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Record W3138264451 · doi:10.1002/app.50742

Effect of Back‐layer on seal performance of multilayer polyethylene‐based sealant films

2021· article· en· W3138264451 on OpenAlexaff
Zahra Kanani Aghkand, Amir Saffar, Abdellah Ajji

Bibliographic record

VenueJournal of Applied Polymer Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceComposite materialLow-density polyethylenePolyethyleneLinear low-density polyethyleneLayer (electronics)SealantViscoelasticitySeal (emblem)High-density polyethylene

Abstract

fetched live from OpenAlex

Abstract This work studies effects of back‐layer materials, thickness of sealant layer, and sealing condition on seal performance of multilayer polyethylene‐based films. Multilayer films with back‐layers of high‐density polyethylene (HDPE), or low‐density polyethylene (LDPE), or linear low‐density polyethylene (LLDPE) were produced with different thicknesses of the metallocene layer. It was found that increasing the thickness of the metallocene layer improved hot tack properties. In addition, films with back‐layers of LLDPE or LDPE showed higher hot tack strength compared to those with HDPE back‐layer. Increasing sealing temperature reduced significantly the hot tack strength and its dependency on metallocene layer thickness. It was found that increasing delay time after sealing, before peeling test, increased hot tack strength, but the rate of hot tack evolution and the type of peeling behavior were considerably affected by the type of back‐layer material. The effect of dwell time was also examined, and it was observed that increasing dwell time in the studied range did not affect the hot tack evolution. The mechanisms involved in the development of hot tack evolution were discussed, and it was shown that the back‐layer effects can be explained by bulk viscoelastic energy dissipation theory.

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)0.002
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 score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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