Color stability for wood products during use: Effects of inorganic nanoparticles
Bibliographic record
Abstract
Despite significant progress having been achieved in recent years to improve wood’s durability, additional developments are still necessary to increase its color stability. ZnO and CeO2 nanoparticles were compared to UV absorbers (Tinuvin 477 DW, 292 and 5151) commonly used to stabilize the wood products color. Nanoparticles, with concentrations in the range 1 to 2 wt%, and UV absorbers, using concentrations advised by the manufacturer, were dispersed in a waterborne UV curable polyurethane/polyacrylate resin. Dispersions were carried out with a high speed mixer at 2,500 RPM with micro glass beads or not, depending on the form of the nanoparticles. Nanocomposite coatings were aged with a weather-o-meter (CI 3000+ – Atlas) according ASTM G155. Color variations were measured with a colorimeter (BYK Gardner – Color Guide 45/0) working with the CIE L*a*b* system. The aim of the present study was to assess the effects of inorganic UV absorbers on the stabilization of color under indoor conditions. Results showed that nanoparticles (ZnO, CeO2) absorbed UV light frequencies in a manner similar to common organic molecules. Their efficiency was better at medium durations of light exposure, for which they could achieve the action of organic absorbers. Finally, the simultaneous use of both absorbers seems to create a protective synergy when degradation is due to UV energy alone.
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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 | 0.001 |
| 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".