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Record W1967190485 · doi:10.1002/pola.23560

The effect of Co(II)‐mediated catalytic chain transfer on the emulsion polymerization kinetics of methyl methacrylate

2009· article· en· W1967190485 on OpenAlexaff
Niels M. B. Smeets, Johan P. A. Heuts, Jan Meuldijk, Michael F. Cunningham, Alex M. van Herk

Bibliographic record

VenueJournal of Polymer Science Part A Polymer Chemistry · 2009
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
Fundersnot available
KeywordsChain transferChemistryEmulsion polymerizationMethyl methacrylateCatalytic chain transferPolymerizationMolar mass distributionEmulsionPolymer chemistryKineticsRadical polymerizationCatalysisLiving polymerizationChemical engineeringOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Abstract The effect the catalytic chain transfer agent, bis[(difluoroboryl) dimethylglyoximato] cobalt(II) (COBF), on the course of the ab initio emulsion polymerization of methyl methacrylate, and the product properties in terms of the molecular weight distribution were investigated. The emulsion polymerization kinetics have been studied with varying surfactant, initiator, and COBF concentrations. The experimentally determined average number of radicals per particle strongly depends on the concentration of COBF and proves to be in good agreement with the results of model calculations. The apparent chain transfer constant, determined up to high conversion, is in excellent agreement with the predicted value based on a mathematical model based on COBF partitioning and the Mayo equation. The results of this work enhance the fundamental understanding of the influence a catalytic chain transfer agent has on the course of the emulsion polymerization and the control of the molecular weight distribution. © 2009 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 47: 5078–5089, 2009

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 score0.015
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.248
Teacher spread0.241 · 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
Published2009
Admission routes1
Has abstractyes

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