Thermodynamics of mixtures with strongly negative deviations from Raoult's law. Part 3. Application of the DISQUAC model to mixtures of triethylamine with alkanols. Comparison with Dortmund UNIFAC and ERAS results
Bibliographic record
Abstract
Binary mixtures of triethylamine (TEA) and alkanols have been investigated in the framework of DISQUAC. The systems are built by three contacts: aliphatichydroxyl, aliphaticnitrogen, and hydroxylnitrogen. The corresponding interaction parameters are reported and discussed. The former are avalilable in the literature but were modified (particularly the third dispersive (DIS) and quasichemical (QUAC) interchange coefficients) for sec- and tert-alkanols + n-alkanes using recent data on excess heat capacities at constant pressure (CEP) for systems of these alkanols with n-heptane. The interaction parameters for aliphatic-nitrogen contacts are purely dispersive. The structure dependence of the DIS and QUAC interchange coefficients of the hydroxyl-nitrogen contacts in 1-alkanols + TEA systems is similar to that found in other solutions previously investigated. The QUAC interchange coefficients remain constant from ethanol and are also valid for 2-alkanols and tert-butanol. Methanol behaves differently. A short discussion in terms of effective dipole moments is also included. DISQUAC represents well the thermodynamic properties examined: vapor-liquid equilibria (VLE), molar excess Gibbs energies (GE) and molar excess enthalpies (HE). DISQUAC provides better results than the Dortmund version of UNIFAC using the published geometrical and interaction parameters. ERAS parameters for 1-alkanols + TEA systems are also reported. Interactions between unlike molecules are stronger for solutions with methanol or ethanol. DISQUAC improves ERAS results on HE, while both models give similar results for GE. However, ERAS needs an specific parameter, with unknown temperature-dependence, to describe properly GE. The main advantage of ERAS is its ability to provide information on VE. Its main limitation is that can be only applied to those systems where association is expected. DISQUAC, a purely physical model, can be applied to any type of binary mixture, as it is followed from this and previous studies.Key words: theory, liquids, associated, thermodynamics, group contributions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".