Effective Control of Molds Using a Combination of Nanoparticles
Bibliographic record
Abstract
Molds are filamentous fungi able to grow on a variety of surfaces, including constructed surfaces, food, rotten organic matter, and humid places. Mold growth is characterized by having an unpleasant odor in enclosed or non-ventilated places and a non-aesthetic appearance. They represent a health concern because of their ability to produce and release mycotoxins, compounds that are toxic to animals and humans. The aim of this study was to evaluate commercial nanoparticles (NPs) that can be used as an additive in coatings and paints to effectively control the growth of harmful molds. Four different NPs were screened for their antifungal activities against the mycotoxin producing mold strains Aspergillus flavus and A. fumigatus. The minimal inhibitory concentrations of the NPs were determined in broth media, whereas an agar diffusion test was used to assess the antimold activity on acrylic- and water-based paints. The cytotoxic activity and the inflammatory response of the NPs were also evaluated using the established human derived macrophage cell line THP-1. Results showed that a combination of mix metallic- and ZnO-NPs (50:10 μg/mL) effectively inhibited the fungal growth when exposed to fluorescent light. Neither cytotoxic effect nor inflammatory responses were recorded, suggesting that this combination can be safely used in humid or non-ventilated environments without any health concerns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".