Composition effect on thermophobicity of ternary mixtures: An enhanced molecular dynamics method
Bibliographic record
Abstract
Thermodiffusion or the Ludwig-Soret effect is known as the cross effect between the temperature gradient and induced separation of mixture species in multicomponent mixtures. The performance of the boundary driven non-equilibrium molecular dynamics enhanced heat exchange (eHEX) algorithm was validated by evaluating the sign and magnitude of the thermodiffusion process in methane/n-butane/n-dodecane (nC1–nC4–nC12) ternary mixtures. The eHEX algorithm consists of an extended version of the HEX algorithm with an improved energy conservation property. In addition to this, the transferable potentials for phase equilibria-united atom augmented force field was employed in all molecular dynamics (MD) simulations to accurately represent molecular interactions in the fluid. Our newly employed MD algorithm was capable to appropriately reflect the thermophobicity concept and the coupled effect of relative density and mole fraction of the mixture species on the thermodiffusion process. The separation ratio of the ternary mixture for five different compositions (at 333.15 K and 35 MPa) showed good agreement with experimental data and better accuracy in predicting the sign change of the intermediate component (nC4) as its concentration in the mixture increases, when compared to other MD models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".