Quantifying the effect of polar interactions on the behavior of binary mixtures: Phase, interfacial, and excess properties.

In this work, polar soft-Statistical Associating Fluid Theory (SAFT) was used in a systematic manner to quantify the influence of polar interactions on the phase equilibria, interfacial, and excess properties of binary mixtures. The theory was first validated with available molecular simulation data and then used to isolate the effect of polar interactions on the thermodynamic behavior of the mixtures by fixing the polar moment of one component while changing the polar moment of the second component from non-polar to either highly dipolar or quadrupolar, examining 15 different binary mixtures. It was determined that the type and magnitude of polar interactions have direct implications on the vapor-liquid equilibria (VLE), resulting in azeotropy for systems of either dipolar or quadrupolar fluids when mixed with non-polar or low polar strength fluids, while increasing the polar strength of one component shifts the VLE to be more ideal. Additionally, excess properties and interfacial properties such as interfacial tension, density profiles, and relative adsorption at the interface were also examined, establishing distinct enrichment in the case of mixtures with highly quadrupolar fluids. Finally, polar soft-SAFT was applied to describe the thermodynamic behavior of binary mixtures of experimental systems exhibiting various intermolecular interactions (non-polar and polar), not only demonstrating high accuracy and robustness through agreement with experimental data but also providing insights into the effect of polarity on the interfacial properties of the studied mixtures. This work proves the value of having an accurate theory for isolating the effect of polarity, especially for the design of ad hoc polar solvents.

Polar soft-SAFT: theory and comparison with molecular simulations and experimental data of pure polar fluids.

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.

Mapping the Flammability Space of Sustainable Refrigerant Mixtures through an Artificial Neural Network Based on Molecular Descriptors.

As the EU's mandates to phase out high-GWP refrigerants come into effect, the refrigeration industry is facing a new, unexpected reality: the introduction of more flammable yet environmentally compliant alternatives. This paradigm shift amplifies the need for a rapid, reliable screening methodology to assess the propensity for flammability of emerging fourth generation blends, offering a pragmatic alternative to laborious and time-intensive traditional experimental assessments. In this study, an artificial neural network (ANN) is meticulously constructed, evaluated, and validated to address this emerging challenge by predicting the normalized flammability index (NFI) for an extensive array of pure, binary, and ternary mixtures, reflecting a substantial diversity of compounds like CO2, hydrofluorocarbons (HFCs), hydrofluoroolefins (HFOs), six saturated hydrocarbons (sHCs), hydroolefins (HOs), and others. The optimal configuration ([61 (I) × 14 (HL1) × 24 (HL2) × 1 (O)]) demonstrated a profound fit to the data, with metrics like R 2 of 0.999, root-mean-square error (RMSE) of 0.1735, average absolute relative deviation (AARD)% of 0.8091, and SDav of ±0.0434. Exhaustive assessments were conducted to ensure the most efficient architecture without compromising the accuracy. Additionally, the analysis of the standardized residuals (SDR) and applicability domain (AD) exhibited fine control and consistency over the data points. External validation using quaternary mixtures further attested to the model's adaptability and predictive capability. The exploration into the relative contribution of descriptors led to the identification of 23 significant sigma descriptors derived from conductor-like screening model (COSMO), responsible for 90.98% of the total contribution, revealing potential avenues for model simplification without a substantial loss in predictive power. Moreover, the model successfully predicted the behavior of prospective industry-relevant mixtures, reinforcing its reliability and opening the door to experimentation with untested blends. The results collectively manifest the developed ANN's efficiency, robustness, and adaptability in modeling flammability, catering to the demands of industry standards, environmental concerns, and safety requirements.

Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning.

We present here a novel integrated approach employing machine learning algorithms for predicting thermophysical properties of fluids. The approach allows obtaining molecular parameters to be used in the polar soft-statistical associating fluid theory (SAFT) equation of state using molecular descriptors obtained from the conductor-like screening model for real solvents (COSMO-RS). The procedure is used for modeling 18 refrigerants including hydrofluorocarbons, hydrofluoroolefins, and hydrochlorofluoroolefins. The training dataset included six inputs obtained from COSMO-RS and five outputs from polar soft-SAFT parameters, with the accurate algorithm training ensured by its high statistical accuracy. The predicted molecular parameters were used in polar soft-SAFT for evaluating the thermophysical properties of the refrigerants such as density, vapor pressure, heat capacity, enthalpy of vaporization, and speed of sound. Predictions provided a good level of accuracy (AADs = 1.3-10.5%) compared to experimental data, and within a similar level of accuracy using parameters obtained from standard fitting procedures. Moreover, the predicted parameters provided a comparable level of predictive accuracy to parameters obtained from standard procedure when extended to modeling selected binary mixtures. The proposed approach enables bridging the gap in the data of thermodynamic properties of low global warming potential refrigerants, which hinders their technical evaluation and hence their final application.

Assessment of Low Global Warming Potential Refrigerants for Drop-In Replacement by Connecting their Molecular Features to Their Performance.

The use of hydrofluorocarbons (HFCs) as an alternative for refrigeration units has grown over the past decades as a replacement to chlorofluorocarbons (CFCs), banned by the Montreal's Protocol because of their effect on the depletion of the ozone layer. However, HFCs are known to be greenhouse gases with considerable global warming potential (GWP), thousands of times higher than carbon dioxide. The Kigali Amendment to the Montreal Protocol has promoted an active area of research toward the development of low GWP refrigerants to replace the ones in current use, and it is expected to significantly contribute to the Paris Agreement by avoiding nearly half a degree Celsius of temperature increase by the end of this century. We present here a molecular-based evaluation tool aiming at finding optimal refrigerants with the requirements imposed by current environmental legislations in order to mitigate their impact on climate change. The proposed approach relies on the robust polar soft-SAFT equation of state to predict thermodynamic properties required for their technical evaluation at conditions relevant for cooling applications. Additionally, the thermodynamic model integrated with technical criteria enable the search for compatibility of currently used third generation compounds with more eco-friendly refrigerants as drop-in replacements. The criteria include volumetric cooling capacity, coefficient of performance, and other physicochemical properties with direct impact on the technical performance of the cooling cycle. As such, R1123, R1224yd(Z), R1234ze(E), and R1225ye(Z) demonstrate high aptitude toward replacing R134a, R32, R152a, and R245fa with minimal retrofitting to the existing system. The current modeling platform for the rapid screening of emerging refrigerants offers a guide for future efforts on the design of alternative working fluids.