Polar soft-SAFT: theory and comparison with molecular simulations and experimental data of pure polar fluids
Bibliographic record
Abstract
The consideration of polar interactions is of vital importance for the development of predictive and accurate thermodynamic models for polar fluids, as they govern most of their thermodynamic properties, making them highly non-ideal fluids. We present here for the first time the extension of the soft-SAFT equation of state (EoS), named polar soft-SAFT, to explicitly model intermolecular polar interactions (dipolar and quadrupolar), using the approach of Jog and Chapman (P. K. Jog and W. G. Chapman, Mol. Phys., 1999, 97(3), 307-319). The theory is first validated using molecular simulation data for a wide range of polar model systems including Stockmayer fluids, LJ dimers with dipole, and quadrupolar LJ fluids, for a wide range of thermophysical properties such as liquid density, vapour pressure, surface tension and heat capacities. Excellent agreement between polar soft-SAFT and simulation data has been obtained for all examined fluids and properties for systems exhibiting low to intermediate polar strength, while the agreement deteriorates at very high polar strengths. Once validated with simulations, the equation has been applied to calculate vapour-liquid equilibria (VLE), surface tension and second-order derivative properties of systems such as 2-ketone and methane chloride families as showcases for dipolar fluids and the benzene family for quadrupolar fluids, finding very good agreement with experimental data. In order to preserve the robustness of the model, the experimental value of the dipole or quadrupole was used in these calculations, while the additional parameter for the polar fluids was set a priori rather than included in the fitting procedure. The excellent agreement found with simulations and experiments empowers the soft-SAFT equation with new capabilities for the development of robust and accurate molecular models of polar fluids of industrial relevance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".