Water-Dispersible Glassy Organic Dots Exhibiting Near-Infrared Delayed Emission for Bioimaging
Bibliographic record
Abstract
Red to near-infrared (NIR) emitting thermally activated delayed fluorescence (TADF) emitters and room temperature phosphorescence (RTP) emitters are attractive for time-gated biological imaging. Luminophores that emit red to NIR light can operate within the biological transparency window (650–1350 nm), enabling deeper penetration into tissue while also being easier to distinguish from cellular autofluorescence. Herein, we report a TADF emitter NAI-Q-MeOTPA and an RTP emitter NAI-Py-MeOTPA which incorporate the strong electron-donating group bis(4-methoxyphenyl)amine and strong acceptor cores quinoxaline-naphthalimide and pyrido[2,3- b ]pyrazine-naphthalimide, respectively. Delayed emission was observed for both luminophores with emission maxima for NAI-Q-MeOTPA and NAI-Py-MeOTPA of 746 and 749 nm in toluene, respectively. When encapsulated into glassy organic dots (g-Odots), the luminophores still maintained NIR emission with significant delayed lifetimes. The NAI-Q-MeOTPA and NAI-Py-MeOTPA g-Odots were probed for bioimaging in HeLa cells in which g-Odot uptake was observed and demonstrated bioimaging capability in the 630–740 nm imaging window.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".