A Critical Review of Thermodiffusion Models: Role and Significance of the Heat of Transport and the Activation Energy of Viscous Flow
Bibliographic record
Abstract
In this paper thermodiffusion models developed to estimate the thermal diffusion factor in nonideal liquid mixtures are reviewed; the merits and shortcomings of each model are discussed in detail. Most of these models are multicomponent in principle; however our focus here is on binary mixtures. Two rather different groups of models are identified: models needing a matching parameter to be obtained usually from the outside of thermodynamics, and the self-contained or independent models. Derivation of the matching parameter models using linear non-equilibrium thermodynamics and the details of how to find the matching parameters are investigated. The physical meaning of parameters such as the net heat of transport and the activation energy of viscous flow is elucidated, as the literature is overwhelmed with confusing and misleading information. The so-called dynamic and static models and their relations to the matching and non-matching parameter models are also discussed. We conclude that modeling the net heat of transport by the activation energy of self-diffusion may provide better results than approximating it by the activation energy of viscous flow. Nonetheless, the matching parameter models, which use the activation energy of viscous flow, are more dynamic and predict the thermal diffusion factor better than the non-matching parameter or static models, such as those of Kempers and Haase.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".