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Record W2030913534 · doi:10.1002/pen.23736

Microstructure and properties of poly(ethylene terephthalate)/organoclay nanocomposites prepared by water‐assisted extrusion: Effect of organoclay concentration

2013· article· en· W2030913534 on OpenAlexaff
Maryam Dini, Tahereh Mousavand, Pierre J. Carreau, Musa R. Kamal, Minh‐Tan Ton‐That

Bibliographic record

VenuePolymer Engineering and Science · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsMcGill UniversityNational Research Council CanadaPolytechnique Montréal
Fundersnot available
KeywordsOrganoclayMaterials scienceNanocompositeComposite materialExtrusionUltimate tensile strengthMicrostructureTransmission electron microscopyOxygen permeabilityChemical engineeringOxygenNanotechnology

Abstract

fetched live from OpenAlex

Poly(ethylene terephthalate) (PET)/Cloisite 30B (C30B) nanocomposites containing different concentrations of the organoclay were prepared using two different twin‐screw extrusion processes: conventional melt mixing and water‐assisted melt mixing. The reduction of the molecular weight of the PET matrix, caused by hydrolysis during the water‐assisted extrusion, was compensated by subsequent solid‐state polymerization (SSP). X‐ray diffraction, scanning electron microscopy, and transmission electron microscopy analyses showed intercalated/exfoliated morphology in all PET/C30B nanocomposites, with a higher degree of intercalation and delamination for the water‐assisted process. Rheological, thermal, mechanical, and gas barrier properties of the PET nanocomposites were also studied. Enhanced mechanical and barrier properties were obtained in PET‐C30B nanocomposites compared to the neat PET. The nanocomposites exhibited higher tensile modulus and lower oxygen permeability after SSP. The elongation at break was significantly higher for SSP nanocomposites than for nanocomposites processed by conventional melt mixing. POLYM. ENG. SCI., 54:1879–1892, 2014. © 2013 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 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 score0.016
Threshold uncertainty score0.589

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 studies0.0000.001
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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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

Citations12
Published2013
Admission routes1
Has abstractyes

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