CHARACTERIZATION AND ANTIBACTERIAL STUDIES OF LEAF ASSISTED SILVER NANOPARTICLES FROM CARICA PAPAYA: A GREEN SYNTHETIC APPROACH
Bibliographic record
Abstract
Objective: The aim of this study is to synthesis silver nanoparticles using C arica papaya leaf extracts and testing its antibacterial activity. Methods: Silver nanoparticles (Agnps) were prepared by treating Carica papaya leaf extracts with 1 mm silver nitrate (AgNO 3 ). The bioreduced silver nanoparticles were characterized by ultraviolet visible spectroscopy (UV-Vis), Fourier transform infrared spectroscopy (FTIR) and the size and shape of the silver nanoparticles were obtained from transmission electron microscopy (TEM). The synthesized silver nanoparticles were tested for their antibacterial activity by disc diffusion method. Results: From the UV spectrum, it was observed that the peak at 430 nm showing the reduction of silver ions to stable silver nanoparticles. The size of the biosynthesized nanoparticles was in the range of 10-50 nm obtained from TEM image. FTIR measurements provided the bands at different wave numbers, indicating the stretching of alcohols, carboxylic acids, esters and ethers. The synthesized silver nanoparticles exhibited an excellent bactericidal activity against bacterial strains like Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus . Conclusion: Green synthesis of silver nanoparticles using papaya leaf extract exhibited effective antibacterial property.
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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".