The effect of silver nanoparticles on the black-stain resistance of acrylic resin for translucent wood coating application
Bibliographic record
Abstract
Translucent coatings applied to wood that is used for exterior applications often fail because of photodegradation and colonisation by black-stain fungi. This paper reports the effect of silver nanoparticles on the black-stain resistance of acrylic latex coatings. Acrylic latexes that contained various concentrations of silver nanoparticles were mixed with a commercial acrylic resin. The formulations were then applied to red pine (Pinus resinosa) sapwood, which was later evaluated for fungal resistance to Aureobasidium pullulans, Sclerophoma pityophila, and Eppicoccum nigrum. Latexes with silver nanoparticle concentrations as low as 0.03% (total coating formulation weight) were able to limit S. pityophila and E. nigrum growth, while higher concentrations were needed to limit the growth of A. pullulans. The influences of silver nanoparticles on the optical properties of the coating (i.e., colour, opacity, and gloss) were evaluated. It was demonstrated that the addition of silver nanoparticles to the formulation does not compromise the development of a translucent coating.
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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 | 0.000 | 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) | 0.001 | 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".