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Record W4221086993 · doi:10.1002/pc.26630

Mechanical and water absorption behavior of thermoset matrices reinforced with natural fiber

2022· article· en· W4221086993 on OpenAlexaff
Manel Haddar, Youssef Ben Slim, Sana Koubaa

Bibliographic record

VenuePolymer Composites · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsMaterials scienceComposite materialEpoxyComposite numberAbsorption of waterFlexural strengthCompression moldingThermosetting polymerFlexural modulus

Abstract

fetched live from OpenAlex

Abstract In this work, we investigate the effect of the Posidonia Oceanica fiber (POF) rate and matrix type on the mechanical properties and water absorption behavior of the produced composites by compression molding process. The obtained results show that the stiffness, strength and hardness of unsaturated polyester resin (UPR)/POF composite increase with POF reinforcement rates. Further, the choice of matrix plays in important role on the flexural properties of the composites. At equal percentage of POF (20 wt%), UPR/20POF composite shows the best flexural modulus compared to Epoxy/20POF composite. By contrast, the maximum deflection of UPR/20POF is approximately three times less important than the Epoxy/20POF composite. In addition, in the case of UPR/POF composite, water absorption tests revealed that the percentage of water content was found to increase with POF content. It was observed that the water absorption pattern of the all formulation of the composites was found to approach the Fickian diffusion behavior. It was also proven that the moisture resistance of UPR/20POF composite is greater than Epoxy/20POF composite. Compared to literature, composite reinforced with higher load of POF (UPR/30POF) exhibits higher dimensional stability, which explains their importance when it will be used for application in humid environment.

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 categoriesInsufficient payload (model declined to judge)
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 score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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.

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
Published2022
Admission routes1
Has abstractyes

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