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Record W2089710569 · doi:10.1002/mats.200700060

Stable Free Radical Polymerization in Emulsion: Modeling the Thermodynamics of Monomer Transfer between Droplets and Particles

2008· article· en· W2089710569 on OpenAlexafffund
Jordan Pohn, Catherine Buragina, Michael K. Georges, Barkev Keoshkerian, Michael F. Cunningham

Bibliographic record

VenueMacromolecular Theory and Simulations · 2008
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsXerox (Canada)University of TorontoQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmulsion polymerizationPolymerizationMonomerPolymerKineticsRadical polymerizationParticle (ecology)Polymer chemistryChain transferStyreneChemistryKinetic chain lengthMaterials scienceThermodynamicsChemical engineeringCopolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A mathematical model is developed from polymer solution thermodynamics and stable free radical polymerization kinetics to predict the monomer swelling behavior of large droplets and small particles that coexist during the seeded polymerization of styrene in emulsion. This model is used to predict the sensitivity of polymerized latex stability (based on the persistence of large particles) to changes in seed particle size and final latex target molecular weight. Simulation results show that the use of small seed particles (diameter ≤ 50 nm) in recipe formulation will lead to polymerization occurring preferentially in the large droplets, and offers theoretical evidence that TEMPO‐mediated ab initio emulsion polymerizations will not be feasible. Our predictions are consistent with experimental evidence suggesting that the presence of large particles leads to the formation of undesirable coagulum in the final product. magnified image

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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

Citations12
Published2008
Admission routes2
Has abstractyes

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