Green Synthesis of Silver Nanoparticles at Room Temperature Using Kiwifruit Juice
Why this work is in the frame
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Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 0.001 |
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 it