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Record W4280613963 · doi:10.1122/8.0000441

Linear viscoelasticity of PP/PS/MWCNT composites with co-continuous morphology

2022· article· en· W4280613963 on OpenAlexafffund
Daria Strugova, Éric David, Nicole R. Demarquette

Bibliographic record

VenueJournal of Rheology · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsÉcole de Technologie Supérieure
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialViscoelasticityRheologyAnnealing (glass)Carbon nanotubeMicrostructureScanning electron microscopePolypropylenePolystyrenePercolation thresholdPolymerElectrical resistivity and conductivity

Abstract

fetched live from OpenAlex

In this work, a study of the linear viscoelastic properties of co-continuous polypropylene/polystyrene blends filled with multiwall carbon nanotubes (MWCNTs) is presented. The YZZ rheological model [W. Yu et al., Polymer 51, 2091–2098 (2010)] is employed to correlate the rheological behavior of the blends with their microstructure and electrical properties. A test design involving a sequence of small amplitude oscillatory shear and a time sweep (simulating thermal annealing) is used to evaluate the morphology and evolution of electrical properties. It was shown that the YZZ rheological model could be successfully modified to be able to quantify a co-continuous morphology of filled composites. The calculated characteristic domain size was found to be in good agreement with the experimental data obtained via scanning electron microscopy. Furthermore, it is shown that the characteristic domain size slightly decreased after 30 min of thermal annealing. It was shown, as well, that thermal annealing promoted a reduction in the electrical percolation threshold (wt. % MWCNT) from 0.28 to 0.06.

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 categoriesInsufficient payload (model declined to judge)
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 score0.026
Threshold uncertainty score0.998

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)0.0030.000

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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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.

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

Citations10
Published2022
Admission routes2
Has abstractyes

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