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Record W2005781640 · doi:10.1002/cjce.21809

Thermodiffusion effect for a non‐associating mixture in a multi layered system of porous media and fluid layers heated from above

2013· article· en· W2005781640 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicField-Flow Fractionation Techniques
Canadian institutionsToronto Metropolitan University
FundersCanadian Space Agency
KeywordsThermophoresisPorous mediumMaterials sciencePorosityLayer (electronics)ThermodynamicsBinary numberTemperature gradientSign (mathematics)Composite materialNanotechnologyNanoparticleMeteorology

Abstract

fetched live from OpenAlex

Abstract A numerical simulation has been conducted to investigate the thermodiffusion phenomenon in a porous layer sandwiched between two liquid layers that are wetted with water alcohol mixtures at different water concentrations. Two different binary mixtures with different Soret coefficients have been used in the entire system, one with a negative Soret coefficient and the other one with a positive sign. The results show that the direction of the component migration in a porous layer depends on the sign of the Soret coefficient. For a binary mixture with a negative Soret coefficient, such as 10% isopropanol and 90% water, the heavier species move in the direction of the hot surface, while for a mixture with a positive Soret effect, such as 50% isopropanol and 50% water, the heavier species migrate toward the colder surface. To reduce the gravity effect, the cavity was heated from the top with different temperatures ranging from 5 to 20 K.

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.

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 score0.467
Threshold uncertainty score0.529

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 head0.181
Teacher spread0.176 · 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