journal article·2025-09-10

Olefin-Linked Cationic Covalent Organic Frameworks as Host Materials for Lithium–Sulfur Batteries

Bertha Lotsi, Jocelyn Elgin, Adam Karp, Abigail M. Houser, Yiying Wu, Shiyu Zhang, Psaras L. McGrier

ACS Applied Materials & Interfaces· Volume 17· Issue 36
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Abstract

Lithium–sulfur batteries (LSBs) are an emerging energy storage system with high volumetric and gravimetric energy densities. However, a significant challenge is the shuttle effect caused by intermediate polysulfide species. The polysulfide species aids in reducing active material retention and degrading battery performance. Covalent organic frameworks (COFs), a crystalline class of porous polymers, have emerged as an excellent platform for constructing sulfur host materials that inhibit the shuttle effect due to their exceptional chemical stability and tunable molecular skeletons. In this study, three olefin-linked cationic COFs containing N -ethyl-2,4,6-trimethylpyridinium iodide (ETMPI), 1,3,5-tri(4-formylbiphenyl)benzene (TFBB), 4,4’-(10-(4′-formyl-[1,1′-biphenyl]-4-yl)-10H-phenothiazine-3,7-diyl)dibenzaldehyde (FPTZ), and [1,1′-biphenyl]-4,4′-dicarbaldehyde (BPDA) units were doped with polysulfide chains and used as hosts for LSBs. S@TFBB-ETMPI-COF exhibited a high initial capacity of 1219 mAh g –1 with an excellent capacity retention of 77% at 0.1C after 100 cycles. The excellent performance of S@TFBB-ETMPI-COF is attributed to a combination of cationic sites that can bind with lithium polysulfide to inhibit the shuttle effect, and the formation of a stable cathode electrolyte interphase (CEI) layer.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Battery Materials and Technologies · Advanced battery technologies research · Covalent Organic Framework Applications

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

Affiliations

Department of Chemistry and Biochemistry

The Ohio State University

References (62)

Show source-provided references
1. 10.1038/nature1147510.1038/nature11475 ↗
2. 10.1038/3510459910.1038/35104599 ↗
3. 10.1126/science.107235710.1126/science.1072357 ↗
4. 10.1016/j.est.2019.10104710.1016/j.est.2019.101047 ↗
5. 10.1021/cr500062v10.1021/cr500062v ↗
6. 10.1038/nmat319110.1038/nmat3191 ↗
7. 10.1016/j.jpowsour.2021.23094510.1016/j.jpowsour.2021.230945 ↗
8. 10.1134/S102319350805002910.1134/S1023193508050029 ↗
9. 10.1016/j.jpowsour.2014.04.01810.1016/j.jpowsour.2014.04.018 ↗
10. 10.1016/j.ensm.2015.09.00810.1016/j.ensm.2015.09.008 ↗
11. 10.1016/j.cej.2018.10.10410.1016/j.cej.2018.10.104 ↗
12. 10.1021/acsaem.8b0135010.1021/acsaem.8b01350 ↗
13. 10.1016/j.ensm.2016.04.00210.1016/j.ensm.2016.04.002 ↗
14. 10.1016/j.electacta.2012.03.08110.1016/j.electacta.2012.03.081 ↗
15. 10.1039/C7CS00139H10.1039/C7CS00139H ↗
16. 10.1039/b925751a10.1039/b925751a ↗
17. 10.1002/adma.20140511510.1002/adma.201405115 ↗
18. 10.1002/adma.20190112510.1002/adma.201901125 ↗
19. 10.1007/s40820-023-01223-110.1007/s40820-023-01223-1 ↗
20. 10.34133/energymatadv.001010.34133/energymatadv.0010 ↗
21. 10.1021/acsomega.8b0255110.1021/acsomega.8b02551 ↗
22. 10.1063/1.512219710.1063/1.5122197 ↗
23. 10.3390/ijms2408729110.3390/ijms24087291 ↗
24. 10.1002/cssc.20180218610.1002/cssc.201802186 ↗
25. 10.1149/2.149306jes10.1149/2.149306jes ↗
26. 10.1038/s41467-024-48160-010.1038/s41467-024-48160-0 ↗
27. 10.1021/jacs.2c0759610.1021/jacs.2c07596 ↗
28. 10.1002/aenm.20220354010.1002/aenm.202203540 ↗
29. 10.1021/jacs.2c0234610.1021/jacs.2c02346 ↗
30. 10.1038/s41467-023-38538-x10.1038/s41467-023-38538-x ↗
31. 10.1055/s-0041-172302010.1055/s-0041-1723020 ↗
32. 10.1039/C9CS00807A10.1039/C9CS00807A ↗
33. 10.1002/adma.20240539910.1002/adma.202405399 ↗
34. 10.1021/jacs.4c0570510.1021/jacs.4c05705 ↗
35. 10.1021/acsami.1c1087010.1021/acsami.1c10870 ↗
36. 10.1016/j.ensm.2023.10301410.1016/j.ensm.2023.103014 ↗
37. 10.1002/aenm.20240052110.1002/aenm.202400521 ↗
38. 10.1039/D4NR03204G10.1039/D4NR03204G ↗
39. 10.1021/acsmacrolett.4c0084710.1021/acsmacrolett.4c00847 ↗
40. 10.1021/acscatal.4c0273810.1021/acscatal.4c02738 ↗
41. 10.1021/acs.chemmater.3c0323010.1021/acs.chemmater.3c03230 ↗
42. 10.1021/acssuschemeng.1c0631810.1021/acssuschemeng.1c06318 ↗
43. 10.1021/acsami.2c1822610.1021/acsami.2c18226 ↗
44. 10.1002/adfm.20242169710.1002/adfm.202421697 ↗
45. 10.1021/acs.energyfuels.2c0103510.1021/acs.energyfuels.2c01035 ↗
46. 10.1016/j.jcis.2024.07.07610.1016/j.jcis.2024.07.076 ↗
47. 10.1039/C8TA07008C10.1039/C8TA07008C ↗
48. 10.1016/j.jcis.2021.02.01010.1016/j.jcis.2021.02.010 ↗
49. 10.1016/j.mtchem.2021.10066410.1016/j.mtchem.2021.100664 ↗
50. 10.1002/anie.20210437510.1002/anie.202104375 ↗
51. 10.1021/j100096a00110.1021/j100096a001 ↗
52. 10.1063/1.46491310.1063/1.464913 ↗
53. 10.1103/PhysRevB.37.78510.1103/PhysRevB.37.785 ↗
54. Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A. Gaussian 16. Revision C.01; Gaussian, Inc., 2019.
55. Ohio Supercomputer Center. Columbus, OH: Ohio Supercomputer Center, 1987.
56. 10.1039/D1PY00297J10.1039/D1PY00297J ↗
57. 10.1021/am302163p10.1021/am302163p ↗
58. 10.1021/acs.chemmater.3c0311910.1021/acs.chemmater.3c03119 ↗
59. 10.1038/s41560-020-0648-z10.1038/s41560-020-0648-z ↗
60. 10.1149/1945-7111/ab9ee810.1149/1945-7111/ab9ee8 ↗
61. 10.1021/acsenergylett.4c0321510.1021/acsenergylett.4c03215 ↗
62. 10.1002/anie.20230227610.1002/anie.202302276 ↗