MétaCan
Menu
Back to cohort
Record W2053721386 · doi:10.1002/pen.21851

Effect of molecular structure and rheology on the compression foam molding of ethylene‐α‐olefin copolymers

2011· article· en· W2053721386 on OpenAlexafffund
Ying Zhang, Marianna Kontopoulou, Mahmoud Ansari, Savvas G. Hatzikiriakos, Chul B. Park

Bibliographic record

VenuePolymer Engineering and Science · 2011
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComonomerMaterials scienceBlowing agentOcteneCopolymerRheologyComposite materialCompression moldingPolymerPolyurethane

Abstract

fetched live from OpenAlex

Abstract The morphology and mechanical properties of foams made out of a series of ethylene‐α‐olefin copolymers having well‐characterized rheological properties were investigated. A compression foaming molding technique was implemented, using azodicarbonamide as the blowing agent. The polymers differed in the amount of comonomer contained (resulting in a range of densities), type of comonomer (octene vs. butene) and molecular weight, resulting in variable thermal properties and different rheological responses under shear and extensional flow. The results showed that the majority of the octene‐based copolymers with comparable rheological properties had similar foam morphology. A distinct behaviour was observed for the butene‐based copolymer, as well as the octene‐containing one having the lowest density and lowest melting/crystallization points. The poor foamability of these grades was attributed to their differences in extensional and thermal properties, respectively. Increasing density resulted in a higher secant modulus of the foamed samples. POLYM. ENG. SCI., 2011. © 2011 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 score0.005
Threshold uncertainty score0.339

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 studies0.0000.001
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 head0.006
GPT teacher head0.205
Teacher spread0.200 · 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
Published2011
Admission routes2
Has abstractyes

Explore more

Same venuePolymer Engineering and ScienceSame topicPolymer Foaming and CompositesFrench-language works237,207