Comprehensive Assessment of COSMO-SAC Models for Predictions of Fluid-Phase Equilibria

Osmotic Attraction: A New Mechanism of Nanoparticle Aggregation

Solvent-induced interactions of nanoparticles in colloidal solutions can substantially affect their physicochemical and transport properties. Predicting these interactions is challenging because the natural causes of the interactions are unclear. Here, we present a comprehensive experimental and theoretical study of the coagulation stability of the surfacted magnetic colloids. The magnetite nanoparticles stabilized by erucic acid were dispersed in 19 different good solvents. The colloidal stability was reduced by the gradual addition of a precipitant. As a precipitant, 19 other liquids were used. We show that coagulation is not associated with either dispersion or magnetic interactions. The coagulation mechanism is due to the osmotic attraction of nanoparticles induced by a specific local distribution of precipitant molecules. The precipitant molecules are repelled from the hydrophobic tails of the surfactant and form a depleted zone inside the surfactant layer leading to the appearance of the osmotic attraction between the nanoparticles and their subsequent coagulation when the critical concentration of the precipitant is reached. The quantitative description of the phenomenon is carried out within the framework of the generalized Asakura-Oosawa model of the attractive depletion forces between two adjacent particles and the Langmuir adsorption model for the equilibrium concentration of precipitant molecules in the surfactant layer of nanoparticles. The calculated precipitant critical concentrations, the coagulation curves of the polydisperse systems, and the variation of the coagulation criterion occurring upon changing the surfactant are in good agreement with the experimental data. The osmotic attraction mechanism is equally suitable for nanoparticles of any nature─plasmonic, semiconductor, or magnetic. This is determined by the surfactant-solvent interactions and is generic for many solvent-mediated systems taken at arbitrary concentrations of precipitant.

Purely Predicting the Pharmaceutical Solubility: What to Expect from PC-SAFT and COSMO-RS?

A pair of popular thermodynamic models for pharmaceutical applications, namely, the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state and the conductor-like screening model for real solvents (COSMO-RS) are thoroughly benchmarked for their performance in predicting the solubility of active pharmaceutical ingredients (APIs) in pure solvents. The ultimate goal is to provide an illustration of what to expect from these progressive frameworks when applied to the thermodynamic solubility of APIs based on activity coefficients in a purely predictive regime without specific experimental solubility data (the fusion properties of pure APIs were taken from experiments). While this kind of prediction represents the typical modus operandi of the first-principles-aided COSMO-RS, PC-SAFT is a relatively highly parametrized model that relies on experimental data, against which its pure-substance and binary interaction parameters (k_ij) are fitted. Therefore, to make this benchmark as fair as possible, we omitted any binary parameters of PC-SAFT (i.e., k_ij = 0 in all cases) and preferred pure-substance parameter sets for APIs not trained to experimental solubility data. This computational approach, together with a detailed assessment of the obtained solubility predictions against a large experimental data set, revealed that COSMO-RS convincingly outperformed PC-SAFT both qualitatively (i.e., COSMO-RS was better in solvent ranking) and quantitatively, even though the former is independent of both substance- and mixture-specific experimental data. Regarding quantitative comparison, COSMO-RS outperformed PC-SAFT for 9 of the 10 APIs and for 63% of the API-solvent systems, with root-mean-square deviations of the predicted data from the entire experimental data set being 0.82 and 1.44 log units, respectively. The results were further analyzed to expand the picture of the performance of both models with respect to the individual APIs and solvents. Interestingly, in many cases, both models were found to qualitatively incorrectly predict the direction of deviations from ideality. Furthermore, we examined how the solubility predictions from both models are sensitive to different API parametrizations.

Vapor-Liquid Equilibrium Study of the Monochlorobenzene-4,6-Dichloropyrimidine Binary System

The number of newly synthesized and produced organic chemicals has increased extremely quickly. However, the measurements of their physical properties, including their vapor-liquid equilibrium (VLE) data, are time-consuming. It so happens that there is no physical property data about a brand-new chemical. Therefore, the importance of calculating their physicochemical properties has been playing a more and more important role. 4,6-Dichloropyrimidine (DCP) is also a relatively new molecule of high industrial importance with little existing data. Therefore, their measurements and the comparison with the calculated data are of paramount concern. DCP is a widespread heterocyclic moiety that is present in synthetic pharmacophores with biological activities as well as in numerous natural products. Isobaric VLE for the binary system of 4,6-dichloropyrimidine and its main solvent monochlorobenzene (MCB) was measured using a vapor condensate and liquid circulation VLE apparatus for the first time in the literature. Density functional-based VLE was calculated using the COSMO-SAC protocol to verify the laboratory results. The COSMO-SAC calculation was found to be capable of representing the VLE data with high accuracy. Adequate agreement between the experimental and calculated VLE data was acquired with a minimal deviation of 3.0 × 10^-3, which allows for broader use of the results.

Phase equilibria modeling of biorefinery-related systems: a systematic review

The search for sustainable ideas has gained prominence in recent decades at all levels of society since it has become imperative an economic, social, and environmental development in an integrated manner. In this context, biorefineries are currently present as the technology that best covers all these parameters, as they add the benefits of waste reuse, energy cogeneration, and fossil fuel substitution. Thus, the study of the various applicable biological matrices and exploring the technical capabilities of these processes become highly attractive. Thermodynamic modeling acts in this scenario as a fundamental tool for phase behavior predictions in process modeling, design, and optimization. Thus, this work aimed to systematize, using the PRISMA statement for systematic reviews, the information published between 2010 and 2020 on phase equilibria modeling in systems related to biorefineries to organize what is already known about the subject. As a result, 236 papers were categorized in terms of the year, country, type of phase equilibria, and thermodynamic model used. Also, the phase behavior predictions of different thermodynamic models under the same process conditions were qualitatively compared, establishing PC-SAFT as the model that best represents the great diversity of interest systems for biorefineries in a wide range of conditions.

Investigation of COSMO-SAC model for solubility and cocrystal formation of pharmaceutical compounds

In this study, a predictive model named COSMO-SAC was investigated in solid/liquid equilibria for pharmaceutical compounds. The examined properties were the solubility of drug in the pure and mixed solvents, octanol/water partition coefficient, and cocrystal formation. The results of the original COSMO-SAC model (COSMO-SAC (2002)) was compared with a semi-predictive model named Flory–Huggins model and a revised version of the COSMO-SAC (COSMO-SAC (2010)). The results indicated the acceptable accuracy of the COSMO-SAC (2002) in the considered scope. The results emphasized on the suitability of the COSMO-SAC model for simple molecules containing C, H, and O by covalent and hydrogen bonding interactions. Applicability of the COSMO-SAC for more complicated molecules made of various functional groups such as COO and COOH doubly requires more modification in the COSMO-SAC.

A Benchmark Open-Source Implementation of COSMO-SAC

The COSMO-SAC modeling approach has found wide application in science as well as in a range of industries due to its good predictive capabilities. While other models for liquid phases, as for example UNIFAC, are in general more accurate than COSMO-SAC, these models typically contain many adjustable parameters and can be limited in their applicability. In contrast, the COSMO-SAC model only contains a few universal parameters and subdivides the molecular surface area into charged segments that interact with each other. In recent years, additional improvements to the construction of the sigma profiles and evaluation of activity coefficients have been made. In this work, we present a comprehensive description of how to postprocess the results of a COSMO calculation through to the evaluation of thermodynamic properties. We also assembled a large database of COSMO files, consisting of 2261 compounds, freely available to academic and noncommercial users. We especially focus on the documentation of the implementation and provide the optimized source code in C++, wrappers in Python, and sample sigma profiles calculated from each approach, as well as tests and validation results. The misunderstandings in the literature relating to COSMO-SAC are described and corrected. The computational efficiency of the implementation is demonstrated.

Palmitic-Acid-Based Hydrophobic Deep Eutectic Solvents for the Extraction of Lower Alcohols from Aqueous Media: Liquid-Liquid Equilibria Measurements, Validation and Process Economics

A new, natural, hydrophobic deep eutectic solvent (NADES) based on DL-menthol and palmitic acid is adopted for the extraction of alcohols from aqueous phase. DL-menthol is used as a hydrogen bond acceptor and palmitic acid, being a natural organic acid, as a hydrogen bond donor. The synthesis is carried out by the addition of DL-menthol and palmitic acid in a defined molar ratio. Physical properties of NADES along with water stability are then measured. Liquid-liquid equilibria (LLE) of lower alcohols, namely, DES (1) + lower alcohols (ethanol/1-propanol/1-butanol) (2) + water (3) are carried out at p = 1 atm and T = 298.15 K. LLE results show type-I phase behavior, where alcohol is preferentially attracted toward DES. The tie lines are then regressed via nonrandom two liquid and universal quasichemical models, which give root mean square deviation (RMSD) in the range of 0.29-0.35% and 0.39-0.75%, respectively. Finally, the quantum-chemical-based conductor-like screening model-segment activity coefficient is used to predict the tie lines, which gives an RMSD of 2.1-5.2%. A hybrid extractive distillation flowsheet is then used for scale up, process economics, and solvent recovery aspects in ASPEN using DES as a "pseudocomponent."

Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs

Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute. Furthermore, while most QSPR models include either experimental or quantum mechanical descriptors, the proposed models combine both, using experimental descriptors to represent the solvent and quantum mechanical descriptors to represent the solute. Up to twelve experimental descriptors and nine quantum mechanical descriptors are considered in the proposed models. Extensive internal and external validation is undertaken to assess model accuracy in predicting the Gibbs free energy of solvation for a large number of solute/solvent pairs. The best MLR model, which includes three solute descriptors and two solvent properties, yields a coefficient of determination (R2) of 0.88 and a root mean squared error (RMSE) of 0.59 kcal mol-1 for the training set. The best PLS model includes six latent variables, and has an R2 value of 0.91 and a RMSE of 0.52 kcal mol-1. The proposed models are compared to selected results based on continuum solvation quantum chemistry calculations. They enable the fast prediction of the Gibbs free energy of solvation of a wide range of solutes in different solvents.