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Record W2055432901 · doi:10.1021/ie070311j

Effect of Molecular Weight on the Surface Tension of Polystyrene Melt in Supercritical Nitrogen

2007· article· en· W2055432901 on OpenAlexafffund
H. Park, C. B. Park, C. Tzoganakis, P. Chen

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2007
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of TorontoUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolystyreneSurface tensionSupercritical fluidDispersityPolymerMaximum bubble pressure methodPolymer chemistryDrop (telecommunication)Materials scienceChemistryThermodynamicsComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

This paper presents experimental results on the effect of molecular weight on the surface tension of polystyrene melt in supercritical nitrogen. The surface tension was determined by the axisymmetric drop shape analysis-profile (ADSA-P) method, for which a high-pressure and high-temperature cell was used to form pendant drops of the polystyrene melt. For two monodisperse polystyrenes of M w ∼ 100 000 and 400 000 and one polydisperse polystyrene, a linear relationship was found between surface tension and temperature and between surface tension and pressure within a temperature range of 170−210 °C and a pressure range of 500−2000 psi. With an increase in pressure or temperature, the surface tension of all three polystyrenes decreases. Monodisperse polystyrene of a higher molecular weight has a higher surface tension by 6−9 mJ/m 2 under all experimental conditions. The surface tension dependence on temperature and on pressure is more significant for the higher molecular weight polystyrene. For the polydisperse polystyrene, high surface tension values seem to be related predominantly to its high molecular weight portion of polystyrene molecules. An empirical equation, the Mecleod relation, was used to relate surface tension with the density difference between the polymer and supercritical nitrogen satisfactorily.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 integrity0.0000.001
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 head0.029
GPT teacher head0.312
Teacher spread0.283 · 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

Citations24
Published2007
Admission routes2
Has abstractyes

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