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Record W2033286369 · doi:10.1021/ie060105w

A Microcellular Foaming Simulation System with a High Pressure-Drop Rate

2006· article· en· W2033286369 on OpenAlexaff
Qingping Guo, Jin Wang, Chul B. Park, Masahiro Ohshima

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDrop (telecommunication)Pressure dropMaterials sciencePolystyreneDrop impactNucleationBubbleBlowing agentExpanded polystyreneComposite materialPolymerThermodynamicsMechanicsComputer science

Abstract

fetched live from OpenAlex

In this paper, we undertook an experimental and theoretical analysis of the pressure-drop behaviors of a batch foaming system with a visualization window that was designed for microcellular foaming simulation. A polystyrene (PS)−CO 2 system was used in the experiment and analysis. The maximum pressure-drop rate achievable was 2.5 GPa/s from the designed system. Some experimental simulation results at high pressure-drop rates and at low pressure-drop rates are also discussed. We observed that the application of a higher pressure-drop rate results in a higher cell density (and, thereby, a smaller cell size) for plastic foams. This confirms that the pressure-drop rate is one of the most important parameters to control the cell density of plastic foams. In addition, the results show that the content of the blowing agent (CO 2 ) dissolved into a given polymer has a significant effect on bubble nucleation and growth.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.261
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations114
Published2006
Admission routes1
Has abstractyes

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