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Record W2056461938 · doi:10.1177/0021955x09102399

Effect of Temperature on Foaming Behaviors of Homo- and Co-polymer Polypropylene/Polydimethylsiloxane Blends with CO2

2009· article· en· W2056461938 on OpenAlexaff
WU Qing-feng, Chul B. Park, Wenli Zhu

Bibliographic record

VenueJournal of Cellular Plastics · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsMinistry of Education and Child CareUniversity of Toronto
Fundersnot available
KeywordsMaterials sciencePolypropyleneNucleationPolydimethylsiloxaneComposite materialCopolymerMaleic anhydridePolymerBlowing agentSupercritical fluidMorphology (biology)Surface tensionGlass transitionChemical engineeringPolymer chemistryPolyurethane

Abstract

fetched live from OpenAlex

Poly(dimethylsiloxane) (PDMS) was blended with two different types of polypropylene (PP). The blends were subsequently batch-foamed with supercritical CO 2 at a series of temperatures that varied by a narrow increment of 2°C to investigate the effect of the foaming temperature on foaming. In the case of the random copolymer PP, it was found that the cell density of the blends containing PDMS increased significantly and good cell structures could be obtained across a wide temperature spectrum. PDMS typically generated high CO 2 concentration and low surface tension, which positively impacted the cell nucleation. In the case of linear homopolymer PP, the addition of PDMS did not result in any pronounced improvement to cell morphology; however, at very low temperatures, much lower than the melting point, a few very small cells appeared. In both experiments, the addition of maleic anhydride grafted PP (PP-g-MAH) as a compatibilizer promoted the dispersion of PDMS and yielded a better cell morphology within a specific temperature range. Moreover, the presence of a compatibilizer enhanced the melt strength, which in turn served to broaden the processing window.

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 score0.008
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.003
GPT teacher head0.224
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

Citations17
Published2009
Admission routes1
Has abstractyes

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