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Record W4295027262 · doi:10.1002/pen.26126

Low density polyethylene composites based on flax fibers modified by a combination of coupling agent

2022· article· en· W4295027262 on OpenAlexafffund
Désiré Yomeni Chimeni, Ali Fazli, Charles Dubois, Denis Rodrigue

Bibliographic record

VenuePolymer Engineering and Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité LavalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesNOVA Chemicals
KeywordsMaterials scienceComposite materialThermogravimetric analysisFlexural strengthComposite numberFiberUltimate tensile strengthMolding (decorative)Scanning electron microscopeIzod impact strength testSurface modificationPolyethyleneCompression moldingMoldChemical engineering

Abstract

fetched live from OpenAlex

Abstract This investigation proposes a natural fiber surface modification approach based on the use of a system of coupling agents (CAs) to improve the mechanical properties of polymer composites made of flax fibers and low‐density polyethylene. This system combines the coating of the NaOH treated (mercerized) flax fibers by a CA (Epolene C18 [M‐C18]) in a solution under moderated heat (70°C–80°C), followed by the addition of 1% or 3% wt. of another CA (Epolene E43 [M‐E43]) during the injection molding. The comparison between the solution modified and the neat and mercerized flax fibers using scanning electron micrograph (SEM), thermogravimetric analysis (TGA), and density underscored some differences ascribed to the presence of M‐C18 onto the modified fibers. The mechanical properties showed that the combination of solution modification and direct introduction during the injection‐molding step of 1% of M‐E43 was efficient as the corresponding composites exhibited an improvement of 9.1%, 15.9%, and 11.1% of his tensile, flexural, and impact strengths respectively compared to the neat fiber composite. Due to their good impact and flexural strengths, these composites can be suitable for automotive parts such as keyboard.

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 score0.221
Threshold uncertainty score0.512

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 head0.007
GPT teacher head0.204
Teacher spread0.198 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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