Heptazine‐Based TADF Materials for Nanoparticle‐Based Nonlinear Optical Bioimaging
Bibliographic record
Abstract
Abstract s‐Heptazines are emerging as strong electron acceptors for efficient thermally activated delayed fluorescent (TADF) materials, yet the difficulties in synthesizing them have limited their practical use. Here, three novel s‐heptazine TADF materials with green to deep‐red emission (λmax = 525–664 nm) and high photoluminescence quantum yields, synthesized by either pseudoelectrophilic substitution or Negishi coupling routes, are described. These materials also demonstrate strong nonlinear optical absorption, with two‐photon cross sections up to 1260 GM. With deep‐red fluorescence, strong two‐photon absorption, high quantum yield, and delayed fluorescence, the emitter HAP‐3MeOTPA is ideally suited for use in nanoparticle‐based bioimaging experiments. The two kinds of luminescent nanoparticle are prepared, namely hostless, aggregate‐based organic dots (a‐Odots) and rigid, glassy Odots (g‐Odots) as biocompatible and water‐dispersible TADF probes. The g‐Odots are shown to retain the nonlinear optical properties, high photoluminescence quantum yield, and TADF observed in the constituent heptazine dye. These g‐Odots are then used as biological imaging probes with immortalized human kidney cancer (HEK293) cells, and single and multi‐photon‐excited microscopy coupled with time‐gated luminescence measurements are demonstrated. This work not only describes new routes to efficient heptazine‐based TADF materials, but also demonstrates their potential as nanoparticle‐based bioimaging probes combining several advanced optical functions.
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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".