Vapor–Liquid Equilibria and Diffusion Coefficients of Difluoromethane, 1,1,1,2-Tetrafluoroethane, and 2,3,3,3-Tetrafluoropropene in Low-Viscosity Ionic Liquids
Bibliographic record
Abstract
The phase-down of hydrofluorocarbons (HFCs) established by the Kigali Amendment to the Montreal Protocol is leading to the formulation and commercialization of new refrigerant blends containing hydrofluoroolefins (HFOs), such as 2,3,3,3-tetrafluoropropene (R1234yf), and HFCs with moderate global warming potential, namely, difluoromethane (R32) and 1,1,1,2-tetrafluoroethane (R134a). Moreover, the recycling of refrigerants is attracting attention as a means to reduce the amount of new HFCs produced and their release to the environment. To that end, the use of ionic liquids has been proposed as entrainers to separate refrigerants with close-boiling points or azeotropic blends. Thus, the vapor–liquid equilibria and diffusion coefficients of the refrigerant–ionic liquid pairs formed by R32 + [C 2 mim][BF 4 ], R134a + [C 2 mim][BF 4 ], R134a+ [C 2 mim][OTf], R1234yf + [C 2 mim][OTf], and R1234yf + [C 2 mim][Tf 2 N] are studied using an isochoric saturation method at temperatures ranging from 283.15 to 323.15 K and pressures up to 0.9 MPa. In addition, the solubility behavior is successfully modeled using the nonrandom two-liquid activity-coefficient method, and the Henry’s law constants at infinite dilution, solvation energies, and infinite dilution activity coefficients are calculated.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".