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Record W4285464460 · doi:10.1002/cjce.24145

Emulsion polymerization of vinylidene fluoride: Effects of mixing and reaction conditions on the initial rate of polymerization

2021· article· en· W4285464460 on OpenAlexvenueno aff
Ana Carolina Mendez Ecoscia, Nida Sheibat‐Othman, Timothy F. L. McKenna

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsPolymerizationEmulsion polymerizationNucleationMaterials sciencePrecipitation polymerizationChain transferChemical engineeringPolymer chemistryReaction rateCoagulationBulk polymerizationMixing (physics)FluorideChemistryRadical polymerizationPolymerOrganic chemistryCatalysisComposite materialInorganic chemistry

Abstract

fetched live from OpenAlex

Abstract In order to contribute to a better understanding of the emulsion polymerization of vinylidene fluoride (VDF), an experimental study under conditions of temperature and pressure similar to those found in industrial processes was carried out. It is shown that the initial rate of polymerization is strongly influenced by the agitation of the reactor, with the rate of reaction increasing as the rate of agitation increases. In addition, using a more efficient impeller also increases the polymerization. It is proposed that this is due to mass transfer limitations. This idea is confirmed by a similar dependence of the average molecular weight on agitation. Experimental data also shows that particles are formed by homogeneous coagulative nucleation. It is proposed that particle nucleation occurs throughout the polymerization, and that a competition between controlled coagulation and particle generation governs the rate of reaction.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.210
Teacher spread0.202 · 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 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

Citations13
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicInnovative Microfluidic and Catalytic Techniques InnovationFrench-language works237,207