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Record W2002068261 · doi:10.1063/1.480925

Molecular dynamics study of diffusion in bidisperse polymer melts

2000· article· en· W2002068261 on OpenAlexfundno aff
Sandra Barsky

Bibliographic record

VenueThe Journal of Chemical Physics · 2000
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsMean squared displacementDispersityPolymerDiffusionScalingExponentThermodynamicsMolecular dynamicsMaterials scienceChemical physicsQuantum entanglementVolume fractionPolymer chemistryChemistryComputational chemistryPhysicsComposite materialMathematicsGeometry

Abstract

fetched live from OpenAlex

Molecular dynamics simulations of the diffusion coefficient of systems of polydisperse chains are presented. Each system consists of two lengths of chain of chemically identical flexible polymers. The mean square displacement of the center of mass of each species is measured as a function its length and volume fraction in the blend. The polymer lengths range from N=10 monomers per chain to N=90, about three times the entanglement length. The polymer species that comprises the bulk of the melt shows little change in behavior regardless of the length of polymer which makes up the remainder. By contrast, when a species is the minority component, its motion is significantly affected by the length of the matrix chains. When a chain is immersed in a matrix of longer chains, its diffusion coefficient is smaller than its monodisperse value; conversely when a chain is in a blend of shorter chain its diffusion coefficient increases compared to a monodisperse melt. For chains shorter than the entanglement length, the diffusion coefficient compares well to theoretical predictions. The scaling exponent of the mean square displacement of the longest polymer is found to be sublinear, unless blended with very short polymers. The scaling exponent seems to be a measurement of the entanglements that the long polymers experience.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.341

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 head0.005
GPT teacher head0.218
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

Citations20
Published2000
Admission routes1
Has abstractyes

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