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Study of resin coating adhesion on GFRP laminate surfaces after UV degradation

2024· article· en· W4402535499 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Adhesion and Adhesives · 2024
Typearticle
Languageen
FieldChemistry
TopicPhotopolymerization techniques and applications
Canadian institutionsMcMaster University
FundersSilesian University of Technology
KeywordsMaterials scienceAdhesiveFibre-reinforced plasticComposite materialCoatingDegradation (telecommunications)AdhesionEpoxyLayer (electronics)

Abstract

fetched live from OpenAlex

This paper describes research on the effect of UV radiation on the surface of a GFRP (Glass Fiber Reinforced Polymer) laminate in the context of increasing the adhesion between the laminate and polymer resin coatings. GFRP composite samples were prepared using the hand lay-up method. The samples were then exposed to various types of ultraviolet radiation (UVA, UVB, UVC) for 1000 h, with surface roughness measurements every 100 h. The flexural strength of laminates after photoaging was tested, which showed an increase in the strength of aged laminates compared to the reference sample by approximately 5–10 %. Coatings of various thicknesses were applied to the surface of the aged laminates and their adhesion to the substrate material was tested using the mandrel bending method alongside the cross-cut technique. The greatest change in surface roughness was observed on the sample exposed to UVC radiation, where the roughness increased approximately twice in relation to the reference sample. Coating adhesion tests have additionally shown that degradation under the influence of UVC has a positive effect on the adhesion of coatings to the laminate.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.023
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.306
Teacher spread0.289 · 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