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Record W1987084165 · doi:10.1002/mats.200800074

The Ludwig‐Soret Effect on the Thermally Induced Phase Separation Process in Polymer Solutions: A Computational Study

2008· article· en· W1987084165 on OpenAlexafffund
Sureshkumar B. Kukadiya, Philip K. Chan, Mehrab Mehrvar

Bibliographic record

VenueMacromolecular Theory and Simulations · 2008
Typearticle
Languageen
FieldEngineering
TopicField-Flow Fractionation Techniques
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaRyerson University
KeywordsThermophoresisDimensionless quantityThermodynamicsBinary numberPolymerPhase (matter)Separation processTemperature gradientThermalStatistical physicsMaterials scienceChemistryPhysicsChromatographyMathematicsQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The Ludwig‐Soret effect was investigated in the thermally induced phase separation process via SD in polymer solutions under an externally imposed spatial linear temperature gradient using mathematical modeling and computer simulation. The mathematical model incorporated non‐linear Cahn‐Hilliard theory for SD, Flory‐Huggins theory for thermodynamics, and the Ludwig‐Soret effect for thermal diffusion. 2D simulation results revealed that the Ludwig‐Soret effect had negligible impact on the phase separation mechanism in binary polymer solutions under a non‐uniform temperature field, as reflected by the time evolution of the dimensionless structure factor and the transition time from the early to the intermediate stages of SD. magnified image

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.303
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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
Published2008
Admission routes2
Has abstractyes

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