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Record W1989050279 · doi:10.1002/app.25622

Differential microemulsion polymerization of styrene: A mathematical kinetic model

2007· article· en· W1989050279 on OpenAlexaff
Guangwei He, Qinmin Pan, Garry L. Rempel

Bibliographic record

VenueJournal of Applied Polymer Science · 2007
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicroemulsionStyrenePolystyreneNucleationPolymerizationMaterials scienceEmulsion polymerizationPolymer chemistryParticle sizeKinetic energyChemical engineeringPulmonary surfactantHomogeneousParticle (ecology)ThermodynamicsCopolymerChemistryPhysical chemistryComposite materialPolymerPhysics

Abstract

fetched live from OpenAlex

Abstract A mathematical model has been developed for the differential microemulsion polymerization of styrene. In the model, both homogeneous and heterogeneous nucleation mechanisms are considered. It is confirmed that the predictions of the particle size and the fractional conversion by the model are in good agreement with the experimental data. It is indicated that the particle size of polystyrene decreases with an increase in the amount of the initiator or surfactant. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.239
Teacher spread0.230 · 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

Citations16
Published2007
Admission routes1
Has abstractyes

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