Development and Investigation of Ultrastable PbS/CdS/ZnS Quantum Dots for Near‐Infrared Tumor Imaging
Bibliographic record
Abstract
Achieving bright, reliable, robust, and stable probes for in vivo imaging is becoming extremely urgent for the cancer imaging research community. To date very few works have reported on elucidating in the varied and chemically complex biological milieu. The authors report detailed investigations of the synthesis of near‐infrared, water dispersive, strongly luminescent, and highly stable PbS/CdS/ZnS core/shell/shell quantum dots (QDs). These QDs are extremely stable, they could keep their initial morphology, dispersion status, and photoluminescence (PL) in phosphate buffered saline buffer for as long as 14 months. The QDs also show excellent photostability and could keep ≈80% of their initial PL intensity after 1 h continuous, strong UV illumination. More interestingly, they show negligible toxicity to cultured cells even at high QDs concentration. Given these outstanding properties, the QDs are explored for in vivo, tumor imaging in mice. With one order of magnitude lower QD concentration (0.04 mg mL –1 ), significantly weaker laser intensity (0.04 W cm –2 vs ≈1 W cm –2 ), and considerably shorter signal integration time (≤1 ms vs hundreds of ms) as compared to the best reported rare earth doped nanoparticles, the QDs show high emission intensity even at injection depth of ≈2.5 mm.
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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".