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Record W2593611616 · doi:10.1177/8756087917699246

Role of molecular architecture in interfacial self-adhesion of polyethylene films

2017· article· en· W2593611616 on OpenAlexafffund
Zahra Najarzadeh, Abdellah Ajji

Bibliographic record

VenueJournal of Plastic Film & Sheeting · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaPolytechnique Montréal
KeywordsPolyethyleneMaterials scienceAdhesionLinear low-density polyethyleneMolar mass distributionLow-density polyethyleneChemical engineeringReptationPolymer chemistryPolymerComposite materialGel permeation chromatography

Abstract

fetched live from OpenAlex

The influence of molecular architecture on interfacial self-adhesion above polyethylene film melt temperature was examined in this study. The investigated molecular structures include molecular weight (M w ), molecular weight distribution, long chain branch amount and distribution and short chain branch among and along polyethylene chains. The long chain branches concentration was quantified using gel permeation chromatography and short branches concentration using nuclear magnetic resonance techniques. The adhesion strength was measured immediately after melt bonding using a T-Peel test. The results showed that increasing M w resulted in higher adhesion strength in linear metallocene ethylene α-olefins. Low long chain branch concentrations hinder reptation motion and diffusion, and result in lower adhesion strength. Low density polyethylene with highly branched chains yielded very low self-adhesion. A drastic difference in adhesion strength between metallocene and conventional linear low density polyethylene is attributed to homogeneity versus heterogeneity of composition distribution. The low interfacial self-adhesion in conventional polyethylene was concluded to be due to enrichment of highly branched low molecular weight chains at the film surface. These segregated chains at the interface diffuse before the high molecular weight chains located in the bulk.

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)0.001
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 score0.008
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.008
GPT teacher head0.229
Teacher spread0.222 · 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

Citations14
Published2017
Admission routes2
Has abstractyes

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