Green Synthesis of Silver Nanoparticles at Room Temperature Using Kiwifruit Juice
Bibliographic record
Abstract
Comparing with physical and chemical methods, green synthesis techniques are emerging as facile and eco-friendly methods for the synthesis of silver nanoparticles. In this work, we demonstrated the biological synthesis of silver nanoparticles by the reduction of silver ions using kiwifruit juice as the reducing and stabilizing reagent. From the evidence of ultraviolet-visible spectroscopy and transmission electron microscopy, different sizes of silver nanoparticles were formed when the juice volume, reaction temperature, and reaction time were altered with respect to 0.01% silver acetate solution. The synthesized silver nanoparticles were stable for more than 1 month. Transmission electron microscopy studies showed the silver nanoparticles synthesized in room temperature have the diameters in the range of 5–25 nm. The proposed synthesis method is green and low cost, and the synthesized silver nanoparticles have potential bioanalytical applications.
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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".