UNIFAC calculation of thermodynamic properties of binary 1-chloroalkane + alkane and α,ω-dichloroalkane + alkane mixtures: Comparison with NittaChao and DISQUAC predictions
Bibliographic record
Abstract
Data available in the literature for vaporliquid equilibria, activity coefficients at infinite dilution, and enthalpies of mixing for binary mixtures of 1-chloroalkanes or dichloroalkanes with alkanes are used to determine interaction parameters for three versions of the UNIFAC model the Tassios et al., Larsen et al., and Gmehling et al. versions. The interaction parameters for chlorine and methyl or methylene groups are calculated using data for the thermodynamic properties of 1-chloroalkane + alkane mixtures. In the case of the Gmehling version, the geometrical parameters for chlorine are also determined. In addition, structure-dependent interaction parameters for α,ω-dichloroalkane + alkane mixtures are presented, taking into account the proximity effect. When the two chlorine atoms of the dichloroalkane are more separated, they become more independent, and the reported values of the interaction parameters approach those of 1-chloroalkane. For all of the properties studied the mean deviation obtained with the new parameter values is lower than that obtained with older values. The results for the thermodynamic properties obtained using the new parameters of the three versions of UNIFAC are compared with those of DISQUAC and NittaChao models. Key words: alkanes, chloroalkanes, DISQUAC, excess thermodynamic properties, proximity effect, UNIFAC.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".