Thermally Assisted Fluorescent Polymers: Polycyclic Aromatic Materials for High Color Purity and White-Light Emission
Bibliographic record
Abstract
Thermally activated delayed fluorescence (TADF) sensitization of fluorescence is a promising strategy to improve the color purity and operational lifetime of conventional TADF organic light-emitting diodes (OLEDs). Here, we propose a new design strategy for TADF-sensitized fluorescence based on acrylic polymers with a pendant energy-harvesting host, a TADF sensitizer, and fluorescent emitter monomers. Fluorescent emitters were rationally designed from a series of homologous polycyclic aromatic amines, resulting in efficient and color-pure polymeric fluorophores capable of harvesting both singlet and triplet excitons. Macromolecular analogues of blue, green, and yellow fourth-generation OLED emissive layers were prepared in a facile manner by Cu(0) reversible deactivation radical polymerization, with emission quantum yields up to 0.83 in air and narrow emission bands with full width at half-maximum as low as 57 nm. White-light emission can easily be achieved by enforcing incomplete energy transfer between a deep blue TADF sensitizer and yellow fluorophore to yield a single white-emissive polymer with CIE coordinates (0.33, 0.39) and quantum yield 0.77. Energy transfer to the fluorescent emitters occurs at rates of 1–4 × 10 8 s –1, significantly faster than deactivation caused by internal conversion or intersystem crossing. Rapid energy transfer facilitates high triplet exciton utilization and efficient sensitized emission, even when TADF emitters with a low quantum yield are used as photosensitizers. Our results indicate that a broad library of untapped polymers exhibiting efficient TADF-sensitized fluorescence should be readily accessible from known TADF materials, including many monomers previously thought unsuitable for use in OLEDs.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".