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Record W3009126046 · doi:10.1002/aic.16966

Concentration‐dependent molecular diffusion coefficient of gaseous ethane in liquid toluene

2020· article· en· W3009126046 on OpenAlexafffund
Mohammad Khalifi, Nasser Sabet, Mohsen Zirrahi, Hassan Hassanzadeh, Jalal Abedi

Bibliographic record

VenueAIChE Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTolueneDiffusionMass transfer coefficientChemistryMolecular diffusionMass transferThermodynamicsEffective diffusion coefficientDissolutionFlux (metallurgy)Mass fluxAnalytical Chemistry (journal)Diffusion layerGrain boundary diffusion coefficientMass diffusivityChromatographyPhysical chemistryThermal diffusivityOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract We report new experimental data on concentration‐dependent molecular diffusion coefficient of ethane in toluene at temperatures ranging from 21 to 125°C and pressures up to 4.14 MPa. An analytical model has also been developed for estimation of the diffusion coefficient utilizing the experimental data of the interface velocity as a result of swelling and the rate of gas dissolution in the liquid phase. It is shown that the diffusion coefficient of ethane in toluene is dependent on the initial mass fraction of the gaseous component in the liquid. In addition, the effect of concentration dependency of the molecular diffusion coefficient on diffusive mass flux is quantified. The results reveal that the assumption of a constant diffusion coefficient introduces ~10–60% error in calculation of diffusional mass transfer flux. The developed methodology finds application in estimation of the concentration‐dependent molecular diffusion coefficient of gases in liquids.

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 score0.522
Threshold uncertainty score0.366

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 head0.008
GPT teacher head0.210
Teacher spread0.203 · 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
Published2020
Admission routes2
Has abstractyes

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