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Record W2018451637 · doi:10.1002/masy.200651126

A Single‐Gallery Model for the In Situ Production of Polyethylene‐Clay Nanocomposites

2006· article· en· W2018451637 on OpenAlexaff
Abolfazl Maneshi, João B. P. Soares, Leonardo C. Simon

Bibliographic record

VenueMacromolecular Symposia · 2006
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials sciencePolymerizationPolyolefinNanocompositeIn situ polymerizationPolymer clayPolyethylenePost-metallocene catalystCoordination polymerizationPolymerChemical engineeringMetalloceneComposite materialPolymer chemistryRadical polymerization

Abstract

fetched live from OpenAlex

Abstract Polyolefin‐clay nanocomposites are finding many new applications because of their improved properties, such as high modulus, elevated scratch resistance and low gas permeability. Currently, these composites are produced by melt blending organically modified clay with polyolefins. The most challenging step in this process is the intercalation and exfoliation of the clay to produce a homogenously dispersed phase at the nanoscale. A promising alternative to melt blending is in‐situ polymerization, where the polymer is produced between the clay layers in the polymerization reactor. In‐situ polymerization of olefins with metallocene catalysts supported on clay can produce nanocomposites using conventional polymerization reactors, provided that the clay can be used as a support for the olefin polymerization catalyst. In this approach, the clay fulfills the functions of catalyst support and dispersed phase in the final nanocomposite. In this work, a mathematical model describing particle growth during in‐situ polymerization of ethylene with a metallocene catalyst supported on clay will be discussed. The model expands the approach of the multi‐grain model used in heterogeneous olefin polymerization to account for the layered structure of clays.

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 score0.013
Threshold uncertainty score0.587

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 studies0.0000.000
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 head0.012
GPT teacher head0.219
Teacher spread0.207 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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