A novel EOS that combines van der Waals and Dieterici potentials
Bibliographic record
Abstract
Abstract A novel approach is proposed for solving the problem of the overestimation of the liquid–liquid equilibrium (LLE) range in mixtures typically associated with the use of equations of state (EOS) of the van der Waals type. This approach modifies the internal EOS structure by combining the van der Waals and the Dieterici forms. It advantageously uses the drawback of the Dieterici form, which strongly underestimates the LLE range. Thus, the new EOS is capable of generating an appropriate balance for the representation of vapor–liquid equilibria (VLE) and LLE critical data and therefore represents a major improvement in comparison with typical van der Waals–type EOSs. In addition, it gives accurate representation of the Joule–Thomson inversion curve and predicts critical data for nonpolar mixtures without any binary parameters. For polar systems, the novel EOS predicts critical data for complete homologous series of mixtures using a pair of binary parameters evaluated from a single system. © 2005 American Institute of Chemical Engineers AIChE J, 2005
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".