Biocompatible Water-Soluble Silicon Quantum Dots for Photodynamic Cancer Therapy
Bibliographic record
Abstract
This study reports the development of silicon quantum dots (SiQDs) designed as water-soluble and biocompatible materials for biomedical applications by functionalizing mixed surfaces of 10-Undecenoic acid (acid-SiQDs) and poly(ethylene oxide) (acid-PEO-SiQDs) through thermally induced hydrosilylation. The SiQDs exhibited exceptional biocompatibility with cell viability exceeding 95% and negligible toxicity at concentrations up to 500 μg/mL after 24 h of culture. In vitro photodynamic therapy (PDT) studies under low-level near-infrared (NIR) laser irradiation demonstrated significant therapeutic efficacy, reducing cancer cell viability to below 50% at concentrations of 250 μg/mL for acid-SiQDs and 50 μg/mL for acid-PEO-SiQDs after 10 min of irradiation. In vitro hemocompatibility of the SiQDs was investigated by measuring red blood cell hemolysis and aggregation, plasma coagulation, and platelet activation studies, which demonstrate that the surface modified SiQDs do not show adverse effects. Additionally, the SiQDs exhibited a red photoluminescent quantum yield exceeding 30%, further underscoring their structural stability and functional versatility. Collectively, these findings highlight the potential of acid-SiQDs and acid-PEO-SiQDs as safe, multifunctional platforms for enhancing cancer therapy through NIR irradiation while maintaining favorable blood compatibility, paving the way for their application in advanced biomedical technologies.
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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".