Self-Healing UV-Curable Acrylate Coatings for Wood Finishing System, Part 2: Impact of Monomer Structure and Self-Healing Parameters on Self-Healing Efficiency
Bibliographic record
Abstract
Wood is increasingly used in construction for the benefits it brings to occupants and for its ecological aspect. Indoor wood products are frequently subject to mechanical aggressions, their abrasion and scratch resistance thus need to be improved. The coating system ensures the wood surface protection, which is, for wood flooring, a multilayer acrylate UV-curable 100% solid system. To increase the service life of wood flooring, a new property is studied: self-healing. The objective of this study is to observe the impact of monomer structure on self-healing efficiency and the effect of self-healing parameters. A previous formulation was developed using hydrogen bond technology to generate the self-healing property. In this paper, the assessment of the formulation and the self-healing parameters’ impact on self-healing efficiency as well as the physicochemical properties are presented. The composition of the monomer part in the formulations was varied, and the effect on the conversion yield (measured by FT-IR), on the Tg and crosslinking density (measured by DMA) and on mechanical resistance (evaluated via hardness pendulum, indentation, and reverse impact) was analyzed. The self-healing efficiency of the coatings was determined by gloss and scratch depth measurements (under constant and progressive load). It was proven that monomers with three acrylate functions bring too much crosslinking, which inhibits the chain mobility necessary to observe self-healing. The presence of the AHPMA monomer in the formulation permits considerably increasing the crosslinking density (CLD) while keeping good self-healing efficiency. It was also observed that the self-healing behavior of the coatings is different according to the damage caused. Indeed, the self-healing results after abrasion and after scratch (under constant or progressive load) are different. In conclusion, it is possible to increase CLD while keeping self-healing behavior until a certain limit and with a linear monomer structure to avoid steric hindrance. Moreover, the selection of the best coatings (the one with the highest self-healing) depends on the damage.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".