Fast Reverse Intersystem Crossing Over 107 s-1 in Organic Emitters with Inverted Singlet-Triplet Gap via Intramolecular Through Space Charge Transfer
Bibliographic record
Abstract
Controlling excited state properties to achieve fast reverse intersystem crossing rates of over 107 s-1 is still challenging for intramolecular through-space charge transfer (TSCT) based delayed fluorescent materials. To gain further insight into the relationship between through-space and through-bond charge transfer (TSCT/TBCT), herein, three compounds DPS-24Ac, DPS-25Ac and DPS-OAc were prepared and characterized via NMR, MS and single crystal, in which the diphenylsulfone (DPS) is used as the acceptor group and acridine (Ac) as the donor moiety. Intense emissions from blue to yellow with high emission efficiency of 70-100% are detected for all emitters. Both computations and experiments suggest that compounds DPS-24Ac and DPS-25Ac have a clear TSCT effect and also an inverted adiabatic singlet-triplet gap which can be explained by the kinetic exchange mechanism. Notably, compound DPS-24Ac achieves the highest reverse intersystem crossing rate constant (krISC) of over 107 s-1 via manipulation of both TSCT and TBCT effects. The solution-processed devices display maximum external quantum efficiencies of 21.73, 12.14 and 4.96% for DPS-24Ac, DPS-25Ac and DPS-OAc, respectively. Overall, this work provides a novel avenue to achieve highly-efficient OLED materials with fast rISC by controlling both TSCT and TBCT effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".