High gravity-assisted green synthesis of ZnO nanoparticles via Allium ursinum: Conjoining nanochemistry to neuroscience
Bibliographic record
Abstract
Abstract This study aims to investigate the synthesis of ZnO nanoparticles (NPs) using high-gravity technique and mediated by novel Allium ursinum leaves’ extract, which is derived for the first time. The synthesized NPs were fully characterized, and the potential biological activities were evaluated in the context of neuroscience. The size of the nanoparticles was found in range of 20 to 60 nm’s, with a considerable size distribution of 30 nm; and their morphology are semi-spherical. More specifically the potential antibacterial activity against gram positive (S. aureus) and gram negative (E. coli) bacteria were screened. To the best of our knowledge, this study could be considered as the first investigation in the world, and the first comprehensive study on synthesizing ZnO NPs using high-gravity technique mediated by this plant extract. The experimental results were found to be very promising to the nano-chemistry, green chemistry and also the applied neuroscience. In addition, the mentioned green synthesis procedure leads to the formation of NPs with considerable antibacterial, cellular proliferation and mitochondrial membrane potential as well as minimum apoptosis index and acceptable relative cell viability that are all independent with the morphology and texture of the media of these NPs. The green synthesized nanoparticles showed considerable antioxidant activity in comparison with the standard drug, more than 80%, and low cytotoxicity, more than 60% cellular viability in most of the concentrations, as well as proliferation inhibition of up to 84% in the maximum concentration. Along with those results, the mitochondrial membrane potential showed also promising absorption of over 1.6. Furthermore, the antioxidant activity of the green synthesized ZnO NPs was recorded above 82% which is greater than the standard BHT as well as the leaf extract
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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".