Carbon quantum dots/Bi <sub>4</sub> O <sub>5</sub> Br <sub>2</sub> photocatalyst with enhanced photodynamic therapy: killing of lung cancer (A549) cells in vitro
Bibliographic record
Abstract
Abstract Inorganic photocatalysts have been regarded as a promising candidate in the domain of tumor photodynamic therapy (PDT) due to their inspirational photocatalytic activity. In this study, a Bi 4 O 5 Br 2 photocatalyst was synthesized and it exhibited effective photo‐killing activity of A549 cells (a human lung carcinoma epithelial cell line) in vitro. On this basis, we modified Bi 4 O 5 Br 2 with carbon quantum dots (CQDs) via a hydrolysis method at room temperature, which resulted in an improved photo‐killing effect of Bi 4 O 5 Br 2 to A549 cells. The samples and the interaction between samples and cells were fully characterized. It has been found that the loading of CQDs on Bi 4 O 5 Br 2 can reduce the hydration ratio, increase the cellular uptake and improve the photogenerated reactive oxygen species (ROS) as compared with pristine Bi 4 O 5 Br 2 . Electron spin resonance (ESR) analysis and radical‐trapping experiments manifested that the ROS contributed to PDT may be ·O 2 − and ·OH. This study may provide a useful strategy to ameliorate the penetrability, cell compatibility and PDT effect upon cancer cells of other inorganic photocatalysts.
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".