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Record W2022379581 · doi:10.1002/pen.23963

Mixing effect on emulsion polymerization in a batch reactor

2014· article· en· W2022379581 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

VenuePolymer Engineering and Science · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMixing (physics)Materials scienceEmulsion polymerizationEmulsionPolymerizationChemical engineeringComposite materialPolymer

Abstract

fetched live from OpenAlex

The emulsion polymerization of methyl methacrylate (MMA) was carried out in a lab‐scale reactor, which was equipped with a pitched blade turbine, four baffles, a U shaped cooling coil, and a temperature controller. Potassium persulfate was used as the initiator and sodium dodecyl sulfate as the surfactant. The effects of impeller speed, mounting baffles, and reaction temperature on the monomer conversion, polymer nano particle size and number, and molecular weight were examined in detail. An increase in the impeller speed up to 250 rpm enhanced the polymer properties but further agitation reduced the conversion, particle size, and molecular weight. The installation of the baffles enhanced the particle size and molecular weight but reduced the conversion and particle number. The use of baffles resulted in a narrower size distribution throughout the polymerization process. While the particle size and molecular weight were reduced with an increase in the reaction temperature, the monomer conversion and particle number were improved. POLYM. ENG. SCI., 55:945–956, 2015. © 2014 Society of Plastics Engineers

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.209
Teacher spread0.205 · 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