Systematic control of silver nanoparticle size and shape for enhanced biocompatibility and antibacterial activity against <i>Staphylococcus aureus</i> and <i>Escherichia coli</i>
Bibliographic record
Abstract
Abstract The antimicrobial activity of silver nanoparticles (AgNPs) is strongly influenced by their size and shape. This study investigates the impact of synthesis conditions on the morphology and antibacterial properties of AgNPs against Staphylococcus aureus and Escherichia coli. Six synthesis routes were tested using sodium borohydride, ascorbic acid, and sodium citrate as reducing agents, yielding nanoparticles with diverse structures, including spherical, ellipsoidal, truncated cubes, polyhedral, and elongated bars. Morphological differences were confirmed by TEM and UV-Vis spectroscopy. The overall results showed that quasi-spherical AgNPs synthesized using sodium borohydride at low temperatures and ascorbic acid at room temperature yielded the best MIC and % hemolysis, 2.75 E−3 ng/ml and 5.49 E−4 ng/ml, respectively, likely due to greater surface reactivity. Hemolysis assays suggested that nanoparticles produced at lower temperatures exhibited reduced cytotoxicity. These results highlight the importance of controlling synthesis parameters to optimize the antimicrobial effectiveness and biocompatibility of AgNPs for biomedical applications, particularly against bacteria that have a negative impact on human health.
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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 | 0.001 |
| 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".