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Record W2761127718 · doi:10.1002/pen.24725

Thermal analysis of foamed polyethylene rotational molding followed by internal air temperature profiles

2017· article· en· W2761127718 on OpenAlexaff
Rubén González‐Núñez, Francisco Javier Moscoso‐Sánchez, Jacobo Aguilar, Rosa G. López‐GonzálezNúñez, Jorge Ramón Robledo‐Ortíz, Denis Rodrigue

Bibliographic record

VenuePolymer Engineering and Science · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversité Laval
FundersConsejo Nacional de Ciencia y Tecnología, Guatemala
KeywordsMaterials scienceDifferential scanning calorimetryBlowing agentMolding (decorative)PolymerExothermic reactionComposite materialPolyethyleneHigh-density polyethyleneCrystallizationThermal analysisThermal decompositionGlass transitionThermalChemical engineeringThermodynamicsOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

In this study, the internal air temperature (IAT) profile was measured to analyze the thermal behavior of a polymer for a complete rotational molding cycle. Foamed and unfoamed linear low density polyethylene parts were produced by biaxial rotational molding using a chemical blowing agent based on azodicarbonamide at different concentrations (0, 0.15, 0.25, 0.50, 0.75, and 1.0% wt) with different oven temperatures (270, 280, and 285°C). The analysis proposed is based on the temperature profiles and their derivatives to better determine the different transitions occurring in a complete molding cycle. The analysis is completed with differential scanning calorimetry (DSC) to get more information related to the dynamics of the different processing stages like polymer melting, exothermic decomposition of the chemical blowing agent, and polymer crystallization. The results obtained show a good agreement between the melting and crystallization temperatures from IAT derivatives and DSC. POLYM. ENG. SCI., 58:E235–E241, 2018. © 2017 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 score0.058
Threshold uncertainty score0.468

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 science0.0010.000
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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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 routes1
Has abstractyes

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