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Record W3115940166 · doi:10.1002/pen.25617

Rotational molding of compatibilized PA6/LLDPE blends

2020· article· en· W3115940166 on OpenAlexaff
Rosa Gabriela López Gonzaleznúñez, Roberto Carlos Vázquez Fletes, Pedro Ortega‐Gudiño, Milton Vázquez‐Lepe, Denis Rodrigue, Rubén González‐Núñez

Bibliographic record

VenuePolymer Engineering and Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversité Laval
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsMaterials scienceLinear low-density polyethyleneComposite materialPolyamideCompression moldingCompoundingMolding (decorative)MoldPlastics extrusionCompatibilizationPolyethyleneExtrusionHeat deflection temperaturePolymerPolymer blendIzod impact strength testCopolymerUltimate tensile strength

Abstract

fetched live from OpenAlex

Abstract In this work, rotational molding was used to produce parts based on a blend of linear low‐density polyethylene (LLDPE) and polyamide 6 (PA6). In particular, the concentration of PA6 (0, 10, 20, and 30% vol) with and without a compatibilizer (Surlyn 9020) was investigated via two methods: dry blending in a high‐shear mixer and melt compounding in a twin‐screw extruder followed by pulverization. To determine the efficiency of the rotomolding process, similar parts were produced via compression molding. For rotational molding, dry ice (solid CO 2 ) was used to create an inert atmosphere in the mold and the processing conditions were followed by the mold internal air temperature traces. From the samples produced, thermal, morphological, and mechanical properties were measured. The results showed that the rotomolded parts of the melt blended compounds generated smaller and better dispersed PA6 particles in the LLDPE matrix. This finer morphology led to improved mechanical properties, especially when the compatibilizer was present. But the latter was found to be more effective on the compression‐molded samples.

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 score0.089
Threshold uncertainty score0.344

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 head0.013
GPT teacher head0.220
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

Citations11
Published2020
Admission routes1
Has abstractyes

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