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Record W2777257165 · doi:10.1002/app.46176

Impact of compression molding conditions on the thermal and mechanical properties of polyethylene

2017· article· en· W2777257165 on OpenAlexafffund
Azar Shamloo, Babak Fathi, Saïd Elkoun, Denis Rodrigue, Armand Soldera

Bibliographic record

VenueJournal of Applied Polymer Science · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversité LavalUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de Sherbrooke
KeywordsMaterials scienceComposite materialCompression moldingUltimate tensile strengthMolding (decorative)Flexural strengthDifferential scanning calorimetryPolyethyleneFlexural modulusMoldCompression (physics)Melting pointThermodynamics

Abstract

fetched live from OpenAlex

ABSTRACT Compression molding is a current technique in polymer processing. Despite numerous studies, effect of molding pressure on physical properties has surprisingly not been fully investigated. In this study, the thermal and mechanical behavior of the compression‐molded polyethylene were thus explored to better grasp the relationship between processing parameters and ensuing properties. The effect of the molding temperature, pressure, cooling rate, and temperature profile on the tensile and flexural moduli as well as melting point of polyethylene was studied. We conclude that higher tensile and flexural moduli are obtained by increasing pressure and molding temperature, as well as decreasing the cooling rate. Our results were corroborated by X‐ray diffraction and differential scanning calorimetry measurements. Moreover, the use of a temperature gradient with different temperatures for the upper and bottom plates of the mold leads to asymmetric samples whose tensile and flexural moduli are improved. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135 , 46176.

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)0.001
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 score0.005
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.040
GPT teacher head0.295
Teacher spread0.255 · 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

Citations15
Published2017
Admission routes2
Has abstractyes

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