Development and Evaluation of Gel Incorporated with Biogenically Synthesised Silver Nanoparticles
Bibliographic record
Abstract
The purpose of the study was to prepare gels by using polymer chitosan and carbopol. The prepared gels were incorporated with silver nanoparticles biogenically synthesized by leave extract of Eichhornia crassipes. The silver nanoparticles having average particle size of 21.71+/- 4.42 nm and showing antimicrobial activity against Gram positive and Gram negative bacteria were taken for preparing gels. Different characteristics like pH, spreadability, extrudability, viscosity, in-vitro drug diffusion, swelling, and antimicrobial study of the prepared gels were evaluated. The gel formulated with chitosan showed better physicochemical characteristic as compared to gel formulated with polymer carbopol. In-vitro antibacterial study of the prepared silver nanoparticle incorporated gels was carried out using microorganisms like Staphylococcus aureus , Bacillus subtilis, Escherichia hermanii , and Pseudomonas aeruginosa. The prepared gels showed promising activity against Staphylococcus aureus and Bacillus subtilis, moderate activity against Escherichia coli and Pseudomonas aeruginosa . The optimized silver nanoparticle incorporated gel was having potency to be used as an antimicrobial agent.
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".