Thermodynamics of binary mixtures containing a very strongly polar compound — Part 3: DISQUAC characterization of NMP + organic solvent mixtures
Bibliographic record
Abstract
Binary mixtures of 1-methyl pyrrolidin-2-one (NMP) with alkanes, benzene, toluene, 1-alkanol, or 1-alkyne have been investigated in the framework of the DISQUAC model. The reported interaction parameters change regularly with the molecular structure of the mixture components. The model consistently describes a set of thermodynamic properties, including liquid–liquid equilibria, vapor–liquid equilibria, solid–liquid equilibria, and molar excess enthalpies. A brief comparison of the DISQUAC results and those obtained from the UNIFAC and ERAS models is presented. The experimental excess enthalpies are better represented by DISQUAC than by UNIFAC because this quantity strongly depends on molecular structure. For NMP + alkane mixtures, the liquid–liquid equilibria data are also better represented by DISQUAC, while UNIFAC more accurately describes the vapor–liquid equilibria measurements at temperatures close to the critical point. This result suggests that a mean field theory is not able to represent simultaneously, with the same set of interaction parameters, liquid–liquid and vapor–liquid equilibria at the mentioned temperatures. ERAS fails when treating mixtures with 1-alkanols. This has been attributed to the strong dipole–dipole interactions between NMP molecules, characteristic of the investigated systems. Mixture structure is briefly studied in terms of the concentration–concentration structure factor.Key words: thermodynamics, NMP, organic solvent, self-association, dipole–dipole interactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".