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Record W2008444993 · doi:10.1177/0731684407069950

The Effect of Curing Conditions on the Properties of Silica Modified Glass Fiber Reinforced Epoxy Composite

2007· article· en· W2008444993 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2007
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialCuring (chemistry)EpoxyComposite numberIsothermal processGlass fiberFlexural strength

Abstract

fetched live from OpenAlex

The effects of curing conditions on the mechanical properties of glass fiber reinforced epoxy composites under different fiber prestressing levels were quantitatively studied. The composite samples were prepared with silica particle modified continuous E-glass fibers, epoxy resin matrix, fiber prestressing and step curing processing. Room temperature cured and isothermal high temperature cured composite samples, with the same content and structure, were made to study the difference of three curing conditions on a quantitative level. Based on the test results of flexural, shear, and impact properties of the composite samples, it was found that the step curing procedure generates composite samples with up to 47% and 14% increased properties than room temperature cured and isothermal high temperature cured samples, respectively, when manufactured under the same fiber prestressing level. The possibility of generating an optimum residual stress within the samples during processing is proposed and discussed to explain the contribution of the step curing process to the composite properties.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.407

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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