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Record W2055784878 · doi:10.1002/pc.22442

Mechanical and morphological properties of wood plastic composites based on municipal plastic waste

2013· article· en· W2055784878 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePolymer Composites · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
FundersCentre québécois sur les matériaux fonctionnels
KeywordsMaterials sciencePolypropyleneComposite materialPolyethyleneWood flourCompatibilizationIzod impact strength testPolymerWood-plastic compositeSawdustCompatibility (geochemistry)CopolymerPolymer blendComposite numberUltimate tensile strengthPulp and paper industry

Abstract

fetched live from OpenAlex

Abstract Production and characterization of wood plastic composites (WPC) from the light fraction of municipal plastic wastes (post‐consumer) and wood processing residues (sawdust) were investigated. Composition analysis revealed the presence of polyethylene (PE) and polypropylene (PP) as the two main components of the matrix. In order to improve compatibility and adhesion between all the phases, an ethylene–octene copolymer was used to compatibilize the polymer phases and was also acting as an impact modifier, while the addition of maleated polyethylene and maleated polypropylene (MAPP) were acting as coupling agents between the polymer matrix and the wood flour. The combined effect of all the components was found to produce composites with interesting morphological (dispersion and adhesion) and mechanical properties (tension, torsion, flexion, and impact) after optimization of the additive package (blend of coupling agents). POLYM. COMPOS., 2013. © 2013 Society of Plastics Engineers

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)0.000
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 score0.091
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.018
GPT teacher head0.217
Teacher spread0.199 · 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