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Record W2003805700 · doi:10.1002/pen.20592

Surface tearing and wall slip phenomena in extrusion of highly filled HDPE/wood flour composites

2006· article· en· W2003805700 on OpenAlexaff
Velichko Hristov, E. Takács, J. Vlachopoulos

Bibliographic record

VenuePolymer Engineering and Science · 2006
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceDie swellComposite materialHigh-density polyethyleneExtrusionRheologyWood flourSlip (aerodynamics)Shear rateTearingCapillary actionShear (geology)Polyethylene

Abstract

fetched live from OpenAlex

Abstract The extrudate surface tearing of highly filled high‐density polyethylene (HDPE)/wood flour composites has been investigated in relation to the rheological properties and the wall slip phenomenon in capillary dies. Rotational and capillary rheometers were employed to measure the rheological properties. Mooney analysis was used for determination of wall slip velocity. The results showed considerable increase of storage modulus, dynamic and shear viscosity with increasing wood flour loading. It was also found that all wood filled composites did not obey the Cox–Merz rule. The wall slip velocity depends on wood filler content and shear rate. Generally, with increasing shear rate the slip velocity sharply increases leading to plug‐like flow. It was observed that the surface of the extrudates becomes smoother with increase in shear rate and wood flour content. POLYM. ENG. SCI., 46:1204–1214, 2006. © 2006 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.188
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations102
Published2006
Admission routes1
Has abstractyes

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