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DOE OSTI2024

Flexible MOFs as Pressure-Tunable Filters for Hydrocarbon Separation

Metal–organic frameworks (MOFs) hold significant promise for separating gas mixtures, especially hydrocarbons. While the main focus in the field is the development of new adsorbents for specific separations of binary mixtures, a preferable situation would be using a single framework for separating various species. This has been achieved in some flexible MOFs, where ternary mixtures can be separated at different temperatures. Here, we propose a simple yet fascinating way of utilizing the flexibility of MOFs to create tunable filters as a function of external pressure. We thus replace the more costly temperature-driven tunability with a cost-efficient external-pressure tunability. As a proof-of-concept, we select the CaMOF flexible framework for C6 hydrocarbon separation, however, our results are applicable to other flexible framework and gas molecules. Furthermore, our findings provide mechanistic insight and guidelines to engineer separation filters by designing flexible pores with critical sizes that can be effectively manipulated by external pressure.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Coordinated Cation Transport in Ti 3 C 2 T x MXene Membranes

Membrane nanofiltration is an attractive strategy for the selective recovery of high-demand metals from wastewater and brine. Effective sieving of ions in aqueous environments will require precise control over membranes’ nanochannel size and chemistry. Ti 3 C 2 T x MXene is an environmentally stable 2D material that can be processed into laminar membranes containing nanoscale interlayer spaces. The MXene interlayer environment depends on the ion species and amount of water intercalated between MXene sheets, and it is the major factor governing permeation and selectivity through MXene membranes. Coordinated ion–ion and ion-interlayer dynamics in the presence of complex mixtures can impact ion permeability and selectivity. Herein, we observe strong competitive effects between different cations (Li + , Na + , and Ca 2+ ) in binary mixtures, resulting in reduced selectivity when compared with single-salt permeability ratios. X-ray diffraction, molecular dynamics, and density functional theory simulations support the conclusion that cations with stronger attraction to MXene flakes can preferentially occupy the MXene nanochannels and hinder other ions via charge or size exclusion. In conclusion, elucidation of ion transport behavior in MXene under complex conditions will allow for more rational design of efficient ion-sieving membranes.

2D Materials↗

NASA NTRS2000

R-O-C(triple bond)N species produced by ion irradiation of ice mixtures: comparison with astronomical observations

We have investigated the effects induced by ion bombardment of mixtures containing nitrogen-bearing compounds at low temperatures. The results show the formation of a band at 2080 cm-1 in binary mixtures, NH3:CH4 and N2:CH4, which we attribute to HCN embedded in the organic residue formed by ion irradiation. In addition to this band, ternary mixtures containing an oxygen-bearing species (i.e., H2O) form a compound with a prominent absorption band at about 2165 cm-1 (4.62 microns). We ascribe this band to a nitrile compound containing O that is bonded to the organic residue. A detailed comparison of the laboratory results with astronomical data of the 4.62 microns absorption band in protostellar spectra shows good agreement in peak position and profile. Our experimental studies show that N2, which is a more likely interstellar ice component than NH3, can be the molecular progenitor of the carrier of the interstellar band. This is an alternative to the pathway by which UV photolysis of NH3-containing ices produces the 4.62 microns band and implies that ion bombardment may well play an important role in the evolution of interstellar ices. Here, we discuss the implications of our studies for the chemical route by which the carrier of the 4.62 microns band is formed in these laboratory experiments.

NASA Discipline Exobiology↗

NASA NTRS1986

Measurement of viscosity of gaseous mixtures at atmospheric pressure

Coefficients of viscosity of various types of gas mixtures, including simulated natural-gas samples, have been measured at atmospheric pressure and room temperature using a modified capillary tube method. Pressure drops across the straight capillary tube section of a thermal mass flowmeter were measured for small, well-defined, volume flow rates for the test gases and for standard air. In this configuration, the flowmeter provides the volumetric flow rates as well as a well-characterized capillary section for differential pressure measurements across it. The coefficients of viscosity of the test gases were calculated using the reported value of 185.6 micro P for the viscosity of air. The coefficients of viscosity for the test mixtures were also calculated using Wilke's approximation of the Chapman-Enskog (C-E) theory. The experimental and calculated values for binary mixtures are in agreement within the reported accuracy of Wilke's approximation of the C-E theory. However, the agreement for multicomponent mixtures is less satisfactory, possible because of the limitations of Wilkes's approximation of the classical dilute-gas state model.

Singh, J. J.↗

DOE OSTI2026

Electrocatalytic Hydrotreatment of Bio-Oil: Exploring Interactions Between Functional Groups

Electrocatalytic hydrotreatment (ECH) is being explored as a sustainable route for upgrading bio-oil to renewable fuels and chemicals. Bio-oil, produced by the fast pyrolysis of lignocellulosic biomass, is a complex mixture of compounds with various oxygen-containing functional groups, such as anhydrosugars, carboxylic acids, ketones, aldehydes, furans, phenols and alcohols. The ECH of several bio-oil model compound binary mixtures was conducted to investigate the interactions between these functional groups. Notably, phenolic compound reduction was significantly inhibited in the presence of aldehydes, particularly furfural. A strategy involving the reagent-based reduction of the aldehyde to an alcohol prior to ECH was shown to partially mitigate this inhibitory effect. Additionally, qualitative studies on the ECH of catalytic fast pyrolysis (CFP) oil with low aldehyde content showed promising results. These studies achieved the conversion of cyclopentenones and phenolic compounds present in the CFP oil to cyclopentanols and cyclohexanols, respectively.

09 BIOMASS FUELS↗

DOE OSTI2021

Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal–organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. Here, the results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1988

Interfacial tension and vapor-liquid equilibria in the critical region of mixtures

In the critical region, the concept of two-scale-factor universality can be used to accurately predict the surface tension between near-critical vapor and liquid phases from the singularity in the thermodynamic properties of the bulk fluid. In the present work, this idea is generalized to binary mixtures and is illustrated using the data of Hsu et al. (1985) for CO2 + n-butane. The pressure-temperature-composition-density data for coexisting, near-critical phases of the mixtures are fitted with a thermodynamic potential comprised of a sum of a singular term and nonsingular terms. The nonuniversal amplitudes characterizing the singular term for the mixtures are obtained from the amplitudes for the pure components by interpolation in a space of thermodynamic 'field' variables. The interfacial tensions predicted for the mixtures from the singular term are within 10 percent of the data on three isotherms in the pressure range (Pc - P)/Pc of less than 0.5. This difference is comparable to the combined experimental and model errors.

Moldover, Michael R.↗

DOE OSTI2022

Destabilization of binary mixing layer in supercritical conditions

Compressible mixing layer instabilities are of importance to a wide range of environmental and industrial applications. Past studies have focused on either ideal-gas or real-fluid thermodynamic regimes of single-species mixing layers. However, mixing layers of binary mixtures at supercritical conditions, commonly encountered in fuel injection systems, introduce additional complexities due to the added compositional degree-of-freedom. Moreover, the effect of strong variations in thermodynamic response functions across the Widom line on the binary mixing layer stability remains poorly understood. Thus, the objective of this study is to examine the coupling between the hydrodynamic instability and the real-fluid thermodynamics across the Widom line and its effects on the overall binary mixing layer dynamics. To this end, we develop a linear stability analysis of the full binary-species compressible transport equations coupled with the PC-SAFT equation of state. Analysis shows the existence of a novel instability mechanism that arises from juxtapositioning of the Widom-line transition and the hydrodynamic inflection point. This novel thermodynamically induced instability mechanism has the net effect of destabilizing the binary mixing layer at lowering supercritical conditions towards the critical pressure point. This is in contrast to previous stability analyses of supercritical single-species mixing layers, where increasing pressure destabilizes the flow due to its effect on reducing the density stratification. The discovered thermodynamically induced instability mechanism of binary mixing flows highlights the need for an extension of classical instability criteria to incorporate the effect of strong variations in the thermodynamic response functions across the Widom line on mixing layer instability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1981

Performance tests of a gas blending system based on mass-flow controllers

The system provides many of the gas mixtures required for calibrating analytical instruments used in engine exhaust gas analysis and is capable of blending from one to four additive gases with either of two carrier gases in concentrations from 20 ppm to 50%. Two mixtures can be flowing simultaneously. Performance tests were made to determine the stability accuracy of the system while it was in limited use for a period of 2 years. The accuracy of the blender was measured by comparing binary mixtures from the blender with National Bureau of Standards standard reference materials. Analytical instruments were used to make these comparisons. The expected accuracy of 2% was obtained in some of the tests, by the majority showed a systematic biAs of -5%. Although these tests revealed subtle instabilities in the flow controllers that contributed to the random scatter of data, the accuracy of wet test meters and bubble flowmeters used for calibration is marginal for this purpose. A simple procedure is recommended that should enable the full potential of the system to be realized.

Evans, A., Jr.↗

DOE OSTI2021

High-throughput screening of multifunctional nanocoatings based on combinations of polyphenols and catecholamines

Biomimetic surface coatings based on plant polyphenols and catecholamines have been used broadly in a variety of applications. However, the lack of a rational cost-effective platform for screening these coatings and their properties limits the true potential of these functional materials to be unleashed. Here, we investigated the oxidation behavior and coating formation ability of a library consisting of 45 phenolic compounds and catecholamines. UV–vis spectroscopy demonstrated significant acceleration of oxidation and polymerization under UV irradiation. We discovered that several binary mixtures resulted in non-additive behavior (synergistic or antagonistic effect) yielding much thicker or thinner coatings than individual compounds measured by ellipsometry. To investigate the properties of coatings derived from new combinations, we used a miniaturized high-throughput strategy to screen 2,532 spots coated with single, binary, and ternary combinations of coating precursors in one run. We evaluated the use of machine learning models to learn the relation between the chemical structure of the precursors and the thickness of the nanocoatings. Formation and stability of nanocoatings were investigated in a high-throughput manner via discontinuous dewetting. 30 stable combinations (hits) were used to tune the surface wettability and to form water droplet microarray and spot size gradients of water droplets on the coated surface. No toxicity was observed against eukaryotic HeLa cells and Pseudomonas aeruginosa (strain PA30) bacteria after 24 h incubation at 37 C. The strategy introduced here for high-throughput screening of nanocoatings derived from combinations of coating precursors enables the discovery of new functional materials for various applications in science and technology in a cost-effective miniaturized manner.

36 MATERIALS SCIENCE↗

DOE OSTI2021

First-principles equation of state database for warm dense matter computation

We put together a first-principles equation of state (FPEOS) database for matter at extreme conditions by combining results from path integral Monte Carlo and density functional molecular dynamics simulations of the elements H, He, B, C, N, O, Ne, Na, Mg, Al, and Si as well as the compounds LiF , B 4 C , BN , CH 4 , CH 2 , C 2 H 3 , CH , C 2 H , MgO , and MgSiO 3 . For all these materials, we provide the pressure and internal energy over a density-temperature range from ~ 0.5 to 50 g cm - 3 and from ~ 10 4 to 10 9 K, which are based on ~ 5000 different first-principles simulations. We compute isobars, adiabats, and shock Hugoniot curves in the regime of L - and K -shell ionization. Invoking the linear mixing approximation, we study the properties of mixtures at high density and temperature. Furthermore, we derive the Hugoniot curves for water and alumina as well as for carbon-oxygen, helium-neon, and CH-silicon mixtures. We predict the maximal shock compression ratios of H 2 O , H 2 O 2 , Al 2 O 3 , CO , and CO 2 to be 4.61, 4.64, 4.64, 4.89, and 4.83, respectively. Finally we use the FPEOS database to determine the points of maximum shock compression for all available binary mixtures. We identify mixtures that reach higher shock compression ratios than their end members. We discuss trends common to all mixtures in pressure-temperature and particle-shock velocity spaces. In the Supplemental Material, we provide all FPEOS tables as well as computer codes for interpolation, Hugoniot calculations, and plots of various thermodynamic functions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2016

Physical State and Distribution of Materials at the Surface of Pluto from New Horizons LEISA Imaging Spectrometer

From Earth based observations Pluto is known to be the host of N2, CH4 and CO ices and also a dark red material. Very limited spatial distribution information is available from rotational visible and near-infrared spectral curves obtained from hemispheric measurements. In July 2015 the New Horizons spacecraft reached Pluto and its satellite system and recorded a large set of data. The LEISA spectro-imager of the RALPH instruments are dedicated to the study of the composition and physical state of the materials composing the surface. In this paper we report a study of the distribution and physical state of the ices and non-ice materials on Pluto's illuminated surface and their mode and degree of mixing. Principal Component analysis as well as various specific spectral indicators and correlation plots are used on the first set of 2 high resolution spectro-images from the LEISA instrument covering the whole illuminated face of Pluto at the time of the New Horizons encounter. Qualitative distribution maps have been obtained for the 4 main condensed molecules, N2, CH4, CO, H2O as well as for the visible-dark red material. Based on specific spectral indicators, using either the strength or the position of absorption bands, these 4 molecules are found to indicate the presence of 3 different types of ices: N2-rich:CH4:CO ices, CH4-rich(:CO:N2?) ices and H2O ice. The mixing lines between these ices and with the dark red material are studied using scatter plots between the various spectral indicators. CH4 is mixed at the molecular level with N2, most probably also with CO, thus forming a ternary molecular mixture that follows its phase diagram with low solubility limits. The occurrence of a N2-rich - CH4-rich ices mixing line associated with a progressive decrease of the CO/CH4 ratio tells us that a fractionation sublimation sequence transforms one type of ice to the other forming either a N2-rich - CH4-rich binary mixture at the surface or an upper CH4-rich ice crust that may hide the N2-rich ice below. The strong CH4-rich - H2O mixing line witnesses the subsequent sublimation of the CH4-rich ice lag left behind by the N2:CO sublimation (N spring-summer), or a direct condensation of CH4 ice on the cold H2O ice (S autumn). The weak mixing line between CH4-containing ices and the dark red material and the very sharp spatial transitions between these ices and this non-volatile material are probably due to thermal incompatibility. Finally the occurrence of a H2O ice - red material mixing line advocates for a spatial mixing of the red material covering H2O ice, with possibly a small amount intimately mixed in water ice. From this analysis of the different materials distribution and their relative mixing lines, H2O ice appears to be the substratum on which other ices condense or non-volatile organic material is deposited from the atmosphere. N2-rich ices seem to evolve to CH4-dominated ices, possibly still containing traces of CO and N2, as N2 and CO sublimate away. The spatial distribution of these materials is very complex. The high spatial definition of all these composition maps, as well as those at even higher resolution that will be soon available, will allow us to compare them with Pluto's geologic features observed by LORRI panchromatic and MVIC multispectral imagers to better understand the geophysical processes in action at the surface of this astonishingly active frozen world.

Schmitt, B.↗

DOE OSTI2025

Insights into determining pore size properties of ultrafiltration membranes

The selectivity of porous membranes is often characterized using solute rejection tests, where membranes are challenged with dilute aqueous solutions of neutral solutes at operating conditions that minimize concentration polarization and fouling. In single solute tests, a membrane is challenged with one molecular weight (MW) solute at a time from low to high MW. Since single solute methods are time-intensive, mixed solute tests have become more common, where a mixture of several MW solutes challenges a membrane at once. However, the presence of large solutes in a mixture increases the rejection of smaller solutes. Furthermore, there are no universally accepted operating conditions or standard methods used by membrane manufacturers or researchers for the experiments, leading to difficulty in pore size and pore characteristic comparisons. In this paper, commercial ultrafiltration membranes were challenged with single and mixed solute polyethylene glycol (PEG) and dextran aqueous solutions. First, rejection values determined using total organic carbon (TOC) and high-performance liquid chromatography (HPLC) from single solute filtration experiments are compared. Differences in rejection curves obtained by the two techniques are attributed to solute polydispersity. Mixed solute filtration experiments with binary mixtures of solutes showcased co-solute interactions, which increase with both the size and weight percent of large solute in the mixture. Mixed solute filtration experiments at varying operating conditions (i.e., stir speed and flux) were conducted to determine operating conditions that mitigate co-solute interactions. Stir speed had a minimal effect on co-solute interactions. In contrast, low flux conditions can help minimize co-solute interactions, leading to pore size distributions that closely resemble results observed in single solute filtration using narrowly dispersed solutes. Additionally, at low flux conditions, the predicted membrane pore size distributions utilizing mixed solute experiments with PEG and dextran were similar.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Effect of Solvent on the Interaction of Lignin with a Zeolite Nanosheet in the Condensed Phase

Lignin is the essential building block of lignocellulosic biomass, an excellent renewable source of different aromatic monomers for the polymer and biofuel industry. The depolymerization of lignin into value-added chemicals and fuels through the catalytic process poses a significant challenge due to the complex structure of lignin. Understanding lignin’s conformational diversity and dynamics in the liquid phase is crucial to designing an effective depolymerization process. Here, we conducted all-atom molecular dynamics simulations to understand the conformation and dynamics of softwood lignin on the all-silica zeolite nanosheet based on the MFI topology in a binary mixture of water–methanol at three different molar compositions (0%, 50%, and 100% methanol). We observed that the methanol–surface interaction is stronger than the water–surface interaction, and methanol readily diffused into the MFI core. Lignin surface contacts decrease with increasing methanol composition due to higher solubility and dynamics. Lignin dynamics on the surface in neat water is an order of magnitude smaller than methanol. Here, we also found that lignin adopts a slightly extended conformation when it stays on the surface than in the bulk solution phase for the pure water case, whereas for pure methanol and the binary solution structures are statistically similar.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures

Machine learning interatomic potentials have emerged as a powerful tool for bypassing the spatiotemporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an ensemble active learning software workflow for generating multicomposition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities include: (1) setting up user-defined combinatorial chemical spaces of charge neutral mixtures of arbitrary molten mixtures spanning 11 cations (Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba and two heavy species, Nd, and Th) and 4 anions (F, Cl, Br, and I), (2) configurational sampling using low-cost empirical parameterizations, (3) active learning for down-selecting configurational samples for single point density functional theory calculations at the level of Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation functional, and (4) Bayesian optimization for hyperparameter tuning of two-body and many-body GAP models. Here, we apply the AL4GAP workflow to showcase high throughput generation of five independent GAP models for multicomposition binary-mixture melts, each of increasing complexity with respect to charge valency and electronic structure, namely: LiCl–KCl, NaCl–CaCl 2 , KCl–NdCl 3 , CaCl 2 –NdCl 3 , and KCl–ThCl 4 . Our results indicate that GAP models can accurately predict structure for diverse molten salt mixture with density functional theory (DFT)-SCAN accuracy, capturing the intermediate range ordering characteristic of the multivalent cationic melts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Structural Color Production in Melanin-Based Disordered Colloidal Nanoparticle Assemblies in Spherical Confinement

Melanin is a ubiquitous natural pigment that exhibits broadband absorption and high refractive index. Despite its widespread use in structural color production, how the absorbing material, melanin, affects the generated color is unknown. Using a combined molecular dynamics and finite-difference time-domain computational approach, here we investigates structural color generation in one-component melanin nanoparticle-based supraparticles (called supraballs) as well as binary mixtures of melanin and silica (nonabsorbing) nanoparticle-based supraballs. Experimentally produced one-component melanin and one-component silica supraballs, with thoroughly characterized primary particle characteristics using neutron scattering, produce reflectance profiles similar to the computational analogs, confirming that the computational approach correctly simulates both absorption and multiple scattering from the self-assembled nanoparticles. These combined approaches demonstrate that melanin's broadband absorption increases the primary reflectance peak wavelength, increases saturation, and decreases lightness factor. Additionally, the dispersity of nanoparticle size more strongly influences the optical properties of supraballs than packing fraction, as evidenced by the production of a larger range of colors when size dispersity is varied versus packing fraction. For binary melanin and silica supraballs, the chemistry-based stratification allows for more diverse color generation and finer saturation tuning than does the degree of mixing/demixing between the two chemistries.

broadband absorption↗

DOE OSTI2021

Autoignition study of iso -cetane/tetralin blends at low temperature

iso-Cetane and tetralin are the two fuel components commonly considered in literature diesel surrogates, and are hydrocarbon classes representative of iso-alkanes and naphthoaromatics, respectively. Since both surrogate components are involved in the key reactions controlling the autoignition reactivity of diesel surrogates, autoignition studies of iso-cetane, tetralin, and their blending behavior at low temperatures can be helpful in developing/refining chemical kinetic models of diesel surrogates, in order to better predict diesel ignition response under low temperature combustion conditions. In this investigation, autoignition experiments of iso-cetane, tetralin, and their binary mixtures at varying blending ratios have been conducted in a rapid compression machine (RCM) in the temperature/pressure range of 630–930 K and 10–20 bar. For the neat fuel components, the newly-acquired RCM data have been compared with the literature experimental results at some overlapping conditions. In addition, a chemical kinetic model describing the oxidation of iso-cetane and tetralin compiled/updated from the diesel surrogate model developed by Lawrence Livermore National Laboratory is used to simulate the experimental datasets. It is shown that this model exhibits good agreement with the experimental results of tetralin and also predicts the total ignition delay times of iso-cetane at lower temperatures reasonably well. However, the current model fails to fully capture the increase of total ignition delay time with increasing amount of tetralin in the binary blends observed in the present RCM experiments. Therefore, chemical kinetic analyses of the current model are conducted to help identify possible reasons leading to the discrepancies between simulations and experiments. Furthermore, the chemical kinetic interactions between iso-cetane and tetralin are explored by including additional cross-reactions among iso-cetane, tetralin, and their fuel fragments in the current model to assess their effects on model predictions. As a result, based on the chemical kinetic analysis results, future direction for model refinements is discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗