Antibacterial efficacy of green-synthesized silver nanoparticles from rosemary, pennyroyal, and eucalyptus extracts against E. coli and S. aureus bacteria
Bibliographic record
Abstract
Bacteria, including those causing hospital-acquired infections, have become a significant concern for human health due to their resistance to common antibiotics. Silver nanoparticles possess highly antimicrobial properties and can be applied in various medical and healthcare contexts. The purpose of this research is to produce silver nanoparticles through a bio-based (green synthesis) method using extracts from the leaves of rosemary, pennyroyal, and eucalyptus plants and to investigate their antibacterial activity. Extracts from the leaves of rosemary, pennyroyal, and eucalyptus plants were prepared and added to a silver nitrate solution in the process of synthesizing silver nanoparticles. The production of silver nanoparticles in the solution was investigated by recording the color changes during the experiment and measuring the absorption levels across different wavelengths using a spectrophotometer. The antimicrobial effects exhibited by the silver nanoparticle solution were investigated and confirmed targeting both Staphylococcus aureus and Escherichia coli (E. coli) strains using the agar well diffusion method. Nanoparticles with diameters approximately ranging from 18 to 80 nanometers were successfully synthesized, exhibiting a varied assortment of spherical geometries and a notable purity level of 88% silver. Furthermore, nanoparticles synthesized from rosemary plant extract exhibited superior antibacterial properties compared to those from other plant extracts.
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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".