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Record W2130333496 · doi:10.1021/ma500451g

Wall Slip of Bidisperse Linear Polymer Melts

2014· article· en· W2130333496 on OpenAlexaff
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
FundersOffice of the DirectorU.S. Department of Energy
KeywordsSlip (aerodynamics)PolymerPolybutadieneMaterials scienceCouette flowShear stressShear flowThermodynamicsDispersityShear ratePolymer chemistryRheologyChemistryComposite materialMechanicsFlow (mathematics)CopolymerPhysics

Abstract

fetched live from OpenAlex

We have characterized the effect of molecular weight distribution on slip of linear 1,4-polybutadiene samples sandwiched between cover glass and silicon wafer. Monodisperse polybutadiene samples with molecular weights in the range of 4–195 kg/mol and their binary mixtures were examined at steady state in planar Couette flow using tracer particle velocimetry. Slip velocity was measured at shear rates over the range of ∼0.1–15 s –1 . Our results revealed that weakly entangled short chains play a crucial role in wall slip and flow dynamics of linear polymer melts. It was found that the critical shear stress for the onset of the transition to the strong slip regime is significantly reduced when a small amount of weakly entangled chains is added to a sample of highly entangled polymer. Within the same range of shear stresses, binary mixtures of long and short chains exhibit significantly enhanced slip compared to the moderate slip of the individual long chains. This is attributed to the reduced friction coefficient at the polymer–solid interface which is likely a complicated function of the nature of entanglement, chain adsorption, and relaxation dynamics of chains at the interface.

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 score0.463
Threshold uncertainty score0.439

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 head0.217
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 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

Citations27
Published2014
Admission routes1
Has abstractyes

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