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Record W2312472027 · doi:10.1021/ma501320d

Wall Slip of Tridisperse Polymer Melts and the Effect of Unentangled versus Weakly Entangled Chains

2014· article· en· W2312472027 on OpenAlexafffund
S. Mostafa Sabzevari, Itai Cohen, Paula M. Wood‐Adams

Bibliographic record

VenueMacromolecules · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsConcordia University
FundersBasic Energy SciencesDivision of Chemical, Bioengineering, Environmental, and Transport SystemsNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsSlip (aerodynamics)PolymerDispersityMaterials sciencePolybutadieneThermodynamicsChemistryPolymer chemistryComposite materialChemical physicsCopolymerPhysics

Abstract

fetched live from OpenAlex

We characterized wall slip of tridisperse linear 1,4-polybutadiene on a silicon wafer in a parallel plate shear cell and tracer particle velocimetry. Tridisperse mixtures of fixed weight-average molecular weight M w and varying number-average molecular weight M n were prepared from nearly monodisperse polybutadienes. Their steady state slip behavior was examined at shear rates over the range of ∼0.1–15 s –1 . The results show that the slip behavior in the transition regime depends on M n at constant M w . This study also revealed that weakly entangled and unentangled chains in the mixtures influence wall slip differently: mixtures containing moderate amounts of weakly entangled chains exhibited enhanced slip while those containing unentangled chains did not. We explain this observation using the tube theory through the slip disentanglement mechanism proposed by Brochard and de Gennes in 1992 and conclude that the slip behavior is changed because of the force balance between the mobile and adsorbed chains and the reduced entanglement density and coil stiffness related to the incorporation of short chains of different lengths.

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 score0.166
Threshold uncertainty score0.424

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 head0.004
GPT teacher head0.206
Teacher spread0.202 · 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

Citations14
Published2014
Admission routes2
Has abstractyes

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