Photocatalytic degradation and heat reflectance recovery of waterborne acrylic polymer/ZnO nanocomposite coating
Bibliographic record
Abstract
Abstract This work aims to clarify the photocatalytic degradation mechanism and heat reflectance recovery performance of waterborne acrylic polymer/ZnO nanocomposite coating. To fabricate the nanocomposite coating, ZnO nanoparticles (nano‐ZnO) were dispersed into acrylic polymer matrix at the various concentrations from 1 to 6% (by total weight of resin solids). The photocatalytic degradation of nanocomposite coating under ultraviolet (UV) light irradiation has been investigated by monitoring its weight loss and chemical/microstructural/morphological changes. As the topcoat layer, its heat reflectance recovery has been evaluated under UV/condensation exposure by using an artificial dirty mixture of 85 wt% nanoclay, 10 wt% silica particles (1–5 μm), 1 wt% carbon black, and 2 wt% engine oil. After 108‐cycle UV/condensation exposure, infrared spectra and weight loss analysis indicated that the maximal degradation for nanocomposite coating is observed at 1 wt% nano‐ZnO. On the other hand, after 96 hr of UV light exposure, the nanocomposite coating with1 wt% nano‐ZnO could restore effectively the reflective index of solar‐heat reflectance coating (from 58.45 to 80.78%). Finally, the photodegradation mechanism of this waterborne acrylic polymer coating has been proposed as the UV‐induced formation of CCCO conjugated double bonds. As a result, its self‐cleaning phenomenon can be achieved as the recovery of heat reflectance.
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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".