Development of silver nanoparticle loaded antibacterial polymer mesh using plasma polymerization process
Bibliographic record
Abstract
Plasma polymerized polyacrylic acid (PPAA) was deposited on a polymer substrate, namely polyethylene terephthalate (PET) mesh, for entrapment of silver nanoparticle (Ag-NP) in order to achieve antibacterial property to the material. Carboxylic groups of PPAA act as anchor as well as capping and stabilizing agents for Ag-NPs synthesized by chemical reduction method using NaBH(4) as a reducing agent. Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy (XPS), and water contact angle analysis were used to characterize the PPAA coatings. The Ag-NPs loaded polymer samples were characterized by UV-visible spectroscopy, field emission scanning electron microscopy, energy dispersive X-ray, and XPS techniques. XPS analysis showed ~1.0 at.% loading of Ag-NPs on to the PPAA-PET-mesh, which was composed of 79% zero-valent (Ag°) and 21% oxidized nano-Ag (Ag(+) ). The plasma processed PET meshes samples were tested for antibacterial activity against two bacterial strains, namely Staphylococcus aureus (Gram positive) and Escherichia coli (Gram negative). Qualitative and quantitative tests showed that silver containing PPAA-PET meshes exhibit excellent antibacterial property against the tested bacteria with percent reduction of bacterial concentration >99%, compared to the untreated PET mesh.
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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".