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Record W2080048864 · doi:10.1002/pen.20398

LLDPE‐based mono‐ and multilayer blown films: Property enhancement through coextrusion

2005· article· en· W2080048864 on OpenAlexafffund
Saïd Elkoun, Michel A. Huneault, K. McCormick, F. Puterbaugh, L. Kale

Bibliographic record

VenuePolymer Engineering and Science · 2005
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsNova Chemicals (Canada)National Research Council Canada
FundersNOVA Chemicals
KeywordsLinear low-density polyethyleneMaterials science1-OcteneComposite materialCopolymerPolyethyleneMonolayerLayer (electronics)OctenePolymerNanotechnology

Abstract

fetched live from OpenAlex

Abstract In this study we investigated the performance of multilayer coextruded linear low‐density polyethylene (LLDPE) blown films. Five‐layer films were compared with monolayer dry‐blended films, and the effects of layer composition and layout on the end‐use properties of the coextruded films were highlighted. Three different LLDPEs were used: a conventional Ziegler‐Natta LLDPE gas phase butene copolymer, an advanced Ziegler‐Natta LLDPE solution octene copolymer, and a single‐site LLDPE solution octene copolymer. Numerous five‐layer coextruded structures comprising the single‐site resin and the other two Ziegler‐Natta resins were produced. The coextruded structures composed of the LLDPE butene and the single‐site resin yielded improved end‐use properties relative to the monolayer‐blended films. This result was ascribed to the presence of interfacial transcrystalline layers. Also, blends of the single‐site LLDPE and the advanced Ziegler‐Natta LLDPE octene resins within selected layers of coextruded films showed slightly enhanced tear resistance. Finally, it was found that haze was significantly reduced when the outside layers were composed of the single‐site resin. POLYM. ENG. SCI., 45:1222–1230, 2005. © 2005 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 score0.125
Threshold uncertainty score0.406

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 communication0.0000.001
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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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
Published2005
Admission routes2
Has abstractyes

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