Current Research on Zinc Oxide Nanoparticles: Synthesis, Characterization, and Biomedical Applications
Bibliographic record
Abstract
Zinc oxide nanoparticles (ZnO-NPs) have piqued the curiosity of researchers all over the world due to their extensive biological activity. They are less toxic and biodegradable with the capacity to greatly boost pharmacophore bioactivity. ZnO-NPs are the most extensively used metal oxide nanoparticles in electronic and optoelectronics because of their distinctive optical and chemical properties which can be readily modified by altering the morphology and the wide bandgap. The biosynthesis of nanoparticles using extracts of therapeutic plants, fungi, bacteria, algae, etc., improves their stability and biocompatibility in many biological settings, and its biofabrication alters its physiochemical behavior, contributing to biological potency. As such, ZnO-NPs can be used as an effective nanocarrier for conventional drugs due to their cost-effectiveness and benefits of being biodegradable and biocompatible. This article covers a comprehensive review of different synthesis approaches of ZnO-NPs including physical, chemical, biochemical, and green synthesis techniques, and also emphasizes their biopotency through antibacterial, antifungal, anticancer, anti-inflammatory, antidiabetic, antioxidant, antiviral, wound healing, and cardioprotective activity. Green synthesis from plants, bacteria, and fungus is given special attention, with a particular emphasis on extraction techniques, precursors used for the synthesis and reaction conditions, characterization techniques, and surface morphology of the particles.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".