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Record W2334055250 · doi:10.1166/jnn.2006.094

Polyethylene/Clay Nanocomposites Prepared by Polymerization Compounding Method

2006· article· en· W2334055250 on OpenAlexaff
Asmaa Gaboune, Suprakas Sinha Ray, A. Aı̈t-Kadi, Bernard Riedl, Mosto Bousmina

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2006
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNanocompositeMaterials sciencePolyethyleneHigh-density polyethylenePolymerizationMontmorilloniteCompoundingComposite materialAdsorptionUltimate tensile strengthChemical engineeringDynamic mechanical analysisPolymerPolymer chemistryOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

A new technique for the preparation of high density polyethylene/clay nanocomposite, "polymerization compounding," is reported. This technique was based on the chemical anchoring of a Ziegler-Natta catalyst on organically modified clay surface containing an ammonium cation bearing primary hydroxyl groups. The polymerization of ethylene was initiated after adequate activation and the growing polyethylene chains are directly adsorbed on to the clay surface through the hydroxyl-functionalized surfactant. Finally, the nanocomposite was prepared by diluting polyethylene adsorbed clay in the high density polyethylene (HDPE) matrix using a batch mixer at 180 degrees C. The as-synthesized nanocomposite was typically characterized by X-ray diffraction (XRD) and transmission electron microscopy (TEM) that revealed the formation of intercalated nanocomposite. Tensile property measurements exhibit substantial increase in stiffness (approximately 50%) and strength (approximately 20%) of nanocomposite as compared to that of neat HDPE. Dynamic mechanical analysis under molten state revealed 25% increase in storage modulus when compared to that of neat HDPE.

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)0.001
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 score0.015
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.236
Teacher spread0.231 · 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
Published2006
Admission routes1
Has abstractyes

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