Ulvan as a Reducing Agent for the Green Synthesis of Silver Nanoparticles: A Novel Mouthwash
Bibliographic record
Abstract
The antibacterial activity of an Ulvan-based silver nanoparticle (AgNP) system was evaluated in the current study. The green synthesis of biogenic silver nanoparticles was conducted using Ulvan, a sulphated polysaccharide extracted from Ulva lactuca. A novel mouthwash containing AgNPs was prepared, and tested for its efficacy and safety. AgNPs were confirmed with spectrophotometric analysis (UV–A visible spectrophotometer), and the characterisation was established with Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The AgNPs were spherical, and their average size was 8–33 nm, as shown via TEM. The antioxidant assay was conducted via DDPH assay, wherein the AgNPs, at a concentration of 50 μL/mL, showed 93.15% inhibition. Furthermore, anticancer activity was tested by evaluating the cell viability utilising the method of an MTT assay on the 3T3-L1 cell lines. AgNPs, at 30 µL/mL, showed maximal cell viability, denoting no cytotoxic effect. The silver-nanoparticle-based mouthrinse, at a concentration of 100 µL/mL, demonstrated antimicrobial activity against Streptococcus mutans, Staphylococcus aureus, Lactobacillus, and Candida albicans. This study shows that mouthwash prepared from the Ulvan-silver nanoparticle system could be a nontoxic and effective oral antimicrobial agent.
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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) | 0.001 | 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".