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Record W2331116764 · doi:10.1177/0892705714563120

Formulation and tensile characterization of wood–plastic composites

2014· article· en· W2331116764 on OpenAlex
Fayçal Mijiyawa, Demagna Koffi, B. V. Kokta, Fouad Erchiqui

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.

Bibliographic record

VenueJournal of Thermoplastic Composite Materials · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsMaterials scienceComposite materialPolypropyleneUltimate tensile strengthCompoundingMicromechanicsCompression moldingYoung's modulusFiberMaleic anhydrideModulusComposite numberPolymerCopolymerMold

Abstract

fetched live from OpenAlex

This study reports the effects of wood fibers and 3 wt% maleic anhydride-grafted polypropylene used as coupling agent on the tensile properties of polypropylene/wood composites. Compounding was done in a roller-based internal batch mixer followed by compression molding. Our findings show that both birch and aspen wood fibers improve the elastic modulus and the tensile strength of composites, and the chemical treatment improves the fiber–matrix interface. A comparison of experimental results’ elastic modulus with micromechanics theoretical models shows that the Lavengood–Goettler model is closer to experimental data. Also the results showed that the polypropylene/wood composites’ elastic modulus exceeds high-performance thermoplastics commonly used in gears manufacturing. Thus, the price of polypropylene/wood fibers makes it a viable alternative for similar application.

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)0.001
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 score0.019
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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