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Physical and Mechanical Properties of Polypropylene-Wood-Carbon Fiber Hybrid Composites

2015· article· en· W2219151855 on OpenAlex
Djamila Kada, Sébastien Migneault, Ghezalla Tabak, Ahmed Koubaa

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

VenueBioResources · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsComposite materialMaterials sciencePolypropyleneFiberCarbon fiber compositeComposite number

Abstract

fetched live from OpenAlex

Effects of the addition of short carbon fibers (CFs) on the mechanical, physical, and morphological properties of polypropylene (PP) and woodpolypropylene composites (WPCs) were investigated. Hybrid composites (mix of wood and CFs) were manufactured in a two-stage process, pellet extrusion and samples mold injection with varying amounts of poplar wood fiber (0%, 20%, 30%, and 40%) and CFs (0%, 3%, 6%, and 9%), with and without maleic anhydride grafted PP (MAPP) as a coupling agent. The composites were prepared with extrusion blending followed by injection molding. The samples where then tested for mechanical and physical properties, and fractured surfaces where observed with scanning electron microscopy. The results indicated that the addition of CFs to WPCs improved the tensile and flexural strength and the modulus of elasticity but had only a small influence on elongation at break and impact strength. The density of hybrid composites slightly increased with CFs proportion but their water absorption was not affected. Scanning electron micrographs of the tensile fractured specimens showed improved adhesion of CFs and poplar with the PP matrix in the presence of a coupling agent.

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 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 score0.002
Threshold uncertainty score0.510

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 head0.022
GPT teacher head0.227
Teacher spread0.205 · 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