Silver Nanoparticles as Antifungal Agents in Acrylic Latexes: Influence of the Initiator Type on Nanoparticle Incorporation and Aureobasidium pullulans Resistance
Bibliographic record
Abstract
Discoloration of wood coatings due to fungal growth negatively affects the aesthetic properties of the coatings, and new ways to control fungal growth on coatings are needed. For this reason, silver nanoparticles (AgNPs) have been incorporated in acrylic latexes as antifungal agents. Using miniemulsion polymerization, latexes were prepared with two types of initiators (hydrophilic and hydrophobic) to assess the influence of the initiator type on AgNPs dispersion, both within the latex particles and the dry film. In addition, the impact of NP dispersion on resistance to black-stain fungi (Aureobasidium pullulans) was also evaluated. Inductively coupled plasma optical emission spectroscopy (ICP-OES) analysis indicates that acrylic latexes prepared with azobisisobutyronitrile (AIBN) as the initiator contain more AgNPs than those prepared with potassium persulfate (KPS). Cryo-TEM and SEM analyses show that the distribution of the AgNPs within the polymer particles is influenced by the nature of the initiator. When AIBN, a hydrophobic initiator, is used, the AgNPs appear to be closer to the surface of the polymer particles and more evenly distributed. However, the antifungal efficiency of the AgNPs-embedded latexes against A. pullulans is found to be higher when KPS is used, despite this initiator leading to a smaller amount of incorporated AgNPs and a less uniform dispersion of the nanoparticles.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".