Fermented juices as reducing and capping agents for the biosynthesis of size-defined spherical gold nanoparticles
Bibliographic record
Abstract
Gold nanoparticles (AuNPs) are of scientific and industrial significance; however, the traditional synthesis methods employ toxic compounds. Hence, non-toxic and environmentally friendly AuNPs synthesis methods are of special interest. Here, AuNPs were produced using four solutions of fermented grape juices. UV/Vis absorption spectrophotometry and transmission electron microscopy indicated that AuNPs synthesized with a solution based on semi-sweet red grapes were mostly spherical with narrow size distribution (average diameter of 82.1 ± 36.2 nm). AuNPs of similar spherical morphology but smaller size were obtained using a solution based on semi-dry red grapes (57.1 ± 16.4 nm). A large variety of AuNPs shapes and broader size distribution were produced when solutions based on semi-sweet or dry white grapes were applied. In this case, the average sizes of the AuNPs were 271.6 ± 130.2 nm and 76.0 ± 47.2 nm, respectively. Using energy dispersive X-ray spectroscopy, Au, C, and O were detected, confirming formation of biogenic AuNPs in all cases. Mie theory calculations for AuNPs synthesized with the aid of solutions based on red grapes suggest that their optical properties are different and best suited for distinct downstream applications. Attenuated total reflectance Fourier transform infrared spectroscopy, the Folin-Ciocalteu assay, and the Bertrand’s method were used to examine bioactive compounds present in the solutions applied for synthesis. Phenolics, and to a lesser extent reducing sugars, were identified as likely playing a significant role in reduction and stabilization of the AuNPs. These results display the great potential of these solutions for green synthesis of size defined AuNPs, and illustrate that different grape varieties may be used to obtain AuNPs with unique properties.
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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) | 0.001 | 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".