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Record W2178843060 · doi:10.1002/macp.201500285

Vinyl pivalate Propagation Kinetics in Radical Polymerization

2015· article· en· W2178843060 on OpenAlexafffund
Otlaatla Monyatsi, Robin A. Hutchinson

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2015
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVinyl acetateChemistryPolymerizationPolymer chemistryKineticsVinyl alcoholBulk polymerizationRadical polymerizationMonomerActivation energyMethyl methacrylateArrhenius equationVinyl esterHeptaneOrganic chemistryCopolymerPolymer

Abstract

fetched live from OpenAlex

Radical propagation kinetics of the bulk homopolymerizations of vinyl pivalate (VPi) and vinyl benzoate (VBz) have been studied using pulsed‐laser polymerization (PLP) combined with size exclusion chromatography (SEC). As part of the study, the Mark–Houwink para­meters of poly(VPi) and poly(VBz) in tetrahydrofuran are determined using a triple detector SEC. The observed significant increase (by ≈ 20%) of the bulk VPi propagation rate coefficient ( k p ) as pulse repetition rate is increased from 200 to 500 Hz is similar to that reported for vinyl acetate (VAc). Data collected in the temperature range of 25–85 °C for VPi is well fit by the Arrhenius relation ln( k p /L mol −1 s −1 ) = 15.73−2093( T /K). The activation energy is similar to that found for vinyl acetate (VAc), with k p values higher by ≈ 50%. PLP studies in ethyl acetate and in heptane find no substantial solvent effect on VPi or VAc k p values. Attempts to measure the propagation kinetics of VBz by PLP are not successful, suggesting that significant radical stabilization occurs for the system. Small‐scale batch poly­merization experiments demonstrate relative polymerization rates of these vinyl ester monomers that are consistent with the PLP results. 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 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 score0.035
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

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