Synthesis of Antibacterial Coating Using Chitosan, Polyethylene Glycol and Silver Nanoparticles and Investigation of Their Antibacterial Properties
Bibliographic record
Abstract
Chitosan/ Polyethylene glycol and silver nanoparticles based antibacterial coating has been synthesized and applied to cotton cloth using sonochemical technique. In addition to the synthesis of nanoparticles using Pyrus seed extract (Green synthesis), Chitosan and PEG compound has also been used to develop the coating in this research. The coating obtained with this compound possesses good antibacterial properties and results. The study shows that the coated fabrics and silver nanoparticles show highly potent antibacterial activity towards gram negative and gram-positive bacteria. A comparison of coating with single and multiple components is studied; specifically, a comparison of pure chitosan and polyethylene glycol coating with their blend is studied. Agar plate test is performed against pseudomonas aeruginosa, Acinetobacter baumannii and methicillin-resistant staphylococcus aureus (MRSA), and the proposed process is helpful in healthcare industry and specified applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".