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Record W2089639160 · doi:10.1002/app.29679

Effect of clay surfactant type and clay content on the rheology and morphology of uncured fluoroelastomer/clay nanocomposites prepared by melt‐mixing

2009· article· en· W2089639160 on OpenAlexaff
Sriram Lakshminarayanan, Bin Lin, Genaro A. Gelves, Uttandaraman Sundararaj

Bibliographic record

VenueJournal of Applied Polymer Science · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceRheologyComposite materialNanocompositeDynamic mechanical analysisDispersion (optics)Transmission electron microscopyMontmorilloniteMixing (physics)ModulusCopolymerMorphology (biology)Pulmonary surfactantPolymerChemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

Abstract Fluoroelastomer/clay composites were prepared by melt mixing in an internal mixer using Cloisite® Nanoclays: NA, 15A, 20A, 30B, and 93A at three different concentrations viz. 2.5, 5.0, and 10.0 phr. Rheology, X‐ray diffraction (XRD), and transmission electron microscopy (TEM) were used to characterize the composites prepared. Dynamic rheological measurements showed significant increase in storage moduli ( G ′) in the terminal frequency region for the uncured composites prepared from Cloisite® 15A and 20A. At higher frequencies, organically modified nanoclays plasticize the polymer matrix leading to lower modulus values. Using all three characterization techniques, Cloisite® 15A and 20A were shown to have intercalated structure in the fluoroelastomer matrix, whereas other nanoclays were shown to have inferior dispersion. The storage modulus increases proportionally with increase in the clay loading and no clay aggregation was observed at higher loadings. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

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)0.002
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 score0.007
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations16
Published2009
Admission routes1
Has abstractyes

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