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Record W2135234869 · doi:10.1177/073168400772678518

Effect of Aqueous Environment and Temperature on Glass-Fibre Epoxy Resin Composites

2000· article· en· W2135234869 on OpenAlexaff
F. Ellyin, Christof Rohrbacher

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2000
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceComposite materialEpoxyImmersion (mathematics)Distilled waterAbsorption of waterScanning electron microscopeEmbrittlementGlass fiberComposite numberAqueous solution

Abstract

fetched live from OpenAlex

The main objective of this experimental investigation is to study the absorption behaviour of fibre-glass reinforced epoxy matrix composite laminates immersed in distilled water, in ambient and 90'C temperatures, and its effect on the mechanical properties. A comparative study is carried out to evaluate the properties of dry and water immersed specimens, and to investigate damage caused by the aqueous environment and temperature. It is found that the degree of damage strongly depends on the immersion temperature. The fracture surfaces in three different environments are studied using a scanning electron microscope and micro-macro damage correlation is established. The threshold strain for the matrix crack initiation increases when the laminated are immersed in ambient water in comparison to that of a dry environment. Immersion at high water temperature leads to embrittlement of the glass fibres and matrix blistering resulting in a slight decrease in threshold crack initiation strain.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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)0.0020.000

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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations63
Published2000
Admission routes1
Has abstractyes

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