Green synthesis of silver nanoparticles from vegetable waste of pea Pisum sativum and bottle gourd Lagenaria siceraria: Characterization and antibacterial properties
Bibliographic record
Abstract
A huge amount of food waste is being generated every day globally. Usually, India generates ∼350 million tons of food waste every year. Therefore, there is an urgent need to initiate research focusing on the management and hygienic methods of reuse of food waste together with advanced user-friendly methods of converting it into some useful products thereby generating wealth from food waste. A promising approach seems to biosynthesize silver nanoparticles (AgNPs) from such unutilized food. An alternative clean technology does not rely on the use of toxic chemicals and solvents. It is commonly allied with traditional nanoparticle synthesis processes. In the present work, the peels of two vegetables, pea (Pisum sativum) and bottle gourd (Lagenaria siceraria), were used to generate AgNPs. AgNPs were obtained by dissolving 1.5 ml of the peel extract of each vegetable in 50 ml of silver nitrate (AgNO3) and incubating for 24 h at room temperature. For the confirmation of AgNP production UV–visible spectroscopy was used. Field Emission Scanning Electron Microscopy (FESEM), X-ray diffraction (XRD), and attenuated total reflection-infrared spectroscopy (ATR-IR Analysis) were used to characterize them. Furthermore, AgNPs in different concentrations were used to test antibacterial activity against bacteria Escherichia coli through the disc diffusion method. Thus, our research indicates that AgNPs can be a safe and environmentally beneficial production technology from unutilized vegetable wastes that may play an important role in the management of food waste in the future and has antibacterial potential to preserve vegetables from bacterial contamination.
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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) | 0.000 | 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".