Effect of Properties of Silver Nanoparticles Coated with Polar Material and the Antibacterial Activity on Marine Pathogenic Bacteria
Bibliographic record
Abstract
Silver nanoparticles (Ag NPs) have become one of the current research hotspots and are used in many fields such as electrochemistry, energy, bioanalysis, and environmental monitoring, especially in the field of antibacterial research. In this study, we investigated the effect of properties of Ag NPs coated with polar materials. Ag NPs covered by a dispersant that was triethylene glycol monoethyl ether was stable and conquered the aggregation of Ag NPs. The effect of the dispersant on biocompatibility was explored through interaction experiments between Ag NPs and DNA sequence. The coated Ag NPs could adsorb DNA, and the fluorescence of FAM-DNA could be quenched by Ag NPs. The adsorption and desorption experiments of DNA showed that the order of DNA functional groups on the interaction process was phosphate>T>C>A>G. Moreover, we selected marine pathogenic bacteria to test the antibacterial effect of Ag NPs coated with a polar dispersant. The polar material had a certain inhibitory effect on the antibacterial activity of Ag NPs. However, small molecules such as bases could interact on the surface Ag NPs and release Ag+ to perform the antibacterial activity. The results could contribute to the further application of Ag NPs.
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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".