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Record W1976443916 · doi:10.1122/1.3635384

Evaluation of molecular linear viscoelastic models for polydisperse H polybutadienes

2011· article· en· W1976443916 on OpenAlexaff
Si Wan Li, Heon E. Park, John M. Dealy

Bibliographic record

VenueJournal of Rheology · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsViscoelasticityDispersityMaterials scienceMolar massMolar mass distributionPlateau (mathematics)ThermodynamicsModulusSize-exclusion chromatographyPolymerComposite materialPolymer chemistryOrganic chemistryChemistryMathematicsPhysics

Abstract

fetched live from OpenAlex

Two tube-based molecular models, the hierarchical 3.0 model and the branch-on-branch model were evaluated for their abilities to predict the behavior of a series of polydisperse, H-shaped, 1,4-polybutadienes. The samples had been synthesized using a novel technique designed to suppress the generation of high molar mass by-products. While size exclusion chromatography data indicated that the samples were monodisperse, low molar mass by-products were later revealed by temperature gradient interaction chromatography. Viscoelastic data were obtained at temperatures ranging from −75 °C to 25 °C, and the samples were found to be thermorheologically simple. Sensitivity and uncertainty analyses revealed that among the model parameters, the value of plateau modulus has the strongest effect on model predictions. As molecular models improve, it will become ever more essential to evaluate them using accurate data on materials whose microstructures have been reliably established. This is especially important for materials that are structurally polydisperse.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.778
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.049
GPT teacher head0.282
Teacher spread0.233 · 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 designSimulation or modeling
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

Citations22
Published2011
Admission routes1
Has abstractyes

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