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Record W1967114321 · doi:10.1021/ma040018k

Effect of Gel Content on Polymer Diffusion in Poly(vinyl acetate-<i>c</i><i>o</i>-dibutyl maleate) Latex Films

2004· article· en· W1967114321 on OpenAlexafffund
Jun Wu, J. Pablo Tomba, Mitchell A. Winnik, Rajeev Farwaha, Jude Rademacher

Bibliographic record

VenueMacromolecules · 2004
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolymerBranching (polymer chemistry)DiffusionPolymer chemistryAnalytical Chemistry (journal)Vinyl acetateChemistryMolar mass distributionMaterials scienceCopolymerChromatographyOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

We used energy transfer experiments to examine the rates of polymer diffusion in latex films of two P(VAc−DBM) copolymer samples: one of nominal M w = 250 000 (by GPC, M 250K ) and the other, the high- M sample, with a substantial (50%) gel content. An important feature of this sample is that its sol fraction had a similar molecular weight distribution (similar GPC curves) to that of the M 250K sample. Films of both samples exhibited the viscoelastic response at high frequencies expected for entangled polymers. At low frequencies, the G ‘ and G ‘ ‘ values for the high- M sample were comparable in magnitude over a wide range of frequencies and were proportional to ω 0.5 . This type of behavior is often seen as a signature of a critical gel but appears here for a mixture of gel and randomly branched polymer. Our most important finding is that the rates of intercellular polymer diffusion were very similar in latex films of the two polymers. This result indicates that the diffusion rate depends primarily on the molecular weight and degree of branching of the diffusing species and is much less sensitive to the cross-linked nature of the matrix present in one of the samples.

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 score0.008
Threshold uncertainty score0.985

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations34
Published2004
Admission routes2
Has abstractyes

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