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Record W4387460513 · doi:10.3390/c9040096

Graphene and Nanoclay as Processing Aid Agents: A Study on Rheological Behavior in Polystyrene

2023· article· en· W4387460513 on OpenAlexafffund
Julie Genoyer, Emna Helal, Giovanna Gutiérrez, Nima Moghimian, Éric David, Nicole R. Demarquette

Bibliographic record

VenueC – Journal of Carbon Research · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsÉcole de Technologie SupérieureNanoXplore (Canada)
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceRheologyGrapheneScanning electron microscopeViscosityPolystyreneComposite materialParticle (ecology)Particle sizeDispersion (optics)Chemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

The effectiveness of layered particles as processing aid agents in molten polystyrene was studied. Three graphene grades and two clays of different lateral size were selected for this purpose. The morphologies of the composites were observed using scanning electron microscopy. Steady shear measurements were carried out and the Carreau–Yasuda model with yield stress was applied to the experimental results. A decrease in viscosity was observed at 2 wt.% of particle content for almost all composites. The most efficient particle for reducing viscosity was found to be graphene in a loose agglomerated configuration. Graphene and clay particles with similar dispersion states had a similar effect on the viscosity, inducing a decrease by 29% and 22%, respectively, suggesting comparable efficiency as processing aid agents. The observed decrease in viscosity is attributed to the phenomenon of superlubricity, which is a lubricating mechanism that is closely linked to the atomic structure of the particles.

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)0.005
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.108
GPT teacher head0.429
Teacher spread0.321 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

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