Antibacterial and Antibiofilm Activity of Chemically and Biologically Synthesized Silver Nanoparticles
Bibliographic record
Abstract
Bacterial biofilms are a significant problem in the food industry, as they are difficult to eradicate and represent a threat to consumer health. Currently, nanoparticles as an alternative to traditional chemical disinfectants have garnered much attention due to their broad-spectrum antibacterial activity and low toxicity. In this study, silver nanoparticles (AgNPs) were synthesized by a biological method using a Jacaranda mimosifolia flower aqueous extract and by a chemical method, and the factors affecting both syntheses were optimized. The nanoparticles were characterized by Ultraviolet–visible (UV–Vis) spectrophotometry, Fourier-transform infrared spectroscopy (FTIR), Dynamic light scattering (DLS), X-ray diffraction (XRD), and Transmission electron microscopy (TEM) with a spherical and uniform shape. The antibacterial and antibiofilm formation activity was carried out on bacterial species of Pseudomonas aeruginosa and Staphylococcus aureus with the capacity to form biofilm. The minimum inhibitory concentration was 117.5 μg/mL for the chemical and 5.3 μg/mL for the biological nanoparticles. Both types of nanoparticles showed antibiofilm activity in the qualitative Congo red test and in the quantitative microplate test. Antibiofilm activity tests on fresh lettuce showed that biological nanoparticles decreased the population of S. aureus and P. aeruginosa by 0.63 and 2.38 logarithms, respectively, while chemical nanoparticles had little microbial reduction. In conclusion, the biologically synthesized nanoparticles showed greater antibiofilm activity. Therefore, these results suggest their potential application in the formulation of sanitizing products for the food and healthcare industries.
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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".