Polymer Dots with Delayed Fluorescence and Tunable Cellular Uptake for Photodynamic Therapy and Time‐Gated Imaging
Bibliographic record
Abstract
Abstract By combining bioimaging and photodynamic therapy (PDT), it is possible to treat cancer through a theranostic approach with targeted action for minimum invasiveness and side effects. Thermally activated delayed fluorescence (TADF) probes have gained recent interest in theranostics due to their ability to generate singlet oxygen (1O2) while providing delayed emission that can be used in time‐gated imaging. However, it is still challenging to design systems that simultaneously show (1) high contrast for imaging, (2) low dark toxicity but high phototoxicity and (3) tunable biological uptake. Here, we circumvent shortcomings of TADF systems by designing block copolymers and their corresponding semiconducting polymer dots (Pdots) that encapsulate a TADF dye in the core and expose an additional boron‐dipyrromethene (BODIPY) oxygen sensitizer in the corona. This architecture provides orange‐red luminescent particles (ΦPL up to 18 %) that can efficiently promote PDT (1O2 QY=42 %) of HeLa cells with very low photosensitizer loading (IC50 ~0.05–0.13 μg/mL after 30 min). Additionally, we design Pdots with tunable cellular uptake but similar PDT efficiencies using either polyethylene glycol or guanidinium‐based coronas. Finally, we demonstrate that these Pdots can be used for time‐gated imaging to effectively filter out background fluorescence from biological samples and improve image contrast.
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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".