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Record W2984863580 · doi:10.18280/rcma.290401

Effect of Form Factor and Mass Fraction of Alfa Short Fibers on the Mechanical Behavior of an Alfa/Greenpoxy Bio-composite

2019· article· fr· W2984863580 on OpenAlexvenueno aff
Aboubakr Amrane, Z. Sereir, Christophe Poilâne, Alexandre Vivet

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2019
Typearticle
Languagefr
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsComposite numberMass fractionMaterials scienceFraction (chemistry)Composite materialChemistryChromatography

Abstract

fetched live from OpenAlex

This experimental study highlights the effect of the geometric form factor (=length/diameter) and the fiber mass fraction on the mechanical behavior of an Alfa/Greenpoxy bio-composite. The Alfa stalks, collected in the Djelfa region in the Algeria highlands, were cut into pieces with length of 7 to 10 cm, washed and dried for two days at 70 C. Using a knife mill coupled to three sieves (1.6, 2 or 2.5 mm), three categories of short fibers, according to their form factor , were obtained. Depending on the incorporated mass fraction (5, 10, 15 or 20 %) and the three form factors of the fibers, twelve types of plates were manufactured by hand molding followed by a curing cycle to accelerate the polymerization, reduce porosity and improve the final surface state. The main mechanical characteristics were determined with tensile, bending and shock tests on ISO 3167-type A samples, obtained by laser cutting. The results revealed that the increase of the form factor and the mass fraction gives rise to a significant improvement of the mechanical properties. We conclude that optimal processing conditions will maximize the mechanical properties of Alfa/Greenpoxy bio-composites.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.293
Teacher spread0.262 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

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