Enabling Bio‐Cathode with Graphene Coating via Networking Soy‐Protein and Polydopamine for Li–S Batteries
Source abstract
Abstract
Abstract The abundance and environmental friendliness in nature of sulfur (S) make Li–S batteries more attractive in addition to the high theoretical capacity (1675 mAh g −1 ) and energy density (2600 Wh kg −1 ) of the batteries. In this study, a bio‐based S cathode with graphene (Gr) coating, capable of effectively suppressing the shuttle effect of polysulfides, is enabled via networking soy protein (SP) and polydopamine (PDA) to form a functional bio‐binder (SP‐PDA). Dopamine self‐polymerization in SP not only generates the interpenetrated network for the bio‐binder but also makes the denatured structure of SP with rich functional groups effective for trapping polysulfides. Meanwhile, the Gr coating with low impedance, and high electronic and ionic conductivity on the cathode surface further significantly reduces polysulfide dissolution. Consequently, the Li–S batteries with the bio‐cathode (SP‐PDA@Gr) demonstrate excellent rate performance and long cycling capacity. In specific, under the current density of 0.5 A g −1 at 70% (500 mAh g −1 ) capacity retention, the cycle life of the Li–S cell with SP‐PDA@Gr cathode is 600 cycles, i.e.,100 times longer than that of the cell with PVDF binder. This study provides a sustainable strategy for enhancing the performance of Li–S batteries through networking natural proteins to form functional bio‐binders.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advancements in Battery Materials · Advanced Battery Materials and Technologies · Extraction and Separation Processes
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical and Materials Engineering Washington State University Pullman WA 99164 USA
Funding
U.S. Department of Agriculture · National Science Foundation
References (71)
Show source-provided references
1. 10.1016/j.ensm.2020.06.04310.1016/j.ensm.2020.06.043 ↗
2. 10.1016/j.est.2023.10855910.1016/j.est.2023.108559 ↗
3. 10.1002/aenm.20170026010.1002/aenm.201700260 ↗
4. 10.1002/aenm.20200130410.1002/aenm.202001304 ↗
5. 10.1002/adma.20180653210.1002/adma.201806532 ↗
6. 10.1002/aenm.20200250810.1002/aenm.202002508 ↗
7. 10.1016/j.nanoen.2021.10592810.1016/j.nanoen.2021.105928 ↗
8. 10.1002/aenm.20180377410.1002/aenm.201803774 ↗
9. 10.1002/adma.20170044910.1002/adma.201700449 ↗
10. 10.1002/adma.20210594710.1002/adma.202105947 ↗
11. 10.1002/adfm.20200061310.1002/adfm.202000613 ↗
12. 10.1002/adma.20210517810.1002/adma.202105178 ↗
13. 10.1039/D2TA09266B10.1039/D2TA09266B ↗
14. 10.3390/nano1006122010.3390/nano10061220 ↗
15. 10.1039/C8NH00172C10.1039/C8NH00172C ↗
16. 10.1016/j.jpowsour.2024.23532210.1016/j.jpowsour.2024.235322 ↗
17. 10.1016/j.rser.2021.11178310.1016/j.rser.2021.111783 ↗
18. 10.1002/aenm.20200258010.1002/aenm.202002580 ↗
19. 10.1002/adfm.20210198010.1002/adfm.202101980 ↗
20. 10.1016/j.nantod.2015.02.00910.1016/j.nantod.2015.02.009 ↗
21. 10.1016/j.ensm.2020.11.02110.1016/j.ensm.2020.11.021 ↗
22. 10.1016/j.ensm.2023.10277610.1016/j.ensm.2023.102776 ↗
23. 10.1039/D4EE00791C10.1039/D4EE00791C ↗
24. 10.1002/adfm.20230710810.1002/adfm.202307108 ↗
25. 10.1002/jat.459010.1002/jat.4590 ↗
26. 10.1016/j.tiv.2024.10582510.1016/j.tiv.2024.105825 ↗
27. Risk Evaluation for n‐Methylpyrrolidone (NMP) 2022.
28. 10.1002/adfm.20190793110.1002/adfm.201907931 ↗
29. 10.1021/acsnano.2c0305910.1021/acsnano.2c03059 ↗
30. 10.1016/j.cej.2022.13634710.1016/j.cej.2022.136347 ↗
31. 10.1002/aenm.20170288910.1002/aenm.201702889 ↗
32. 10.1002/aenm.20190177410.1002/aenm.201901774 ↗
33. 10.1002/adma.20170182810.1002/adma.201701828 ↗
34. 10.1016/j.cej.2022.13528310.1016/j.cej.2022.135283 ↗
35. 10.1002/aenm.20190364210.1002/aenm.201903642 ↗
36. 10.1021/acsami.3c0690710.1021/acsami.3c06907 ↗
37. 10.1038/s41467-020-19246-210.1038/s41467-020-19246-2 ↗
38. 10.1016/j.ensm.2022.02.03110.1016/j.ensm.2022.02.031 ↗
39. 10.1021/acs.jpclett.8b0083610.1021/acs.jpclett.8b00836 ↗
40. 10.1016/j.ensm.2020.01.00710.1016/j.ensm.2020.01.007 ↗
41. 10.1002/adfm.20210485810.1002/adfm.202104858 ↗
42. 10.1021/acs.jpclett.6b0207110.1021/acs.jpclett.6b02071 ↗
43. 10.1021/acs.jpcc.5b0912610.1021/acs.jpcc.5b09126 ↗
44. 10.1007/s10853-018-2121-y10.1007/s10853-018-2121-y ↗
45. 10.1016/j.cej.2024.15273910.1016/j.cej.2024.152739 ↗
46. 10.3390/molecules2605136310.3390/molecules26051363 ↗
47. 10.1002/anie.20160451910.1002/anie.201604519 ↗
48. 10.1016/j.actbio.2021.10.01010.1016/j.actbio.2021.10.010 ↗
49. 10.1021/acsami.0c1891610.1021/acsami.0c18916 ↗
50. 10.1021/nn203436j10.1021/nn203436j ↗
51. 10.1021/nl403130h10.1021/nl403130h ↗
52. 10.1002/adma.20200366610.1002/adma.202003666 ↗
53. 10.1016/j.ensm.2021.05.03110.1016/j.ensm.2021.05.031 ↗
54. 10.1016/j.cej.2021.13430610.1016/j.cej.2021.134306 ↗
55. 10.1002/adfm.20220611310.1002/adfm.202206113 ↗
56. 10.1002/adma.20210081010.1002/adma.202100810 ↗
57. 10.1002/advs.20190316810.1002/advs.201903168 ↗
58. 10.1002/aenm.20230045210.1002/aenm.202300452 ↗
59. 10.1002/adfm.20190768010.1002/adfm.201907680 ↗
60. 10.1021/acsomega.0c0066610.1021/acsomega.0c00666 ↗
61. 10.1016/j.electacta.2022.14090810.1016/j.electacta.2022.140908 ↗
62. 10.1016/j.electacta.2016.05.13010.1016/j.electacta.2016.05.130 ↗
63. 10.1039/C8TA11384J10.1039/C8TA11384J ↗
64. 10.1021/acs.jpcc.8b0937810.1021/acs.jpcc.8b09378 ↗
65. 10.1016/j.ensm.2018.11.00210.1016/j.ensm.2018.11.002 ↗
66. 10.1016/j.jechem.2020.01.03110.1016/j.jechem.2020.01.031 ↗
67. 10.1088/0953-8984/21/39/39550210.1088/0953-8984/21/39/395502 ↗
68. 10.1103/PhysRevLett.77.386510.1103/PhysRevLett.77.3865 ↗
69. 10.1038/s41524-018-0127-210.1038/s41524-018-0127-2 ↗
70. 10.1093/imamat/6.3.22210.1093/imamat/6.3.222 ↗
71. 10.1002/jcc.2049510.1002/jcc.20495 ↗