Fabrication of Nano TiO2-Polymer Encapsulated Fluorescent Pigments for Weatherability Improvement of Powder Coating
Bibliographic record
Abstract
Fluorescent coatings have attracted attention due to their bright colors. However, they have fewer outdoor applications due to low stability, especially the poor weather resistance of the fluorescent pigments. In order to improve their weather resistance and maintain the excellent appearance, this study used polymer binders to coat a light shielding agent nano TiO2 on the surface of the fluorescent pigment to extend the durability. Three organic binders, polyester varnish, polyurethane varnish, and polyvinyl alcohol, were selected. Each binder was dissolved and mixed with pigments and nano TiO2 particles to make a polymer-TiO2 layer on the pigment surface. The effects of binder types and loadings were investigated and evaluated by accelerating weather test of corresponding fluorescent powder coatings. According to SEM and ash test, nano TiO2 was successfully coated on the surface of fluorescent pigments. The modified fluorescent pigment shows a strong ability to absorb ultraviolet rays, and the weather resistance of prepared coatings has been significantly improved compared with the original fluorescent powder coating. When using clear coat PE as a binder and setting the ratio of the binder to the nano TiO2 of 1:2, the UV exposure time of fluorescent powder coatings can be extended by over twelve times compared to the coatings with original pigment for the same color change. This study provides an effective approach to enhance the weather resistance of fluorescent coatings and thus expand their applications.
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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".