| Project/Area Number |
23KF0263
|
|---|
| Research Category |
Grant-in-Aid for JSPS Fellows
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 外国 |
|---|
| Review Section |
Basic Section 35030:Organic functional materials-related
|
|---|
| Research Institution | Kyushu University |
|---|
Principal Investigator |
安達 千波矢 九州大学, 工学研究院, 教授 (30283245)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
KIM HYUNG SUK 九州大学, 工学研究院, 外国人特別研究員
|
|---|
| Project Period (FY) |
2023-11-15 – 2025-03-31
|
| Project Status |
Discontinued (Fiscal Year 2024)
|
| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 2025: ¥300,000 (Direct Cost: ¥300,000)
Fiscal Year 2024: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2023: ¥800,000 (Direct Cost: ¥800,000)
|
|---|
| Keywords | Charge-transfer / TADF / COMPASS model / Spin-flip / Machine learning / QSPR / Multi-resonance TADF / Spin-flip process / IPN-derivative / OLED / RISC / COMPASS / Spin-orbit coupling |
|---|
| Outline of Research at the Start |
We will propose a comprehensive TADF model that incorporates a four-level electronic structure to fully account for the observed experimental responses in our study. This model will take into consideration the spin-flip process facilitated by an intermediate, high-lying T state. It will be constructed based on the principles of universal exciton dynamics using time-dependent population rate equations. This approach will provide us with a better understanding of the underlying spin-flip mechanisms.
|
| Outline of Annual Research Achievements |
Through the JSPS Postdoctoral Fellowships program, I collaborated with Professor Chihaya Adachi at Kyushu University, leading to the publication of 6 SCIE papers in 2024 to 2025. Among these, the research most closely aligned with the objectives of the JSPS program was published in Nat. Commun. In recognition of its significance, we were invited to contribute to the Behind the Paper section of Nature Publishing Group’s Research Communities, where we provided insights into our findings. As of September/October 2024, this highly cited work ranked in the top 1% of the Physics field based on citation thresholds for its publication year. Additionally, in 2025, we harnessed machine learning to design high-performance organic light-emitting materials, culminating in a publication in Sci. Adv..
|
