Growing concerns about the global warming potential of hydrofluorocarbons (HFCs) has led to increasing interest in developing technologies to effectively recover and recycle these refrigerants. Ionic liquids (ILs) have shown great potential to selectively separate azeotropic HFC gas mixtures, such as R-410A composed of HFC-32 (CH2F2) and HFC-125 (CHF2CF3), based on solubility differences between the refrigerant gases in the respective IL. Isothermal vapor–liquid equilibrium (VLE) data for HFC-32 and HFC-125 were measured in ILs containing fluorinated and nonfluorinated anions using a gravimetric microbalance at pressures ranging from 0.05 to 1.0 MPa and a temperature of 298.15 K. The van der Waals equation of state (EoS) model was applied to correlate the experimental solubility data of each HFC refrigerant/IL mixture. The solubility differences between HFC-32 and HFC-125 vary significantly depending on the choice of IL. The diffusion coefficients for both HFC refrigerants in each IL were calculated by fitting Fick’s law to time-dependent absorption data. HFC-32 has a higher diffusivity in most ILs tested because of its smaller molecular radius relative to HFC-125. Based on the calculated Henry’s law constants and the mass uptake for each system, [C6C1im]­[Cl] was found to have the highest selectivity difference for separating R-410A at 298.15 K.

Accurate force fields are necessary for predictive molecular simulations. However, developing force fields that accurately reproduce experimental properties is challenging. Here, we present a machine learning directed, multiobjective optimization workflow for force field parametrization that evaluates millions of prospective force field parameter sets while requiring only a small fraction of them to be tested with molecular simulations. We demonstrate the generality of the approach and identify multiple low-error parameter sets for two distinct test cases: simulations of hydrofluorocarbon (HFC) vapor–liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal phase. We discuss the challenges and implications of our force field optimization workflow.

Current legislation calling for the phase out of hydrofluorocarbon (HFC) refrigerants is driving a global market shift that has prompted industry and research institutions to investigate new refrigerant mixtures and sustainable separation techniques for recycling refrigerants. The recent American Innovation and Manufacturing (AIM) Act of 2020 requires an 85% phase down of HFC production over the next 15 years. To achieve this goal, azeotropic refrigerant mixtures, such as R-410A composed of 50 wt % HFC-32 (difluoromethane, CH 2 F 2 ) and 50 wt % HFC-125 (pentafluoroethane, CHF 2 CF 3 ), will have to be separated to recycle the lower global warming HFC-32 component. The present work investigates the solubility of HFC-32 and HFC-125 in six ionic liquids (ILs) with halogen anions for the purpose of developing the thermophysical property data required for designing extractive distillation recycling processes and understanding the choice of cation and anion type. A gravimetric microbalance was used to collect isothermal vapor liquid equilibrium data for each of the ILs at 298.15 K and pressures from 0.05 to 1.0 MPa. The Peng−Robinson equation of state was used to model the solubility of the HFCs in the ILs. The solubility of HFC-32 in the ILs showed small differences, while the solubility of HFC-125 had significant variations with respect to the anion type and the cation alkyl chain length. Fick's law was applied to calculate diffusion coefficients for each HFC/IL system. HFC-32 has a greater diffusivity than HFC-125 based on smaller molecular size. The 1-n-hexyl-3-methylimidazolium chloride and the trihexyl(tetradecyl)phosphonium chloride ILs have the highest HFC-125/HFC-32 selectivity at 298.15 K. Based on both the mass uptake and selectivity ratio, these two ILs are potential entrainers for the separation of R-410A using extractive distillation.

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