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Record W2566072425

Theoretical Investigation of Thermodiffusion (Soret Effect) in Multicomponent Mixtures

2011· dissertation· en· W2566072425 on OpenAlex
Abbasi Alireza

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTSpace · 2011
Typedissertation
Languageen
FieldEngineering
TopicField-Flow Fractionation Techniques
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsThermophoresisMaterials scienceThermodynamicsStatistical physicsPhysicsThermal
DOInot available

Abstract

fetched live from OpenAlex

Thermodiffusion is one of the mechanisms in transport phenomena in which molecules are transported in a multicomponent mixture driven by temperature gradients. Thermodiffusion in associating mixtures presents a larger degree of complexity than non-associating mixtures, since the direction of flow in associating mixtures may change with variations in composition and temperature. In this study a new activation energy model is proposed for predicting the ratio of evaporation energy to activation energy. The new model has been implemented for prediction of thermodiffusion for acetone-water, ethanol-water and isopropanol-water mixtures. In particular, a sign change in the thermodiffusion factor for associating mixtures has been predicted, which is a major step forward in modeling of thermodiffusion for associating mixtures. In addition, a new model for the prediction of thermodiffusion coefficients for linear chain hydrocarbon binary mixtures is proposed using the theory of irreversible thermodynamics and a kinetics approach. The model predicts the net amount of heat transported based on an available volume for each molecule. This model has been found to be the most reliable and represents a significant improvement over the earlier models. Also a new approach to predicting the Soret coefficient in binary mixtures of linear chain and aromatic hydrocarbons using the thermodynamics of irreversible processes is presented. This approach is based on a free volume theory which explains the diffusivity in diffusion-limited systems. The proposed model combined with the Shukla and Firoozabadi model has been applied to predict the Soret coefficient for binary mixtures of toluene and n-hexane, and benzene and n-heptane. Comparisons of theoretical results with experimental data show a good agreement. The proposed model has also been applied to estimate thermodiffusion coefficients of binary mixtures of n-butane & carbon dioxide and n-dodecane & carbon dioxide at different temperature. The results have also been incorporated into CFD software FLUENT for 3-dimensional simulations of thermodiffusion and convection in porous media. The predictions show the thermodiffuison phenomenon is dominant at low permeabilities (0.0001 to 0.01), but as the permeability increases convection plays an important role in establishing a concentration distribution. Finally, the activation energy in Eyring’s viscosity theory is examined for associating mixtures. Several methods are used to estimate the activation energy of pure components and then extended to mixtures of linear hydrocarbon chains. The activation energy model based on alternative forms of Eyring’s viscosity theory is implemented to estimate the thermodiffusion coefficient for hydrocarbon binary mixtures. Comparisons of theoretical results with the available thermodiffusion coefficient data have shown a good performance of the activation energy model.

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 score0.044
Threshold uncertainty score0.812

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 head0.267
Teacher spread0.259 · 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