Sol-gel zinc oxide nanoparticles: advances in synthesis and applications
Bibliographic record
Abstract
Zinc oxide nanoparticles (ZnO) exhibit numerous characteristics such as biocompatibility, UV protection, antibacterial activity, high thermal conductivity, binding energy, and high refractive index that make them ideal candidates to be applied in a variety of products like solar cells, rubber, cosmetics, as well as medical and pharmaceutical products. Different strategies for ZnO nanoparticles’ preparation have been applied: sol-gel method, co-precipitation method, etc. The sol-gel method is an economic and efficient chemical technique for nanoparticle (NPs) generation that has the ability to adjust the structural and optical features of the NPs. Nanostructures are generated from an aqueous solution including metallic precursors, chemicals for modifying pH using either a gel or a sol as a yield. Among the various approaches, the sol-gel technique was revealed to be one of the desirable techniques for the synthesis of ZnO nanoparticles. In this review, we explain some novel investigations about the synthesis of zinc oxide nanoparticles via sol-gel technique and applications of sol-gel zinc oxide nanoparticles. Furthermore, we study recent sol-gel ZnO nanoparticles, their significant characteristics, and their applications in biomedical applications, antimicrobial packaging, drug delivery, semiconductors, biosensors, catalysts, photoelectron devices, and textiles.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".