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Record W2059077731 · doi:10.1021/ie060295a

Computer Simulation of Bubble-Growth Phenomena in Foaming

2006· article· en· W2059077731 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBubbleRheologyViscositySurface tensionMaterials sciencePolystyreneThermal diffusivityThermodynamicsMechanicsLiquid bubblePolymerComposite materialPhysics

Abstract

fetched live from OpenAlex

This paper discusses the research conducted to achieve an accurate bubble-growth model and simulation scheme to describe precisely the bubble-growth phenomena that occur in polymeric foaming. Using the accurately measured thermophysical and rheological properties of polymer/gas mixtures (i.e., the solubility, the diffusivity, the surface tension, the viscosity, and the relaxation time) as the inputs for computer simulation, the growth profiles for bubbles nucleated at different times were predicted and carefully compared to experimentally observed data obtained from batch foaming simulation with online visualization. A polystyrene/carbon dioxide (PS/CO 2 ) system is used herein as a case example. It was verified that the cell-growth model is capable of thoroughly depicting the growth behaviors of bubble nuclei nucleated under varying processing conditions without using any fitting parameter. These results indicate that the established model accounts for most of the physics behind the bubble-growth phenomena. Furthermore, the effects of the aforementioned thermophysical and rheological parameters on the cell-growth dynamics were demonstrated by a series of sensitivity studies.

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.

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 score0.029
Threshold uncertainty score0.521

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 studies0.0000.000
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 head0.050
GPT teacher head0.300
Teacher spread0.250 · 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