Thermodynamics of organic mixtures containing amines. IV. Systems with aniline
Bibliographic record
Abstract
Binary mixtures of aniline with benzene, toluene, alkane, alkanol, or N,N-dialkylamide 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 liquidliquid equilibria, vaporliquid equilibria, and molar excess enthalpies. The two latter properties for ternary systems are well-represented by DISQUAC using binary parameters only (i.e., neglecting ternary interactions). A comparison of DISQUAC results and those obtained from the UNIFAC (Dortmund version) and ERAS models is also shown. The experimental molar excess enthalpies for binary and ternary mixtures are better described by DISQUAC than by UNIFAC. ERAS fails when representing molar excess enthalpies of those binary systems including methanol or ethanol. This may be due to the existence of strong dipolar interactions among aniline molecules as well as to effects related to the equation of state term, evaluated comparing molar excess enthalpies, and molar excess internal energies at constant volume. The study of the aniline systems in terms of the concentrationconcentration structure factor also underlines the importance of dipolar interactions in solutions with alkanes or alcohols, which may be due to the high polarizability of the aniline molecule.Key words: thermodynamics, mixtures, aniline, dipolar interactions, structural effects.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".