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Record W2805824605 · doi:10.1002/app.46563

Rheological characterization of polyethylene/polyester recycled tire fibers/ground tire rubber composites

2018· article· en· W2805824605 on OpenAlexafffund
Siavosh Moghaddamzadeh, Denis Rodrigue

Bibliographic record

VenueJournal of Applied Polymer Science · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialRheologyExtrusionPolyethyleneDynamic mechanical analysisNatural rubberMaleic anhydrideLinear low-density polyethyleneDynamic modulusHigh-density polyethylenePolyesterPolymerCopolymer

Abstract

fetched live from OpenAlex

ABSTRACT The rheological behavior of polyester recycled tire fibers (RTF) mixed with ground tire rubber and linear low density polyethylene (LLDPE) with and without styrene–ethylene–butylene–styrene grafted maleic anhydride (SEBS‐ g ‐MA) as a compatibilizer was investigated in the melt (small amplitude oscillatory shear) and solid (dynamic mechanical analysis) states. In particular, the effect of RTF content (10, 25, and 50 wt %), extrusion screw speed (110, 180, and 250 rpm), and temperature profiles (extrusion and injection molding) was studied. In general, it was found that the rheological properties in the melt state (modulus and viscosity) of the uncompatibilized samples increased with RTF content, but higher values were obtained when SEBS‐ g ‐MA was added due to better interfacial coupling. Although similar results were obtained in the solid state, it was shown that melt rheology can better explain the variations as the measurements are more sensitive to the interface quality since the matrix contribution is less important. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135 , 46563.

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)0.001
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 score0.011
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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

Citations14
Published2018
Admission routes2
Has abstractyes

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