Structure Engineering of Acridine Donor to Optimize Color Purity of Blue Thermally Activated Delayed Fluorescence Emitters
Bibliographic record
Abstract
Abstract 9,9‐Dimethyl‐9,10‐dihydroacridine (DMAC) is one of the most widely used electron donor for constructing high‐performance thermally activated delayed fluorescence (TADF) emitters. However, DMAC‐based emitters often suffer from the imperfect color purity, particularly in blue emitters, due to its strong electron‐donating capability. To modulate donor strength, 2,7‐F‐Ph‐DMAC and 2,7‐CF3‐Ph‐DMAC were designed by introducing the electron‐withdrawing 2‐fluorophenyl and 2‐(trifluoromethyl)phenyl at the 2,7‐positions of DMAC. These donors were used, in combination with 2,4,6‐triphenyl‐1,3,5‐triazine (TRZ) acceptor, to develop novel TADF emitters 2,7‐F‐Ph‐DMAC‐TRZ and 2,7‐CF3‐Ph‐DMAC‐TRZ. Compared to the F‐ or CF3‐free reference emitter, both two emitters showed hypsochromic effect in fluorescence and comparable photoluminescence quantum yields without sacrificing the reverse intersystem crossing rate constants. In particular, 2,7‐CF3‐Ph‐DMAC‐TRZ based OLED exhibited a blue shift by up to 39 nm and significantly improved Commission International de l′Éclairage (CIE) coordinates from (0.36, 0.55) to (0.22, 0.41), while the external quantum efficiency kept stable at about 22.5 %. This donor engineering strategy should be valid for improving the color purity of large amount of acridine based TADF emitters. It can be predicted that pure blue TADF emitters should be feasible if these F‐ or CF3‐modifed acridine donors are combined with other weaker electron acceptors.
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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) | 0.001 | 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 | 0.001 |
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".