MétaCan
Menu
Back to cohort
Record W1997160268 · doi:10.1002/app.40496

Impact of impeller type on methyl methacrylate emulsion polymerization in a batch reactor

2014· article· en· W1997160268 on OpenAlexafffund
Shideh Fathi Roudsari, Ramdhane Dhib, Farhad Ein‐Mozaffari

Bibliographic record

VenueJournal of Applied Polymer Science · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAgitatorImpellerRushton turbineMaterials scienceEmulsion polymerizationMethyl methacrylatePolymerTurbineComposite materialDraft tubeChemical engineeringPolymer chemistryPolymerizationThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract The emulsion polymerization of methyl methacrylate (MMA) was carried out in a lab‐scale reactor, which was equipped with a top‐entry agitator, four wall baffles, a U‐shaped cooling coil, and a temperature controller. Potassium per sulfate and sodium dodecyl sulfate as were used as the initiator and the surfactant, respectively. The experimental investigation demonstrated the impact of the impeller type (45° six pitched‐blade turbine and Rushton impeller), number of impellers (single and double impellers), and impeller speed (100–350 rpm) on the monomer conversion, polymer particles size, molecular weight, and glass transition temperature. The results revealed that the effect of the impeller speed on the characteristics of the polymer attained using the pitched‐blade turbine was more prominent than that for the Rushton turbine. It was also found that the impact of the impeller speed on the polymer characteristics was much more pronounced for the double pitched‐blade turbines rather than for the double Rushton turbines. However, more uniform size distribution was achieved with the Rushton turbine. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131 , 40496.

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)0.001
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 score0.140
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.245
Teacher spread0.239 · 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

Citations11
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied Polymer ScienceSame topicFluid Dynamics and MixingFrench-language works237,207