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Record W2099701592 · doi:10.1002/cjce.5450800610

Polymer‐Clay Nanocomposites Based on Blends of Polyamide‐6 and Polyethylene

2002· article· en· W2099701592 on OpenAlexafffundvenue
M. Mehrabzadeh, Musa R. Kamal

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2002
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePolyamideDifferential scanning calorimetryHigh-density polyethyleneNanocompositePolyethyleneScanning electron microscopeFourier transform infrared spectroscopyCrystallizationMontmorilloniteComposite materialPolymer clayLow-density polyethylenePolymerChemical engineering

Abstract

fetched live from OpenAlex

Abstract The preparation of polyamide‐6/clay, high‐density polyethylene/clay, and high‐density polyethylene/ polyamide‐6/clay nanocomposites is considered. X‐ray diffraction (XRD), differential scanning calorimetry (DSC), and Fourier Transform Infrared (FTIR) measurements show that the clay enhances the crystallization of the γ‐form of polyamide‐6. The clay also acts as a nucleation agent and causes a reduction of spherulitte size. Scanning electron microscopy (SEM) analysis of fracture surfaces shows that the clay reduces the PA‐6 particle size in the HDPE/PA‐6/clay nanocomposites and changes the morphology. Mechanical properties and the effect of maleated polyethylene are also reported.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.174
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), 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

Citations75
Published2002
Admission routes3
Has abstractyes

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