Estimation of thermodiffusion coefficients in ternary associating mixtures
Bibliographic record
Abstract
Abstract Following the non‐equilibrium thermodynamics formulation and taking into account the complexities in the structure of aqueous associating mixtures, expressions are proposed to estimate the thermodiffusion coefficients in ternary associating mixtures, such as water and alcohol mixtures. The model expressions are used to estimate the thermodiffusion coefficients in methanol–ethanol–water, dimethyl sulfoxide (DMSO)–ethanol–water and DMSO–t‐butanol–water mixtures at various concentrations. The perturbed‐chain statistical associating fluid theory (PC‐SAFT) equation of state is used to obtain the mixture properties, such as the derivatives of the chemical potentials needed to evaluate the thermodiffusion coefficient expressions. The results show that at certain concentrations of one component, variation of the concentration of the other two components can cause a sign change in the thermodiffusion coefficients. While the model cannot be evaluated due to the lack of any pertinent experimental data, the model predictions may be used to choose suitable mixture compositions in space experiments to be performed onboard the International Space Station (ISS) in near future. © 2011 Canadian Society for Chemical Engineering
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".