Exceptional antibacterial and cytotoxic potency of monodisperse greener AgNPs prepared under optimized pH and temperature
Bibliographic record
Abstract
In the present work, silver nanoparticles were prepared by using the extract of Camellia Sinensis. The extract contains phytochemicals which are mainly polyphenols acting as the natural reducing and stabilizing agents leading to the formation of uniformly dispersed and stabilized silver nanoparticles. The synthesis of silver nanoparticles was significantly influenced by the impact of the pH, as well as temperature conditions. It was found that at pH 5 and 25 °C, nanoparticles of different morphologies (spherical, polygonal, capsule) and sizes were formed. However, with the increase in temperature from 25 °C to 65 °C but at the same pH, these particles started attaining the spherical shape of different sizes owing to an increase in the reduction rate. Furthermore, for the reaction of the mixture at 65 °C, an increase in pH from 5 to 11 led to an increase in the monodispersity of spherically shaped nanoparticles, attributed to the hydroxide ions facilitated reduction. The prepared nanoparticles were investigated for their antibacterial activity using Nathan's Agar Well-Diffusion method. It was found that AgNPs prepared at pH 9 and 65 °C demonstrated strong antibacterial activity against gram-negative Escherichia coli in contrast to gram-positive Staphylococcus aureus. In reference to the cytotoxic potency, the prepared AgNPs showed clear cytotoxicity for HeLa cells and showcased a close relationship between activity and concentration as evidenced by the decrease in the percentage (100 to 30%) of metabolically active cells up to 25 µM-75 µM concentration of silver nanoparticles.
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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) | 0.000 | 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".