MétaCan
Menu
Back to cohort
Record W2147288652 · doi:10.1177/0731684411412643

Effect of low-profile additives on thermo-mechanical properties of glass fiber-reinforced unsaturated polyester composites

2011· article· en· W2147288652 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

VenueJournal of Reinforced Plastics and Composites · 2011
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceComposite materialGlass transitionGlass fiberFlexural strengthVinyl esterThermal expansionShrinkageComposite numberPolymerMonomer

Abstract

fetched live from OpenAlex

Low profile additives (LPA) are thermoplastics particles incorporated into unsaturated polyester (UP) resin in order to improve the surface finish of fiber glass/UP composites by shrinkage compensation. They are widely used in automotive applications where high-quality surface finish is required. In this article, the effect of low-profile additives on the thermo-mechanical properties of resin transfer molded fiber glass/UP panels is investigated. A combination of poly vinyl-acetate and poly methyl-methacrylate additives was tested with three concentrations (0%, 10%, and 40%). The flexural and shear properties were measured by three-point bending and short-beam tests. Thermo-mechanical and dynamic mechanical analyses were performed to measure the coefficient of thermal expansion and the glass transition temperature. The mechanical properties as well as the coefficient of thermal expansion were reduced upon addition of low-profile additives, whereas the glass transition temperature was improved with the content of low profile additives due to a better compatibility of the LPA/UP system.

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 score0.005
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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