Surface Hardness of UV-Solidified Coatings Containing In-situ Synthesized, Self-dispersed Nano-gel Domains as a Function of Surface Roughness and Viscoelastic Characteristics
Bibliographic record
Abstract
UV-curable coatings have attracted the interest of researchers in industry and academia because of their outstanding features. In this paper, the effect of monomer functionality and monomer concentration on the photo-induced nano-gelation of UV-curable coatings has been investigated. The coatings were prepared by incorporating an aliphatic urethane acrylate oligomer with a mono-functional monomer (phenoxy ethyl acrylate (PEA)) or multifunctional acrylate monomers (1,6-hexanediol diacrylate (HDDA) and trimethylolpropane triacrylate (TMPTA)) to form different nano-gel domains. The viscoelastic properties of the coatings, including crosslinking density, glass transition temperature, and elastic modulus, were evaluated using dynamic mechanical thermal analysis (DMTA). The surface hardness of the applied coatings was measured by two different methods: the pendulum hardness test and Knoop micro-indentation. Surface roughness and dimensions of nano-gels were evaluated using scanning electron microscopy (SEM) and atomic force microscopy (AFM). The results showed the effect of nano-gel dimensions on the mechanical properties of coatings. The surface hardness of the coatings obtained by different methods showed the same results. The findings of this study provide important insights into the design and optimization of UV-solidified coatings for applications in the field of automotive coatings.
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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".