Cosolvent-Modulated Donor Preaggregation Enhances Molecular Order in 20% Efficient Bilayer Organic Solar Cells
Source abstract
Abstract
Solvent selection critically modulates the aggregation behavior within the active layer, ultimately determining the performance of bilayer organic solar cells (OSCs). However, good solvents (e.g., chloroform and chlorobenzene) for polymer donors typically exhibit moderate boiling points, which suppress rapid aggregation, resulting in a smaller aggregate size and ultimately affecting charge transport properties. Herein, we demonstrate that trace DCM addition to CF modulates donor preaggregation, which enables precise fiber size control, increases the crystallinity, and enhances molecular ordering, thereby facilitating efficient charge transport. Therefore, bilayer OSCs processed with a cosolvent based on CF and DCM suppress bimolecular recombination, reduce trap density, extend carrier lifetime, and enhance carrier mobility, thereby improving charge extraction efficiency, achieving a notable 20.0% efficiency, and establishing it as one of the most efficient binary bilayer OSCs reported to date. The present strategy sheds light on manipulating donor preaggregation states to achieve superior molecular organization, laying the groundwork for advancing bilayer OSC performance.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Organic Electronics and Photovoltaics · Perovskite Materials and Applications · TiO2 Photocatalysis and Solar Cells
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center on Nanoenergy Research, Institute of Science and Technology for Carbon Peak & Neutrality, School of Physical Science & Technology
Guangxi University
Department of Chemical Engineering
Pohang University of Science and Technology
Department of Physics and Chemistry
Daegu Gyeongbuk Institute of Science and Technology (DGIST)
Funding
Natural Science Foundation of Guangxi Zhuang Autonomous Region · National Natural Science Foundation of China (NSFC)
References (61)
Show source-provided references
1. 10.1021/acsmaterialslett.3c0023610.1021/acsmaterialslett.3c00236 ↗
2. 10.1016/j.cej.2025.16724110.1016/j.cej.2025.167241 ↗
3. 10.1038/s41467-025-56225-x10.1038/s41467-025-56225-x ↗
4. 10.1038/s41563-025-02305-810.1038/s41563-025-02305-8 ↗
5. 10.1016/j.solener.2019.11.10110.1016/j.solener.2019.11.101 ↗
6. 10.1038/s41563-020-00835-x10.1038/s41563-020-00835-x ↗
7. 10.1038/s41467-023-41978-010.1038/s41467-023-41978-0 ↗
8. 10.1039/D4EE02330G10.1039/D4EE02330G ↗
9. 10.1039/D4EE05070C10.1039/D4EE05070C ↗
10. 10.1002/adfm.20250659310.1002/adfm.202506593 ↗
11. 10.1002/adfm.20252384810.1002/adfm.202523848 ↗
12. 10.1002/aenm.20210200010.1002/aenm.202102000 ↗
13. 10.1002/adma.20231353210.1002/adma.202313532 ↗
14. 10.1039/D0EE04012F10.1039/D0EE04012F ↗
15. 10.1038/s41560-024-01564-010.1038/s41560-024-01564-0 ↗
16. 10.1002/aenm.7081210.1002/aenm.70812 ↗
17. 10.1021/jacs.4c1526610.1021/jacs.4c15266 ↗
18. 10.1038/s41467-026-69051-610.1038/s41467-026-69051-6 ↗
19. 10.1002/adma.20230860810.1002/adma.202308608 ↗
20. 10.1021/ar900233v10.1021/ar900233v ↗
21. 10.1039/D4TC05207B10.1039/D4TC05207B ↗
22. 10.1039/D3EE00174A10.1039/D3EE00174A ↗
23. 10.1039/C7SE00601B10.1039/C7SE00601B ↗
24. 10.1002/aelm.20210074310.1002/aelm.202100743 ↗
25. 10.1021/acsmaterialslett.4c0218110.1021/acsmaterialslett.4c02181 ↗
26. 10.1038/s41467-020-14926-510.1038/s41467-020-14926-5 ↗
27. 10.1016/j.cej.2025.16731110.1016/j.cej.2025.167311 ↗
28. 10.1016/j.jechem.2025.07.02110.1016/j.jechem.2025.07.021 ↗
29. 10.1016/j.joule.2019.12.00410.1016/j.joule.2019.12.004 ↗
30. Kim, Y. Y.; Kim, J.; Kim, N. PLS-II 3C SAXS Beamline Data Plot Program with MATLAB ; Zenodo, 2022; pp 7042272. Version 0.1).
31. 10.1002/inf2.1253010.1002/inf2.12530 ↗
32. 10.1016/j.mtnano.2019.10003010.1016/j.mtnano.2019.100030 ↗
33. 10.1002/advs.20241305110.1002/advs.202413051 ↗
34. 10.1002/smll.20180087010.1002/smll.201800870 ↗
35. 10.1002/adfm.20230983210.1002/adfm.202309832 ↗
36. 10.1002/adma.20230200510.1002/adma.202302005 ↗
37. 10.1002/anie.20180629110.1002/anie.201806291 ↗
38. 10.1002/adma.20220821110.1002/adma.202208211 ↗
39. 10.1038/s41560-025-01862-110.1038/s41560-025-01862-1 ↗
40. 10.1002/adma.20170711410.1002/adma.201707114 ↗
41. 10.1002/adfm.20252169410.1002/adfm.202521694 ↗
42. 10.1016/j.jechem.2025.12.04210.1016/j.jechem.2025.12.042 ↗
43. 10.1016/j.mser.2025.10100710.1016/j.mser.2025.101007 ↗
44. 10.1002/adma.20242063110.1002/adma.202420631 ↗
45. 10.1002/cjoc.7045410.1002/cjoc.70454 ↗
46. 10.1002/adma.20251037810.1002/adma.202510378 ↗
47. 10.1103/PhysRevApplied.23.03400610.1103/PhysRevApplied.23.034006 ↗
48. 10.1002/anie.20251734110.1002/anie.202517341 ↗
49. 10.1002/aenm.20250494710.1002/aenm.202504947 ↗
50. 10.1016/j.mser.2026.10120510.1016/j.mser.2026.101205 ↗
51. 10.1021/acsami.3c0735010.1021/acsami.3c07350 ↗
52. 10.1016/j.joule.2023.12.01110.1016/j.joule.2023.12.011 ↗
53. 10.1039/D3CS00492A10.1039/D3CS00492A ↗
54. 10.1002/adma.20190830510.1002/adma.201908305 ↗
55. 10.1007/s40843-025-3366-910.1007/s40843-025-3366-9 ↗
56. 10.1038/s41467-026-68731-710.1038/s41467-026-68731-7 ↗
57. 10.1002/adfm.20190126910.1002/adfm.201901269 ↗
58. 10.1021/acsenergylett.2c0050210.1021/acsenergylett.2c00502 ↗
59. 10.1016/j.esci.2024.10030510.1016/j.esci.2024.100305 ↗
60. 10.1007/s40820-024-01576-110.1007/s40820-024-01576-1 ↗
61. 10.1039/D6EE01250G10.1039/D6EE01250G ↗