Self-Healing UV Curable Acrylate Coatings for Wood Finishing System, Part 1: Impact of the Formulation on Self-Healing Efficiency
Bibliographic record
Abstract
In the wood flooring sector, good surface mechanical properties, such as abrasion and scratch resistance, are prerequisite. Surface wood protection is provided by finishing systems. Despite coating improvement, scratches formation on wood flooring is unavoidable. A new approach to increase service life is to confer the self-healing property to the finishing system. The most common coatings used for prefinished wood flooring are acrylate UV curable 100% solids coatings. They usually have good mechanical properties and high cross-linking density. The objective of this study was to develop and evaluate an intrinsic self-healing formulation, which is applicable to wood flooring. For this purpose, acrylate formulations were developed with monomers and oligomers carrying hydroxyl groups. To meet the requirements of wood application, hardness, and polymerization conversion of coatings were evaluated. König pendulum damping tests provide information on coating hardness and flexibility. Results around 80 oscillations is acceptable for UV curable wood sealer. The chemical composition was studied by FT-IR spectroscopy while dynamical mechanical analysis (DMA) was performed to determine glass transition temperature and cross-linking density. The self-healing behavior was evaluated by gloss and scratch depth measurements. The formulation’s composition impacted the hydrogen binding quantity, the conversion, the Tg and the cross-linking density. The (hydroxyethyl)methacrylate (HEMA) monomer provided self-healing and acrylated allophanate oligomer allowed self-healing and cross-linking. This study demonstrated that it is possible to combine high cross-linking density and self-healing property, using components with low steric hindrance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".