1039/C5TA03471J ↗
6. 10.1016/j.jpowsour.2010.11.07010.1016/j.jpowsour.2010.11.070 ↗
7. 10.1002/(SICI)1521-4095(199804)10:6<439::AID-ADMA439>3.0.CO;2-I10.1002/(SICI)1521-4095(199804)10:6<439::AID-ADMA439>3.0.CO;2-I ↗
8. 10.1021/acs.jpcc.6b0327710.1021/acs.jpcc.6b03277 ↗
9. 10.1039/C3CC49588D10.1039/C3CC49588D ↗
10. 10.1039/D1TA10966A10.1039/D1TA10966A ↗
11. 10.3390/nano1008160610.3390/nano10081606 ↗
12. 10.1111/j.1151-2916.2001.tb00685.x10.1111/j.1151-2916.2001.tb00685.x ↗
13. 10.1039/D3QM00315A10.1039/D3QM00315A ↗
14. 10.1021/acs.chemmater.5b0294410.1021/acs.chemmater.5b02944 ↗
15. 10.1039/c2jm15345a10.1039/c2jm15345a ↗
16. 10.1039/D3YA00078H10.1039/D3YA00078H ↗
17. 10.1039/C2CP43267F10.1039/C2CP43267F ↗
18. 10.1016/j.trechm.2019.01.00210.1016/j.trechm.2019.01.002 ↗
19. 10.1021/acs.jpcc.6b0827410.1021/acs.jpcc.6b08274 ↗
20. 10.1021/acs.jpclett.1c0294310.1021/acs.jpclett.1c02943 ↗
21. 10.1039/b920406g10.1039/b920406g ↗
22. 10.1021/acs.jpcb.7b0278010.1021/acs.jpcb.7b02780 ↗
23. 10.1016/0022-3697(61)90076-210.1016/0022-3697(61)90076-2 ↗
24. 10.1039/D1EE02567H10.1039/D1EE02567H ↗
25. 10.1039/c1jm10417a10.1039/c1jm10417a ↗
26. 10.1002/batt.20200004010.1002/batt.202000040 ↗
27. 10.1016/j.electacta.2019.02.07610.1016/j.electacta.2019.02.076 ↗
28. 10.1016/j.mtphys.2022.10060310.1016/j.mtphys.2022.100603 ↗
29. 10.1002/bkcs.1271510.1002/bkcs.12715 ↗
30. 10.1039/C8QM00016F10.1039/C8QM00016F ↗
31. 10.1039/C8NJ05127E10.1039/C8NJ05127E ↗
32. Rajan K. · 2013 · 1
33. 10.1039/D0TB00896F10.1039/D0TB00896F ↗
34. 10.1038/s41586-019-1923-710.1038/s41586-019-1923-7 ↗
35. 10.1093/bioinformatics/btz42210.1093/bioinformatics/btz422 ↗
36. 10.1021/acs.jpcc.8b0291310.1021/acs.jpcc.8b02913 ↗
37. 10.1021/acspolymersau.3c0002510.1021/acspolymersau.3c00025 ↗
38. 10.1021/jacs.9b1144210.1021/jacs.9b11442 ↗
39. 10.1246/cl.14083310.1246/cl.140833 ↗
40. 10.1016/j.elecom.2006.01.00810.1016/j.elecom.2006.01.008 ↗
41. 10.1021/acs.inorgchem.2c0386510.1021/acs.inorgchem.2c03865 ↗
42. 10.1021/cm000625w10.1021/cm000625w ↗
43. Li, Y.; Tarlow, D.; Brockschmidt, M.; Zemel, R. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 , 2015.
44. 10.1039/C9TA11175A10.1039/C9TA11175A ↗
45. 10.1002/celc.20200077210.1002/celc.202000772 ↗
46. 10.1039/D0CP03046E10.1039/D0CP03046E ↗
47. 10.1149/1945-7111/ab76a210.1149/1945-7111/ab76a2 ↗
48. 10.1039/D0TA03502E10.1039/D0TA03502E ↗
49. 10.1038/4551410.1038/45514 ↗
50. 10.1021/cg201592u10.1021/cg201592u ↗
51. 10.1246/cl.2009.78210.1246/cl.2009.782 ↗
52. 10.1002/1521-4095(200107)13:12/13<957::AID-ADMA957>3.0.CO;2-#10.1002/1521-4095(200107)13:12/13<957::AID-ADMA957>3.0.CO;2-# ↗
53. 10.3389/fenrg.2022.85409010.3389/fenrg.2022.854090 ↗
54. 10.1016/j.jpowsour.2006.02.07710.1016/j.jpowsour.2006.02.077 ↗
55. 10.1002/chem.20040053310.1002/chem.200400533 ↗
56. 10.1039/c2cp43066e10.1039/c2cp43066e ↗
57. 10.1039/b911568d10.1039/b911568d ↗
58. 10.1021/jp107740p10.1021/jp107740p ↗
59. 10.1039/b809309a10.1039/b809309a ↗
60. 10.1007/s10953-018-0814-010.1007/s10953-018-0814-0 ↗
61. 10.1021/jp805419f10.1021/jp805419f ↗
62. Atkins P. · 2010