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Record W4303980847 · doi:10.3390/polym14194124

Mechanical and Thermal Properties of Functionally Graded Polyolefin Elastomer Foams

2022· article· en· W4303980847 on OpenAlexafffund
Ehsan Rostami‐Tapeh‐Esmaeil, Sahar Shojaei, Denis Rodrigue

Bibliographic record

VenuePolymers · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyolefinElastomerMaterials scienceComposite materialThermalPolymer science

Abstract

fetched live from OpenAlex

In this work, uniform and graded polyolefin elastomer (POE) foams were prepared using a single-step technology based on a fixed chemical blowing agent (azodicarbonamide) concentration of 4 phr (parts per hundred rubber). The effect of molding temperature, including the average temperature (Tavg) and temperature difference (ΔT), on the foams’ morphology, mechanical properties (tension, compression and hardness) and thermal conductivity was investigated. Two series of samples were produced by fixing Tavg with different ΔT or setting different ΔT, leading to different Tavg. The morphological analyses showed that two or three regions inside the foams were produced depending on the molding conditions, each region having different cellular structure in terms of cell size, cell density and cell geometry. The results obtained for the conditions tested showed a range of density (0.55–0.72 g/cm3), tensile modulus (0.44–0.70 MPa) and compression elastic modulus (0.35–0.71 MPa), with a thermal conductivity between 0.125 and 0.180 W/m.K. Based on the information provided, it can be concluded that the foam’s properties can be easily controlled by the cellular structure and that graded samples are more interesting than uniform ones, especially for thermal insulation applications, such as packaging, construction, transportation, automotive and aerospace industries.

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 score0.003
Threshold uncertainty score0.955

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)0.0010.000

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 head0.014
GPT teacher head0.193
Teacher spread0.179 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

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