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Record W1856086932 · doi:10.1002/mren.201500019

Using a Novel CFD Model to Assess the Effect of Mixing Parameters on Emulsion Polymerization

2015· article· en· W1856086932 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMacromolecular Reaction Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAgitatorImpellerBaffleMaterials scienceRotational speedEmulsion polymerizationComputational fluid dynamicsMixing (physics)MechanicsMethyl methacrylateVolume fractionPolymerComposite materialThermodynamicsPolymerizationMechanical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract A novel computational fluid dynamics (CFD) model was developed and validated with the experimental data to explore the effect of the agitator type, rotational speed, and the installation of baffles on the methyl methacrylate conversion, particle size and number of particles of poly methyl methacrylate (PMMA) produced in an emulsion polymerization reactor. The originality of this study was to include the reaction kinetics to the population balance through nucleation and growth rates while taking into account the velocity gradients generated by the impeller rotation inside the reactor. The number density achieved by the Rushton impeller was higher than that for the pitched blade impeller. The distribution of polymer volume fraction was more uniform with the pitched blade impeller than that for the Rushton impeller.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.494
Threshold uncertainty score0.725

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 studies0.0000.000
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 head0.035
GPT teacher head0.254
Teacher spread0.219 · 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