A Novel Method to Formulate Pigmented Powder Coatings by Ultrafine Powders
Bibliographic record
Abstract
Powder coatings are a green alternative to conventional solvent-borne liquid coatings, but they have the intrinsic drawback of color-matching and adjustment in production with the conventional extrusion process. In this study, an industrially applicable approach to formulate color powder coatings utilizing ultrafine powders, i.e., a powdery blending and pressing method, was invented. This novel method was validated by comparing samples prepared by the Method 1 conventional extrusion method with an extra ultramarine pigment at 3%; Method 2 powdery blending and pressing of the original coatings and the same coating with 6% ultramarine pigment utilizing regular (coarse) powder coatings; and Method 3 utilizing ultrafine powder coatings for the two coatings with the same formulations as Method 2. The coating powders were prepared to have similar particle sizes and particle size distributions, with three commonly used coating binders, namely polyester-epoxy hybrid, polyester/TGIC (triglycidyl isocyanurate), and polyurethane (PU). The powders prepared by Methods 1 and 3 had similar flow abilities in terms of angle of repose (AOR) and avalanche angle (AVA). The performance of the new coatings by Method 3 was close to or better than the ones prepared by Method 1 in terms of the specular gloss, DOI (distinctness-of-image), reflection haze and color values, being superior to Method 2. The coatings via ultrafine powders also exhibited a comparable ultramarine particle distribution in the coating cross-sections as the conventional ones, whereas the ones via regular powders had an inferior pigment dispersion. The new method can greatly enhance the production efficiency and reduce the cost of powder coatings with compound colors, especially for small batch manufacturing.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".