Highly Efficient Red Multi-Resonant Thermally Activated Delayed Fluorescence Emitters as Bioimaging Reagents
Bibliographic record
Abstract
Multi-resonant thermally activated delayed fluorescence (MR-TADF) emitters have attracted strong interest for organic electroluminescent devices due to their high photoluminescence quantum yield (ΦPL) and superior narrowband emission, resulting in high color purity output in the device. These properties are also crucial for high-performance biological probes, especially red emitters. Orange and red MR-TADF emitters, PhDPA-DiKTa and MeODPA-DiKTa, were designed by decorating the DiKTa core with di([1,1’-biphenyl]-4-yl)amine (PhDPA) and bis(4-methoxyphenyl)amine (MeODPA). Both compounds emit at long wavelengths, with PL of 592 nm (full-width at half-maximum, FWHM = 45 nm) for PhDPA-DiKTa and 633 nm (FWHM= 72 nm) for MeODPA-DiKTa in toluene. As 5 wt% doped films in mCP, PhDPA-DiKTa emits at PL of 617 nm, while MeODPA-DiKTa emits at PL of 655 nm. Both show delayed fluorescence, with delayed lifetimes, td, of 658.4 and 249.2 s, respectively. Water-dispersible glassy organic dots (g-Odots) based on these materials were prepared by encapsulating them and mCP host into an amphiphilic DSPE-PEG2k polymer. Both families of g-Odots showed a deeper red emission and enhanced ΦPL compared to the corresponding 5 wt% doped films in mCP (PL = 618 nm, PL = 77% for PhDPA-DiKTa g-Odots, PL = 663 nm, PL = 38% for MeODPA-DiKTa g-Odots). The TADF character of the emitters was conserved in the g-ODots, with d of 203.9 s for PhDPA-DiKTa g-Odots and 131.6 s for MeODPA-DiKTa g-Odots. These MR-TADF g-Odots were successfully demonstrated as biological imaging probes of HeLa cells.
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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 | 0.001 |
| 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".