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Record W1969507763 · doi:10.3139/217.2238

Rheology and Processing of HDPE/Wood Flour Composites

2009· article· en· W1969507763 on OpenAlexaff
Cristiano Ribeiro de Santi, Elias Hage, J. Vlachopoulos, Carlos A. Correa

Bibliographic record

VenueInternational Polymer Processing · 2009
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceExtrusionComposite materialHigh-density polyethyleneRheologyPlastics extrusionWood flourFlexural strengthExtrusion mouldingLubricantComposite numberFlexural modulusRheometerDie (integrated circuit)Linear low-density polyethylenePolyethylene

Abstract

fetched live from OpenAlex

Abstract Rheological and mechanical properties of extruded wood plastic composites have been investigated for the purpose of providing a better understanding of processing conditions in profile extrusion for this novel class of thermoplastics composites. The basic formulations prepared for the study were comprised of 60 wt.% HDPE, with or without LLDPE-MAH as coupling agent, compounded with up to 40 wt.% of wood flour and lubricant. Capillary and rotational rheometers were employed to measure the rheological properties of the melt composite. A cooled die at the end of a single screw extruder was used for the extrusion of rectangular profiles having smooth surfaces and edges. Finite element flow simulation was used for the interpretation of the experimental data. Once extrusion steady conditions were achieved in the die, linear output was monitored and profile samples were cut for determination of flexural modulus and strength. The results showed that flexural properties can be influenced by many factors such as, coupling agents, lubricants, processing conditions, die pressure drop and cooling at the die. Linear speed of HDPE wood-plastic profile extrusion and end-use properties are strongly influenced by combination of these factors.

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 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 score0.045
Threshold uncertainty score0.554

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 communication0.0000.001
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 head0.009
GPT teacher head0.266
Teacher spread0.257 · 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 teacher head, 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

Citations32
Published2009
Admission routes1
Has abstractyes

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