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Record W3211215471 · doi:10.3390/coatings11111328

Self-Healing UV-Curable Acrylate Coatings for Wood Finishing System, Part 2: Impact of Monomer Structure and Self-Healing Parameters on Self-Healing Efficiency

2021· article· en· W3211215471 on OpenAlexafffund
Chloé Paquet, Stephen Brown, J.E. Klemberg-Sapieha, Jean‐François Morin, Véronic Landry

Bibliographic record

VenueCoatings · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsPolytechnique MontréalUniversité LavalNatural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSelf-healingGloss (optics)Composite materialMonomerAcrylateSelf-healing materialCoatingPolymer

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.259
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes2
Has abstractyes

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