Computational Microscopy of Spatial Dopant Distribution in Conjugated Polymer
Source abstract
Abstract
Abstract Conjugated organic polymers are becoming increasingly important for numerous applications due to their tunable properties, which are often optimized through doping. This study employs molecular dynamics simulations to investigate the distribution of the molecular dopant Mo(tfd–COCF 3 ) 3 in the conjugated polymer p(g 4 2T–T), aiming to compare the theoretical findings with the experimental results of Persson et al., who used electron tomography to explore the dopant's 3D spatial distribution (Nanoscale, 2022, 14, 15404). Simulations reveal a random dopant distribution with no evidence of clustering, contrasting with Persson et al.’s observations of isolated molecules and small clusters. The discrepancy is suggested to be due to a possible difference in dopant density between the simulations and the experimental system, and potential explanations for this difference are proposed. The study highlights the need for further experimental and theoretical efforts to resolve these apparent discrepancies in dopant distributions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Conducting polymers and applications · Organic Electronics and Photovoltaics · Analytical Chemistry and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratory of Organic Electronics Department of Science and Technology Linköping University Norrköping 60174 Sweden
Funding
Vetenskapsrådet · Knut och Alice Wallenbergs Stiftelse
References (44)
Show source-provided references
1. 10.1021/acs.chemrev.7b0048210.1021/acs.chemrev.7b00482 ↗
2. 10.1016/j.jiec.2023.05.01310.1016/j.jiec.2023.05.013 ↗
3. 10.3390/polym904015010.3390/polym9040150 ↗
4. 10.1002/adma.20170306310.1002/adma.201703063 ↗
5. 10.1021/acs.chemrev.1c0058110.1021/acs.chemrev.1c00581 ↗
6. 10.1002/marc.20230024610.1002/marc.202300246 ↗
7. 10.1038/nmat463410.1038/nmat4634 ↗
8. 10.1038/srep3050110.1038/srep30501 ↗
9. 10.1021/acs.jpclett.9b0207010.1021/acs.jpclett.9b02070 ↗
10. 10.1039/D2TC03927C10.1039/D2TC03927C ↗
11. 10.1039/C5TC04207K10.1039/C5TC04207K ↗
12. 10.1021/acs.macromol.9b0238910.1021/acs.macromol.9b02389 ↗
13. 10.1002/adma.20231406210.1002/adma.202314062 ↗
14. 10.1021/jacs.9b1276910.1021/jacs.9b12769 ↗
15. 10.1021/acsaelm.0c0083310.1021/acsaelm.0c00833 ↗
16. 10.1002/adfm.20210072310.1002/adfm.202100723 ↗
17. 10.1021/acs.macromol.4c0044610.1021/acs.macromol.4c00446 ↗
18. 10.1039/D1MH01079D10.1039/D1MH01079D ↗
19. 10.1002/adfm.20231007110.1002/adfm.202310071 ↗
20. 10.1103/PhysRevB.87.11520910.1103/PhysRevB.87.115209 ↗
21. 10.1103/PhysRevB.71.04521410.1103/PhysRevB.71.045214 ↗
22. 10.1021/acs.chemmater.6b0016510.1021/acs.chemmater.6b00165 ↗
23. 10.1103/PhysRevB.94.20520210.1103/PhysRevB.94.205202 ↗
24. 10.1002/marc.20170053310.1002/marc.201700533 ↗
25. 10.1038/natrevmats.2017.8610.1038/natrevmats.2017.86 ↗
26. 10.1126/science.aaw929510.1126/science.aaw9295 ↗
27. 10.1002/adma.20080073510.1002/adma.200800735 ↗
28. 10.1021/acs.accounts.5b0043810.1021/acs.accounts.5b00438 ↗
29. 10.1038/s41467-023-36748-x10.1038/s41467-023-36748-x ↗
30. 10.1016/j.joule.2023.09.00210.1016/j.joule.2023.09.002 ↗
31. 10.1038/s41563-018-0263-610.1038/s41563-018-0263-6 ↗
32. 10.1039/C5CP05266A10.1039/C5CP05266A ↗
33. 10.1039/D2NR03554E10.1039/D2NR03554E ↗
34. 10.1002/adma.20200863510.1002/adma.202008635 ↗
35. 10.1021/acs.macromol.1c0044410.1021/acs.macromol.1c00444 ↗
36. 10.1021/acs.macromol.1c0072310.1021/acs.macromol.1c00723 ↗
37. 10.1039/c3sm51906f10.1039/c3sm51906f ↗
38. 10.1088/2632-2153/ab983b10.1088/2632-2153/ab983b ↗
39. 10.1039/D2TC03158B10.1039/D2TC03158B ↗
40. 10.1002/advs.20190114410.1002/advs.201901144 ↗
41. 10.1002/jcc.2122410.1002/jcc.21224 ↗
42. 10.1021/acs.chemmater.2c0104010.1021/acs.chemmater.2c01040 ↗
43. 10.1063/1.44733410.1063/1.447334 ↗
44. 10.1021/acs.chemmater.3c0087310.1021/acs.chemmater.3c00873 ↗