Evaluation of COSMO-RS for solid–liquid equilibria prediction of binary eutectic solvent systems

Song, Zhen; Wang, Jingwen; Sundmacher, Kai

doi:10.1016/j.gee.2020.11.020

Cited by 41 publications

(61 citation statements)

References 35 publications

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“…However, the eutectic composition was estimated with a small deviation, which remained to be further improved when more reliable phase diagram data are available. Table 5 presents the different predictability of the eutectic temperacompared to Abranches' work, 30 Song's work, 64 and Martins's work, 65 which predicted the eutectic temperature of 27, 67, 12 families of DESs, respectively. In general, the results demonstrated that the GGIC method has good versatility, which can predict the eutectic temperatures of all types of DESs with good accuracy as the other methods, except for two special DES systems with unreasonable predictive results.…”

Section: The Predictive Ability For the Phase Behavior Of Dessmentioning

confidence: 99%

Group and group‐interaction contribution method for estimating the melting temperatures of deep eutectic solvents

Hou

et al. 2021

AIChE Journal

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Deep eutectic solvents (DESs) are mixtures of two or more components that have lower melting temperatures compared to their constituting components. DESs possess many advantages, for example, low volatility, low flammability, and low toxicity, which make them promising alternatives to traditional organic solvents. The melting temperature, one of the important physical properties, is of essential importance for industrial applications. In this work, a group and group-interaction contribution method was proposed to estimate the melting temperatures of DESs using an extensive database (1528 DESs, 1541 data points). The average absolute relative deviation (%AARD) between the estimated and experimental values of the melting temperature was 5.67% for binary DESs. Subsequently, this method was also extended to estimate the melting temperature of ternary DESs, with the AARD of 6.13%. The results indicate the high accuracy and broad applicability of the method and pave the way for the rational design of task-specific DESs.

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Section: The Predictive Ability For the Phase Behavior Of Dessmentioning

confidence: 99%

Group and group‐interaction contribution method for estimating the melting temperatures of deep eutectic solvents

Hou

et al. 2021

AIChE Journal

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“…On the other hand, ab initio approaches were developed for a priori characteristics of chemical systems without external parametrisation. Among them, one of the outstanding is the COSMO-RS model [ 63 , 64 ], extensively utilised for predicting solubility [ 65 , 66 ], solvent screening [ 67 , 68 , 69 ] and multiphase chemical equilibria [ 70 , 71 , 72 ] such as SLE, LLE, VLE, SLLE, etc. It combines density functional theory calculations with statistical thermodynamics as a post-treatment [ 73 ] and has become a standard approach in many scientific and industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning

Cysewski

Jeliński

Cymerman

et al. 2021

IJMS

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Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally studied binary systems, the one containing DMSO with water in unimolar proportions was found to be the most effective in theophylline dissolution. Likewise, for NADES, the addition of water (0.2 molar fraction) resulted in increased solubility compared to pure eutectics, with the highest solubilisation potential offered by the composition of choline chloride with glycerol. The ensemble of Statistica Automated Neural Networks (SANNs) developed using intermolecular interactions in pure systems has been found to be a very accurate model for solubility computations. This machine learning protocol was also applied as an extensive screening for potential solvents with higher solubility of theophylline. Such solvents were identified in all three subgroups, including neat solvents, binary mixtures and ternary NADES systems. Some methodological considerations of SANNs applications for future modelling were also provided. Although the developed protocol is focused exclusively on theophylline solubility, it also has general importance and can be used for the development of predictive models adequate for solvent screening of other compounds in a variety of systems. Formulation of such a model offers rational guidance for the selection of proper candidates as solubilisers in the designed solvents screening.

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“…Among the most widely utilized approaches is the COSMO-RS model [ 72 , 73 ]. This approach was used for the prediction of solubility [ 74 , 75 ], solvent screening [ 76 , 77 , 78 ], as well as describing multiphase chemical equilibria [ 79 , 80 , 81 ]. It relies on the combination of the density functional theory calculations and statistical thermodynamics as post treatment [ 82 ], becoming a standard approach in many scientific and industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Theoretical Study on Theobromine Solubility Enhancement in Binary Aqueous Solutions and Ternary Designed Solvents

et al. 2021

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The solubility of theobromine was studied both experimentally and theoretically. The solubility was determined spectrophotometrically at 25 °C in neat organic solvents, aqueous binary mixtures, Natural Deep Eutectic Solvents (NADES) and ternary NADES mixtures with water. It was found that addition of water in unimolar proportions with some organic solvents increases theobromine solubility compared to neat solvents. Additionally, using NADES results in a solubility increase of the studied compound not only in relation to water but also DMSO. The addition of water (0.2 molar fraction) to NADES is responsible for an even larger increase of solubility. The measured solubilities were interpreted in terms of three theoretical frameworks. The first one—belonging to the set of data reduction techniques—proved to be very efficient in quantitative back-computations of excess solubility of theobromine in all studied systems. The default approach utilizing the well-recognized COSMO-RS (Conductor-like Screening Model for Real Solvents) framework offered at most a qualitative solubility description. The extended search for possible contacts provided evidence for the existence of many intermolecular complexes that alter the electron density of the solute molecule, thus influencing solubility computations. Taking into account such intermolecular contacts by using the COSMO-RS-DARE (Conductor-like Screening Model for Realistic Solvation-Dimerization, Aggregation, and Reaction Extension) framework seriously increased the accuracy of solubility computations.

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Evaluation of COSMO-RS for solid–liquid equilibria prediction of binary eutectic solvent systems

Cited by 41 publications

References 35 publications

Group and group‐interaction contribution method for estimating the melting temperatures of deep eutectic solvents

Group and group‐interaction contribution method for estimating the melting temperatures of deep eutectic solvents

Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning

Experimental and Theoretical Study on Theobromine Solubility Enhancement in Binary Aqueous Solutions and Ternary Designed Solvents

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