Synthesis of Silver Nanoparticles Using Azadirachta indica and Syzygium aromaticum Extract and Its Antibacterial Action Against Enterococcus faecalis: An In Vitro Study
Bibliographic record
Abstract
Introduction Nanotechnology is the study of manipulating matter at the atomic scale involving particles smaller than 100 nm. Silver nanoparticles (AgNPs) are gaining popularity across diverse sectors including medical, food, healthcare, consumer goods, and industrial fields due to their distinctive physical and chemical characteristics. The eco-friendly synthesis of AgNPs offers a straightforward, cost-effective, and environmentally benign method devoid of hazardous chemicals. Methodology Eighty milliliters (mL) of silver nitrate mixed with 20 mL of Azadirachta indica and Syzygium aromaticum plant extract underwent two days of magnetic stirring for AgNP synthesis. Characterization was done via ultraviolet-visible (UV-vis)-spectroscopy (300-700 nm), and antimicrobial properties, which were checked with Enterococcus faecalis, were assessed using the agar-well diffusion method. Results The change in color and peak observed in the UV-vis spectrum confirmed the successful synthesis of AgNPs. Both neem and clove extract-mediated synthesis of AgNPs exhibited antibacterial activity against E. faecalis. However, neem extract synthesized AgNPs displayed a larger inhibitory zone diameter and lower minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values compared to those synthesized using clove extract. Conclusion Incorporating neem and clove extracts in AgNP synthesis offers a practical, eco-friendly, and cost-efficient method with notable efficacy. These AgNPs exhibit antibacterial activity against E. faecalis, suggesting their viability as potent antibacterial agents for addressing oral pathogens. Their sustainable synthesis underscores a promising avenue for developing effective antimicrobial solutions in oral healthcare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".