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Record W2063794423 · doi:10.1177/0021955x14566207

Morphological and mechanical characterization of foamed polyethylene via biaxial rotational molding

2014· article· en· W2063794423 on OpenAlexaff
Francisco Javier Moscoso‐Sánchez, E. Mendizábal, Carlos F. Jasso-Gastinel, Pedro Ortega‐Gudiño, Jorge Ramón Robledo‐Ortíz, Rubén González‐Núñez, Denis Rodrigue

Bibliographic record

VenueJournal of Cellular Plastics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceBlowing agentComposite materialExothermic reactionMoldMolding (decorative)Ultimate tensile strengthRelative densityHigh-density polyethyleneLow-density polyethyleneCharacterization (materials science)PolyethyleneMicrostructureThermodynamicsNanotechnology

Abstract

fetched live from OpenAlex

Foamed linear medium density polyethylene parts were prepared by rotational molding in biaxial mode, using different amounts of chemical-blowing agent (azodicarbonamide). Morphological and mechanical properties are presented and discussed in terms of foam density, cell density, average cell diameter, and open cell content. Internal air temperature of the mold was measured as a function of time. Significant differences were observed between unfoamed and foamed parts. The use of an exothermic chemical-blowing agent increased the peak internal air temperature and part cooling was slower due to the presence of gas bubbles acting as insulating material. The most important changes were observed for foam density: adding 1 phr of azodicarbonamide the density decreased from 0.931 g/cm3 (0 phr azodicarbonamide) to 0.295 g/cm3. Finally, the mechanical properties were highly influenced by azodicarbonamide content. Tensile and impact properties were correlated with part density using a simple power–law equation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.200
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2014
Admission routes1
Has abstractyes

Explore more

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