Silver Nanoparticles Embedded in Natural Rubber Films: Synthesis, Characterization, and Evaluation of<i>In Vitro</i>Toxicity
Bibliographic record
Abstract
Natural rubber (NR) films can reduce silver metal ions forming embedded metal nanoparticles, a process that could be described as green synthesis. The NR films acting as a reactor generate and incorporate silver nanoparticles (AgNPs). Organic acids and amino acids play a crucial role in the formation of AgNPs. The plasmon extinction obtained in the UV-visible spectrum shows the presence of nanoparticles in the film after dipping the NR film into a solution of silver nitrate at 80°C. Electron microscopic analysis confirms the presence of AgNPs in the NR film and characterization by atomic force microscopy shows a change in the roughness of the NR film with AgNPs. In addition, our preliminary results from in vitro toxicity studies (MTT and comet assays) of the NR films and NR films with silver nanoparticles (NR/Ag) show that they are not toxic to cell lineage CHO-K1 (cells from the ovary of a Chinese hamster), an important result for potential medical applications.
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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".