Tunable Thermally Activated Delayed Fluorescence from Supramolecular Polymers Toward Application in Aqueous Media
Bibliographic record
Abstract
Thermally activated delayed fluorescence (TADF) offers great potential for application in light emitting devices and bioimaging. Supramolecular polymers can offer intriguing properties for the same applications, such as stimuli responsiveness and self-healing owing to their dynamic intermolecular interactions. However, merging the two has remained a formidable challenge, due to the nonplanar geometry of common TADF chromophores. Herein, we overcome this challenge by utilizing a less distorted multiple resonance TADF (MR-TADF) chromophore connected to a polymerization inducing building block. The obtained supramolecular synthon is capable of assembling in aliphatic solvents due to combined interchromophore interactions and hydrogen bonding. Within the supramolecular ensemble the long-lived photoluminescence properties of the chromophore are maintained. Further modification of the photoluminescence properties could be achieved by using different supramolecular modulators in a social self-sorting approach, allowing fine-tuning of the photoluminescence lifetime and bandwidth. Notably, the extent of intermolecular interactions can switch these assemblies from kinetically to thermodynamically controlled regimes. Finally, we employ this co-assembly strategy to move from organic to aqueous media highlighting the potential toward biological applications.
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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".