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Record W2026391935 · doi:10.4313/teem.2014.15.5.235

Structural and Dielectric Studies of LLDPE/O-MMT Nanocomposites

2014· article· en· W2026391935 on OpenAlexafffund
Bouchaib Zazoum, Éric David, Anh Dung Ngô

Bibliographic record

VenueTransactions on Electrical and Electronic Materials · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLinear low-density polyethyleneMaterials scienceNanocompositeMasterbatchCrystallinityMontmorilloniteComposite materialScanning electron microscopeMicrostructureTransmission electron microscopyDielectricFourier transform infrared spectroscopyPolyethyleneChemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

Nanocomposites made of linear low density polyethylene (LLDPE) and organo-modified montmorillonite (O-MMT) were processed by melt compounding from a commercially available premixed LLDPE/nanoclay masterbatch, at different nanoclay loadings, by co-rotating twin-screw extruder. The morphological and dielectric properties of LLDPE/O-MMT nanocomposites were investigated to understand the structure-dielectric properties relationship in the nanocomposites. The microstructures of the materials were characterized by wide angle X-ray diffraction (WAXD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and atomic force microscopy (AFM). Initial findings by FTIR spectroscopy characterization indicated the absence of any chemical interaction between LLDPE and nanoclay during the extrusion process, while DSC showed that a 1% wt loading of nanoclay particles increased the degree of crystallinity of the nanocomposites samples. On the other hand, XRD, SEM, TEM and AFM indicated that nanoclay layers were intercalated or exfoliated in the LLDPE matrix. A correlation between the structure and dielectric properties of LLDPE/O-MMT nanocomposites was found and discussed.

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 score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.241
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.

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
Published2014
Admission routes2
Has abstractyes

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