High‐Efficiency Blue‐Emitting Multi‐Resonance TADF Organic Light‐Emitting Diodes Constructed on Spiro Quinolinoacridine/Fluorene‐diphenylphosphine Oxide Positional Isomers
Bibliographic record
Abstract
Abstract Improving the performance of blue‐emitting devices is one of the most important challenges in Organic Light‐Emitting Diodes (OLEDs). In recent years, Multi‐Resonance Thermally Activated Delayed Fluorescence (MR‐TADF) with a phosphorescent sensitization technique has shown its potential to enhance efficiency and mitigate efficiency roll‐off. It is reported herein the investigations on the design, the synthesis, and the study of quinolinoacridine‐based positional isomers (SQA‐1‐FPOPh2, SQA‐3‐FPOPh2, and SQA‐4‐FPOPh2) as host for blue phosphorescence‐sensitized MR‐TADF OLEDs. The molecular design strategy involves the spatial separation of the electron‐rich (quinolinoacridine) and electron‐poor (fluorene/diphenylphosphine oxide) fragments. The impact of the π‐conjugation disruption between the fluorene and the diphenylphosphine oxide on the electronic properties of the materials is discussed. These bipolar materials are successfully incorporated as hosts in blue MR‐TADF OLEDs employing different emitters BNCz or v‐DABNA and different phosphorescent sensitizers, FIrpic, CN‐Ir, or fac‐Ir(tpz)3. Remarkably, when used with the v‐DABNA: CN‐Ir combination (0.5:20 wt.% respectively), SQA‐4‐FPOPh2 presents excellent electroluminescent performances. These devices possess a narrowband emission peak at 472 nm, a full width at half‐maximum of 21 nm, and a high maximum external quantum efficiency of 22.9%. The excellent molecular orientation, which maximizes the light outcoupling efficiency (ηout), is proposed to be the origin of this high performance.
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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".