Influence of Magnetic Particles and Magnetic Field on Gloss in UV Coating
Bibliographic record
Abstract
UV-curable coatings possess numerous advantages, including high production rate, low environmental impact, and customizability, making them highly appealing for a wide range of applications. However, one of the greatest challenges in UV-curable coating is achieving an optimal low-gloss surface by adding matting agents to the coating formulation. Therefore, it is essential to find a suitable matting agent type and an efficient roughness creation method to tailor the surface gloss and generate a controlled low-gloss surface. In this study, modified magnetic particles were added to the coating formulation as matting agents, and the UV curing process was conducted under a magnetic field of 10 to 100 mT. The combined effect of adding magnetic particles and magnetic field during UV curing on the coatings’ surface gloss was investigated. The impact of modification, dispersion, and concentration of magnetic particles and the effect of magnetic field force on the final surface gloss and roughness were assessed. Moreover, the effect of the dispersion and concentration of magnetic particles on the photopolymerization of the coating was evaluated. The result indicated that both the magnetic field force and modification of the magnetic particles impact the surface roughness. A CI-APTES 5% wt. sample cured under a 60 mT magnetic field led to the highest decrease in 20° gloss.
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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".