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Environmental Aging Effect on Tensile Properties of GFRP Made of Furfuryl Alcohol Bioresin Compared to Epoxy

2014· article· en· W1963717715 on OpenAlexaff
Amanda Eldridge, Amir Fam

Bibliographic record

VenueJournal of Composites for Construction · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsQueen's University
Fundersnot available
KeywordsFibre-reinforced plasticFurfuryl alcoholEpoxyUltimate tensile strengthMaterials scienceComposite materialDurabilityGlass fiberPolymerChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Biocomposites are emerging as a possible sustainable alternative in structural applications, and as a result, a major focus should be on their durability. This study focuses on the environmental aging of bioresin glass fiber–reinforced polymer (GFRP) through immersion in saltwater at elevated temperatures. The resin is furfuryl alcohol based, derived from renewable resources, such as corncobs and sugarcanes. Deterioration was quantified by tensile testing of unidirectional bioresin GFRP coupons at various stages of exposure, and compared to conventional epoxy GFRP coupons under the same conditions. A total of 150 specimens were exposed to three different environments, namely 23°, 40°, and 55°C water with 3% salt concentration, for up to 300 days. It was found that the bioresin GFRP retained 80, 44, and 39% of its original strength at the three temperatures, respectively. On the other hand, the epoxy GFRP exhibited 86, 72, and 61% strength retentions, respectively. No reductions occurred to the Young’s moduli. The Arrhenius model was applied, assuming environments with mean annual temperatures of 3°, 10°, and 20°C, representing different regions in North America. It was estimated that bioresin GFRP strength retentions after 100 years, at the three mean temperatures, are 65, 61, and 50%, respectively.

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)0.000
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 score0.016
Threshold uncertainty score0.565

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 head0.011
GPT teacher head0.228
Teacher spread0.218 · 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.

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

Citations22
Published2014
Admission routes1
Has abstractyes

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