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Record W2010017266 · doi:10.1002/pen.20044

Effect of molecular structure on the rotational molding characteristics of ultra‐low‐density ethylene‐α‐olefin copolymers

2004· article· en· W2010017266 on OpenAlexafffund
Wanyu Wang, Marianna Kontopoulou

Bibliographic record

VenuePolymer Engineering and Science · 2004
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsMaterials scienceComonomerCopolymerCoalescence (physics)Low-density polyethyleneMelt flow indexComposite materialEthyleneElastomerPolyolefinMelting pointMolding (decorative)Particle sizePolymer chemistryPolymerChemical engineeringCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The rotational molding characteristics of two ultra‐low‐density ethylene‐α‐olefin copolymers with elastomeric properties (polyolefin plastomers), made by metallocene catalysts, were investigated with the purpose of examining the effect of molecular structure on their processability. Particle coalescence and densification experiments revealed that higher comonomer content resulted in slower rates of coalescence and densification, thus affecting bubble content. Based on detailed material characterization, slower densification was attributed to the presence of broad melting endotherms, higher viscosity immediately after the melting transition, and higher melt elasticity. Investigation of the effect of particulate form revealed that use of powders instead of micropellets resulted in the formation of fewer bubbles on the outer surface, because of the broader particle size distribution, leading to improved appearance, better mechanical properties, and reduced overall processing time. Rotomolded parts displayed excellent impact properties and a high degree of flexibility. Polym. Eng. Sci. 44:496–508, 2004. © 2004 Society of Plastics Engineers.

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 score0.004
Threshold uncertainty score0.419

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 head0.003
GPT teacher head0.198
Teacher spread0.195 · 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

Citations9
Published2004
Admission routes2
Has abstractyes

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