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Record W2155374579 · doi:10.1002/pen.20854

Effects of raw fiber materials, fiber content, and coupling agent content on selected properties of polyethylene/wood fiber composites

2007· article· en· W2155374579 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.

Bibliographic record

VenuePolymer Engineering and Science · 2007
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of TorontoUniversité LavalUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceComposite materialFiberCrystallizationPolyethyleneRheologyWood flourChemical engineering

Abstract

fetched live from OpenAlex

Abstract This study investigated the effects of raw fiber materials, fiber content, and coupling agent (CA) content on mixing torque, rheological properties, and crystallization behavior of wood plastic composites (WPC). WPCs were prepared through melt molding processes. This study adopted a response surface strategy of 20 run optimal design for three factors including wood fiber type, fiber content, and CA content. Wood fiber type or wood fiber characteristics influence equilibrium torque and viscosity. The power index n for viscosity as a function of frequency was affected not only by wood fiber content, but also by CA content and wood fiber type. Addition of wood fibers to the system as nucleating agents favors polyethylene crystallization. The values of crystallization enthalpy and melt enthalpy were correlated with wood fiber content and CA content, but they were not affected by wood fiber type. The melt temperatures of polyethylene and composites were comparable. This indicates that the crystallite structure and lamellar thickness are similar. POLYM. ENG. SCI., 47:1678–1687, 2007. © 2007 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 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 score0.004
Threshold uncertainty score0.777

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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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