Microwave‐mediated synthesis of zinc oxide nanoparticles: a therapeutic approach against Malassezia species
Bibliographic record
Abstract
A successful protocol was developed to aid in the reduction in dandruff‐causing fungi, namely Malassezia globasa and Malassezia furfur . Both the species were isolated from volunteers aged between 20 and 22 suffering from dandruff, cultured ex vivo, and tested against the presence of synthesised zinc oxide nanoparticles (ZnNP). Direct microscopy, scanning electron microscopy (SEM), and biochemical assays specific to Malassezia species were conducted to identify dandruff‐causing fungal species. Microwave‐mediated synthesis of ZnNP was performed and characterised by UV–vis, X‐ray diffraction, and SEM. The nanoparticles were tested against both Malassezia species and proved highly effective in inhibiting these fungi, although M. furfur was more susceptible than M. globosa . An optimum amount of 100 ppm was found to be sufficient to work as an antifungal agent. Synergistic effects of ZnNP with commercial shampoos were tested, and the result showed enhanced antifungal effects. To mimic the natural biofilm formed by these species on human skin, the formation of fungal biofilm was allowed on polystyrene coverslips. ZnNP was effective in eradication biofilm. Since zinc is an essential mineral for all living organism and is considered as biocompatible, the synthesised nanomaterials can be used in the formulation of antidandruff shampoos.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".