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As ionic liquids (ILs) continue to be prepared, there is a growing need to develop theoretical methods for predicting the properties of ILs, such as gas solubility. In this work, different strategies were employed to obtain the solubility of CO2 and N2, where a conductor-like screening model for real solvents (COSMO-RS) was used as the basis. First, experimental data on the solubility of CO2 and N2 in ILs were collected. Then, the solubility of CO2 and N2 in ILs was predicted using COSMO-RS based on the structures of cations, anions, and gases. To further improve the performance of COSMO-RS, two options were used, i.e., the polynomial expression to correct the COSMO-RS results and the combination of COSMO-RS and machine learning algorithms (eXtreme Gradient Boosting, XGBoost) to develop a hybrid model. The results show that the COSMO-RS with correction can significantly improve the prediction of CO2 solubility, and the corresponding average absolute relative deviation (AARD) is decreased from 43.4% to 11.9%. In contrast, such an option cannot improve that of the N2 dataset. Instead, the results obtained from coupling machine learning algorithms with the COSMO-RS model agree well with the experimental results, with an AARD of 0.94% for the solubility of CO2 and an average absolute deviation (AAD) of 0.15% for the solubility of N2. [ABSTRACT FROM AUTHOR]

Deep eutectic solvents (DESs) are popular green media used for various industrial, pharmaceutical, and biomedical applications. However, the possible compositions of eutectic systems are so numerous that it is impossible to study all of them experimentally. To remedy this limitation, the solubility landscape of selected active pharmaceutical ingredients (APIs) in choline chloride- and betaine-based deep eutectic solvents was explored using theoretical models based on machine learning. The available solubility data for the selected APIs, comprising a total of 8014 data points, were collected for the available neat solvents, binary solvent mixtures, and DESs. This set was augmented with new measurements for the popular sulfa drugs in dry DESs. The descriptors used in the machine learning protocol were obtained from the σ-profiles of the considered molecules computed within the COSMO-RS framework. A combination of six sets of descriptors and 36 regressors were tested. Taking into account both accuracy and generalization, it was concluded that the best regressor is nuSVR regressor-based predictive models trained using the relative intermolecular interactions and a twelve-step averaged simplification of the relative σ-profiles. [ABSTRACT FROM AUTHOR]

The removal of nitrogen compounds from fuel via the conventional method, which is hydrodenitrogenation, is costly and involves catalysts and energy-intensive conditions (600 K and 300 atm). Recently, ionic liquids (ILs) have emerged as a promising alternative solvent for the denitrogenation of fuel oil. However, certain ILs are expensive and challenging to synthesize, prompting the exploration of protic ionic liquid (PIL) substitutes, which offer similar advantages to ILs. This study utilized the conductor-like screening model for real solvents (COSMO-RS) to predict the phase equilibria for three PILs—triethylammonium p-toluenesulfonate (TEA-TSA), triethylammonium salicylate (TEA-SA) and triethylammonium benzoate (TEA-BZ)—which were subsequently validated through experimental investigations. Liquid–liquid extraction experiments were conducted at 298 K and 1 atm, with pyrrole (serving as the model nitrogen compound) concentrations in n-hexadecane (representing the model fuel) ranging from 10 to 50 wt%. Additionally, the NRTL model effectively correlated the experimental tie lines. The obtained data indicated that TEA-TSA exhibited superior selectivity and distribution ratio compared to TEA-SA and TEA-BZ. All the ternary systems tested displayed positive slopes, suggesting a higher affinity of nitrogen compounds for the PIL. Supporting this observation, interaction energy (ΔE) and excess enthalpy (HE) were employed. The predicted outcomes revealed that TEA-TSA had high ΔE, and all PILs exhibited negative values of HE. The HE calculation underscored the significance of strong hydrogen bond interactions between pyrrole and the PIL for successful extraction. [ABSTRACT FROM AUTHOR]

Deep eutectic solvents (DES) represent a promising class of green solvents, offering particular utility in the extraction and development of new formulations of natural compounds such as ferulic acid (FA). The experimental phase of the study undertook a systematic investigation of the solubility of FA in DES, comprising choline chloride or betaine as hydrogen bond acceptors and six different polyols as hydrogen bond donors. The results demonstrated that solvents based on choline chloride were more effective than those based on betaine. The optimal ratio of hydrogen bond acceptors to donors was found to be 1:2 molar. The addition of water to the DES resulted in a notable enhancement in the solubility of FA. Among the polyols tested, triethylene glycol was the most effective. Hence, DES composed of choline chloride and triethylene glycol (TEG) (1:2) with added water in a 0.3 molar ration is suggested as an efficient alternative to traditional organic solvents like DMSO. In the second part of this report, the affinities of FA in saturated solutions were computed for solute-solute and all solute-solvent pairs. It was found that self-association of FA leads to a cyclic structure of the C 2 8 type, common among carboxylic acids, which is the strongest type of FA affinity. On the other hand, among all hetero-molecular bi-complexes, the most stable is the FA-TEG pair, which is an interesting congruency with the high solubility of FA in TEG containing liquids. Finally, this work combined COSMO-RS modeling with machine learning for the development of a model predicting ferulic acid solubility in a wide range of solvents, including not only DES but also classical neat and binary mixtures. A machine learning protocol developed a highly accurate model for predicting FA solubility, significantly outperforming the COSMO-RS approach. Based on the obtained results, it is recommended to use the support vector regressor (SVR) for screening new dissolution media as it is not only accurate but also has sound generalization to new systems. [ABSTRACT FROM AUTHOR]

Stabilized enzymes are crucial for the industrial application of biocatalysis due to their enhanced operational stability, which leads to prolonged enzyme activity, cost-efficiency and consequently scalability of biocatalytic processes. Over the past decade, numerous studies have demonstrated that deep eutectic solvents (DES) are excellent enzyme stabilizers. However, the search for an optimal DES has primarily relied on trial-and-error methods, lacking systematic exploration of DES structure-activity relationships. Therefore, this study aims to rationally design DES to stabilize various dehydrogenases through extensive experimental screening, followed by the development of a straightforward and reliable mathematical model to predict the efficacy of DES in enzyme stabilization. A total of 28 DES were tested for their ability to stabilize three dehydrogenases at 30°C: (S)-alcohol dehydrogenase from Rhodococcus ruber (ADH-A), (R)-alcohol dehydrogenase from Lactobacillus kefir (Lk-ADH) and glucose dehydrogenase from Bacillus megaterium (GDH). The residual activity of these enzymes in the presence of DES was quantified using first-order kinetic models. The screening revealed that DES based on polyols serve as promising stabilizing environments for the three tested dehydrogenases, particularly for the enzymes Lk-ADH and GDH, which are intrinsically unstable in aqueous environments. In glycerol-based DES, increases in enzyme half-life of up to 175-fold for Lk-ADH and 60-fold for GDH were observed compared to reference buffers. Furthermore, to establish the relationship between the enzyme inactivation rate constants and DES descriptors generated by the Conductor-like Screening Model for Real Solvents, artificial neural network models were developed. The models for ADH-A and GDH showed high efficiency and reliability (R² > 0.75) for in silico screening of the enzyme inactivation rate constants based on DES descriptors. In conclusion, these results highlight the significant potential of the integrated experimental and in silico approach for the rational design of DES tailored to stabilize enzymes. [ABSTRACT FROM AUTHOR]

The present investigation examines the antimicrobial and antifungal characteristics of natural deep eutectic solvents (NADES) and apple vinegar in relation to a diverse array of bacterial and fungal strains. The clinical bacterial strains, including gram-negative and gram-positive, and the fungal pathogen Candida albicans, were subjected to solid medium diffusion to determine the inhibitory effects of these compounds. The results show that NADES has superior antimicrobial and antifungal action compared to apple vinegar. The observed inhibitory zones for apple vinegar and NADES varied in length from 16.5 to 24.2 and 16 to 52.5 mm, respectively. The results obtained indicate that no synergy is observed for this mixture (50% AV + 50% NADES). The range of values for bactericidal concentrations (MBC) and minimal inhibitory concentrations (MIC) was 0.0125 to 0.2 and 0.0125 to 0.4 µl/ml, respectively. Antibacterial and antifungal chemicals may be found in apple vinegar and NADES, with NADES offering environmentally safe substitutes for traditional antibiotics. Additional investigation is suggested to refine these compounds for a wide range of bacteria, which could create antimicrobial solutions that are both highly effective and specifically targeted, thereby offering extensive potential in medicine and the environment. [ABSTRACT FROM AUTHOR]

Deep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental solubility data were collected for all DES systems. A machine learning model was developed using COSMO-RS molecular descriptors to predict solubility. All studied DESs exhibited a cosolvency effect, increasing drug solubility at modest concentrations of water. The model accurately predicted solubility for ibuprofen, ketoprofen, and related analogs (flurbiprofen, felbinac, phenylacetic acid, diphenylacetic acid). A machine learning approach utilizing COSMO-RS descriptors enables the rational design and solubility prediction of DES formulations for improved pharmaceutical applications. [ABSTRACT FROM AUTHOR]

Over the past few years, eutectic solvents (ESs) have drawn the scientific community's attention because they are usually more environmentally friendly than traditional organic solvents. One of the applications of ESs is in the gas capture field, where they are considered promising absorbers to replace amine- (MEA, DEA, or MDEA processes), methanol- (Rectisol process), dimethyl ethers of polyethylene glycol- (Selexol process), N-methyl-2-pyrrolidone- (Purisol process), propylene carbonate- (Fluor solvent process), or morpholine-based (Morphysorb process) solvents on CO2 capture from the atmosphere. Although several studies have reported experimental gas solubility data in ESs, especially for CO2, only a few existing options are covered. In fact, resorting to experimental methods to obtain the solubility data seems unfeasible considering the vast number of possible eutectic mixtures. Therewith, theoretical predictions of gas solubility in ESs are valuable for the fast pre-screening of prospective solvents. In this work, the ability of the thermodynamic model COSMO-RS to represent solubility data of CO2, CH4, and H2S in 17 choline chloride-based (ChCl) ESs was evaluated. The experimental data were collected from the literature at different molar ratios, at 298.15 K or 313.15 K, and in the pressure range from 1 to 125 bar. COSMO-RS offers a qualitative description of these gases' solubility, which was expected due to the model's fully predictive character. To improve the CO2 and CH4 solubility data description, a temperature–pressure-dependent correction was applied to the COSMO-RS predictions for these gases, offering a global average relative deviation of 15%. [ABSTRACT FROM AUTHOR]

The current study comprehensively investigates the adsorption behavior of chromium (Cr(III)) in wastewater using Algerian kaolinite clay. The structural and textural properties of the kaolinite clay are extensively characterized through a range of analytical methods, including XRD, FTIR, SEM-EDS, XPS, laser granulometry, N2 adsorption isotherm, and TGA–DTA. The point of zero charge and zeta potential are also assessed. Chromium adsorption reached equilibrium within five minutes, achieving a maximum removal rate of 99% at pH 5. Adsorption equilibrium is modeled using the Langmuir, Freundlich, Temkin, Elovich, and Dubinin–Radushkevitch equations, with the Langmuir isotherm accurately describing the adsorption process and yielding a maximum adsorption capacity of 8.422 mg/g for Cr(III). Thermodynamic parameters suggest the spontaneous and endothermic nature of Cr(III) sorption, with an activation energy of 26.665 kJ/mol, indicating the importance of diffusion in the sorption process. Furthermore, advanced DFT computations, including COSMO-RS, molecular orbitals, IGM, RDG, and QTAIM analyses, are conducted to elucidate the nature of adsorption, revealing strong binding interactions between Cr(III) ions and the kaolinite surface. The integration of theoretical and experimental data not only enhances the understanding of Cr(III) removal using kaolinite but also demonstrates the effectiveness of this clay adsorbent for wastewater treatment. Furthermore, this study highlights the synergistic application of empirical research and computational modeling in elucidating complex adsorption processes. [ABSTRACT FROM AUTHOR]

Solubility is not only a crucial physicochemical property for laboratory practice but also provides valuable insight into the mechanism of saturated system organization, as a measure of the interplay between various intermolecular interactions. The importance of these data cannot be overstated, particularly when dealing with active pharmaceutical ingredients (APIs), such as dapsone. It is a commonly used anti-inflammatory and antimicrobial agent. However, its low solubility hampers its efficient applications. In this project, deep eutectic solvents (DESs) were used as solubilizing agents for dapsone as an alternative to traditional solvents. DESs were composed of choline chloride and one of six polyols. Additionally, water–DES mixtures were studied as a type of ternary solvents. The solubility of dapsone in these systems was determined spectrophotometrically. This study also analyzed the intermolecular interactions, not only in the studied eutectic systems, but also in a wide range of systems found in the literature, determined using the COSMO-RS framework. The intermolecular interactions were quantified as affinity values, which correspond to the Gibbs free energy of pair formation of dapsone molecules with constituents of regular solvents and choline chloride-based deep eutectic solvents. The patterns of solute–solute, solute–solvent, and solvent–solvent interactions that affect solubility were recognized using Orange data mining software (version 3.36.2). Finally, the computed affinity values were used to provide useful descriptors for machine learning purposes. The impact of intermolecular interactions on dapsone solubility in neat solvents, binary organic solvent mixtures, and deep eutectic solvents was analyzed and highlighted, underscoring the crucial role of dapsone self-association and providing valuable insights into complex solubility phenomena. Also the importance of solvent–solvent diversity was highlighted as a factor determining dapsone solubility. The Non-Linear Support Vector Regression (NuSVR) model, in conjunction with unique molecular descriptors, revealed exceptional predictive accuracy. Overall, this study underscores the potency of computed molecular characteristics and machine learning models in unraveling complex molecular interactions, thereby advancing our understanding of solubility phenomena within the scientific community. [ABSTRACT FROM AUTHOR]

Estrogens are emergent pollutants found in low levels in water bodies, and for this reason they require concentration for quantification. In this study, aqueous two‐phase systems (ATPSs) were used as an alternative strategy to improve hormone detection and quantification from real water samples. In addition, a predictive computational method (COSMO‐RS) was used to understand better the migration of hormones between the two phases at the molecular level. Water samples previously analyzed as hormone‐free were added to the 250‐fold concentrated ATPS, where it was possible to detect and quantify concentrations of these micropollutants in the range of 9.3–29.19 ng L−1. [ABSTRACT FROM AUTHOR]

As a volatile organic compound (VOC), styrene can pose significant risks to human health and environment if released into the air without proper treatment. Absorption based on ionic liquids (ILs) has been widely regarded as a promising VOC treatment approach, whereas there is limited research on capturing styrene by ILs. In this work, a multilevel framework was proposed to screen IL absorbents from the largest number of cation-anion combinations in the latest COSMObase. The COSMO-RS model is applied to estimate the absorption and desorption potentials of all IL candidates for styrene capture based on solubility prediction, while important physical properties of them namely melting point, viscosity, and toxicity are estimated by a recently-reported deep learning model. A parallel screening specific to the commercially available ILs in the initial candidate database was also performed. The top-ranked three ILs, identified in both scenarios with and without limiting the commercially availability, were further evaluated in a simulated absorption-desorption process in Aspen Plus. The most promising ILs in each scenario were figured out, which vividly demonstrates the value of discovering novel ILs. [Display omitted] • Propose a multilevel framework for screening ionic liquid (IL) absorbents. • Present two parallel scenarios with and without limiting commercial availability. • Find top 3 ILs from 347406 hypothetical and 108 available candidates, respectively. • Demonstrate the value of discovering novel ILs guided by large-scale screening. [ABSTRACT FROM AUTHOR]

Ionic liquids (ILs) is an effective way to convert and remove hydrogen sulfide, H 2 S from natural gas streams. Amine-based ILs have recently been discovered due to their efficiency of removing H 2 S due to their excellent characteristics. However, the selection of potential amine-based ILs from a wide range combination of cations and amine-based anions are rather challenging if performed experimentally. Conductor-like Screening Model for Real Solvents (COSMO-RS) was used for the prediction of liquids thermodynamics such as activity coefficient at infinite dilution, selectivity, capacity and performance index (PI) values to evaluate the performance of each amine-based ILs and determine the most efficient ILs for H 2 S absorption without requiring any experimental data. A total of 42 previously unexplored ionic liquids, amine-based ILs from a combination of 7 amine cations and 6 anions were screened by COSMO-RS model. The findings from COSMO-RS prediction results revealed that ILs without aromatic rings displayed higher efficiencies compared to ILs with aromatic rings. Moreover, N,N,N′,N′-tetramethyl-1,3-propanediamine chloride, [TMPDA][Cl] cation-anion combinations displayed the highest PI value of 52.55 among the screened ILs thus, predicted as the most efficient ILs for H 2 S absorption. The experimental results obtained validated the COSMO-RS predictions with an approximation of ± 4.51% discrepancies between predicted and experimental data. Hence, it is concluded that COSMO-RS can be used for a reliable pre-experimental prediction and amine-based ILs approach has a huge potential for effective H 2 S absorption in industrial applications. [Display omitted] • Successfully screened an effective amine-based ILs for H 2 S absorption using COSMO-RS approach. • The synthesized ABILs possess desirable physical properties for H 2 S absorption. • The predicted results obtained were in agreement with the experimental data. • ILs of [TMPDA][Cl] displayed the highest removal efficiency for H 2 S absorption. [ABSTRACT FROM AUTHOR]

Context: One of the prevalent methods for evaluating separation performance is to predict the interactions of solvent and solute molecules. The infinite dilution activity coefficient, Gibbs free energy, and sigma profiles provided insights into the solubilization of a solute and revealed the intensity of the solution's molecular interactions. The effective thermodynamic tools (infinite dilution activity coefficient, Gibbs free energy) were evaluated for predicting the efficiency of 18 polar and non-polar organic solvents in rubber seed oil (RSO) extraction. An infinite dilution activity coefficient was computed to evaluate the solubility of the rubber seed oil model compound (linoleic acid) in the organic solvents. Gibbs free energy was evaluated to show the energy change associated with the molecules mixing process and forecast the miscibility of linoleic acid molecules in the solvents. Moreover, the study examined the sigma profiles and sigma surfaces of organic solvents and linoleic acid to acquire a deeper insight into their similarities and how they interact molecularly. According to the computational prediction and experimental verification, the thermodynamic properties of Gibbs free energy and activity coefficient proved to be highly effective tools for screening polar and moderately polar solvents, predicting the molecular interactions with solute. Whereas the sigma profile and sigma surface were found to be the most efficient tools for evaluating the efficacy of non-polar solvents. Solvents with moderate polarity, such as tetrahydrofuran and diethyl ether, as well as non-polar solvents like pentane, heptane, and n-hexane, proved to be effective and favorable for oil extraction, resulting in the highest oil yields of approximately 27.0%. Overall, the COSMO-RS method demonstrates its utility in estimating the solubility of RSO in organic solvents, enabling early identification of the most effective solvent. Methods: The initial geometry optimization of every component was conducted through density functional theory (DFT) using TmoleX software. A single-point density functional theory (DFT) computation using Becke Perdew 86 (BP86) and the Triple-Zeta Valence Potential (TZVPD) was performed to produce.cosmo files. COSMO-RS calculations were performed by applying the parameterization file BP_TZVPD_FINE_19.ctd using COSMOthermX software. The practical extraction of oil from plant seeds was performed using a sonicator bath to verify the accuracy of the COSMO-RS predictions. [ABSTRACT FROM AUTHOR]

The solid-liquid equilibrium and thermodynamic properties of FOX-7 in three binary mixed solvents (N-methyl pyrrolidone-water, N,N-dimethylacetamide-water and 1,4-Butyrolactone-water) are studied by focused beam reflectance measurement (FBRM) dynamic detection method and the solubility is predicted by conductor-like screening model for real solvents (COSMO-RS) under atmospheric pressure from 298.15 to 343.15 K. The results show that the solubility of FOX-7 increase with the increasing temperature and the mole fraction of W-methyl pyrrolidone, N,N-dimethylacetamide and 1,4-Butyrolactone. The solubility data can be well correlated by Apelblat, van't Hoff and Yaws equation. The data of hybrid thermodynamic parameters (including the hybrid enthalpy, entropy, and Gibbs energy) show that the dissolution process is endothermic, non-spontaneous and enthalpy driven. In addition, COSMO-RS can accurately predict the increasing trend of solubility with increasing of temperature and mole fraction of solvent. However, the COSMO-RS overestimates solubility in three binary solvents except for xNMP = 0.1575, the average mean squared quadratic error becomes larger with an increasing mole fraction of water. Solubility data and thermodynamic parameters can lay a foundation for the crystallization and spheroidization modification of FOX-7. [ABSTRACT FROM AUTHOR]

The Abraham solvation parameter model, known alternatively as the Linear Solvation-Energy Relationships (LSER) model, is critically examined for its capacity to predict the hydration free-energy for a variety of solutes. The very linearity of the LSER approach is reconsidered as regards the hydrogen-bonding contribution to solvation free energy. This is done by combining the equation-of-state solvation thermodynamics with the statistical thermodynamics of hydrogen bonding. Thus, this hydrogen-bonding contribution is placed on a firm thermodynamic basis and the predictive calculations are now possible with known acidity and basicity, A and B, molecular descriptors. The LFER coefficients are now expressed in terms of the A and B descriptors. The methodology for the derivation of the new linear equations for the hydrogen-bonding contribution to solvation free energy is presented along with examples of calculations. The implication for the exchange of information on intermolecular interactions between diverse Quantitative Structure–Property Relationships (QSPR) and other approaches is discussed. The proposed changes and descriptor adjustments augments the LSER capacity for solvent screening and use in numerous applications in the broader chemical, biochemical and environmental sector. A critical discussion of perspectives and the challenging issues is also presented. [ABSTRACT FROM AUTHOR]

Acid-base properties are the simplest expression of compounds' coordinating ability. In the present work, we studied in silico how the gas-phase Brønsted acidity (GA) of several polycyano-substituted compounds change when cyano (CN) groups are replaced by 1,2,2-tricyanovinyl (TCNV) groups in (iso)cyanic acid, dicyanoamine, cyanoform, and hydrogen tetracyanoborate. Different tautomers and conformers/isomers are included in this study. Gas-phase acidity values are compared with the acidities of various acids, including percyanated protonated monocarba-closo-dodecaborate (carborane acid) and dodecaborate, as well as hydrogen cyanide and 1,2,2-tricyanoethene. An estimation of acetonitrile (MeCN), dimethylsufoxide (DMSO), and 1,2-dichloroethane (DCE) acidities is presented using the COSMO-RS method and correlation analysis. The strongest acid with four TCNV groups shows remarkable acidic properties. [ABSTRACT FROM AUTHOR]

The conventional hydrodenitrogenation method is expensive and involves the use of catalysts and harsh procedures. In the last few years, ionic liquids (ILs) have gained attention as a promising alternative solvent for fuel oil extractive denitrogenation. In this work, the Conductor-like Screening Model for Real Solvents (COSMO-RS) was used to screen 173 potential ILs as solvents for fuel oil. Two ILs (1-ethyl-3-methylimidazolium dicyanamide ([EMIM][N(CN)2]) and 1-ethyl-3-methylimidazolium methanesulfonate ([EMIM][MeSO3])) were selected for experimental investigation. The experimental liquid–liquid extraction of pyrrole (taken as the model nitrogen compound) from n-hexadecane (the model fuel) was conducted at 298 K and 1 atm with feed concentrations of pyrrole ranging from 10 to 50 wt%, using either the two pure ILs or their mixtures with dimethylformamide or ethylene glycol. Moreover, the NRTL model was effectively used to correlate the experimental tie lines. This work shows that the use of a binary mixture of ILs with a conventional solvent results in good selectivity, but has a low capacity for extracting pyrrole compounds. On the other hand, using an IL-IL mixture exhibits good results for both capacity and selectivity. All the ternary systems tested showed positive slopes, indicating that the nitrogen compounds had a higher affinity for the IL and binary mixture extract phase. In fact, the extraction efficiency for all the systems shows promising results. This characteristic is advantageous, as it requires less solvent to remove nitrogen compounds. [ABSTRACT FROM AUTHOR]

Solubility prediction and intermolecular interaction of four explosives (1,3,5-Trinitroperhydro-1,3,5-triazine,ε‑2,4,6,8,10,12-Hexanitro-2,4,6,8,10,12-hexaazaiso-wurtzitane, 3-Nitro-1,2,4-triazolin-5-one，3,4-dinitro-1 H-pyrazole) are researched by the Conductor-like Screening Model – Realistic solvents (COSMO-RS). The results show that the study of σ-surface and σ-profile can qualitatively analyze the potential mechanism of the dissolution behavior of four explosives in the studied solvents. COSMO-RS can accurately predict the solubility of four explosives in the studied solvents, but some of the predicted solubility is different from the experimental solubility, and the deviation between the predicted solubility and the experimental solubility was analyzed. The molecular interaction energy between explosive molecules and solvent partially reveals the internal mechanism of the dissolution of explosives, however, the solvation of the four explosives is a complex process that requires the synthesis of various molecular interactions. This has a great effect on the study of solvation of explosives, and it is of great significance for solvent screening of other explosives molecules. [ABSTRACT FROM AUTHOR]

Using ionic liquids to capture CO to produce high-purity hydrogen (H 2) is a promising method. However, there is a lack of theoretical screening for CO capture, which can reduce the number of experiments and costs. In addition, ionic liquids need to reduce their viscosity and maintain an excellent CO capture effect. Therefore, The COSMO-RS was used to screen 300 ionic liquids using CO/H 2 selectivity and viscosity at 293.15 K as indicators. The results showed that the target ionic liquid screened was [EimH][CuCl 2 ]. The addition of ethylene glycol (EG) reduced the viscosity of [EimH][CuCl 2 ] and improved the CO capture ability. After adding 25 wt% EG, its separation factor of CO/H 2 could reach 180 at 293.15 K and 2.1 MPa, which was 59% higher compared to the pure ionic liquid. Furthermore, EG inhibits foaming, and the mixed solvent could maintain high stability after recycling. [Display omitted] • Solvent screening from 300 ionic liquids by COSMO-RS. • Mechanistic analysis by σ -profiles and Infrared spectroscopy. • Solvent improvement by addition of ethylene glycol to protic ionic liquid. • Experiments show the CO/H 2 separation factor using mixed solvent reach 180. [ABSTRACT FROM AUTHOR]

This study explores the edaravone solubility space encompassing both neat and binary dissolution media. Efforts were made to reveal the inherent concentration limits of common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured, which made the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called for an extension of the available pool of edaravone solubility data. Hence, new measurements were performed to collect edaravone solubility values in polar non-protic and diprotic media. Such an extended set of data was used in the machine learning process for tuning the parameters of regressor models and formulating the ensemble for predicting new data. In both phases, namely the model training and ensemble formulation, close attention was paid not only to minimizing the deviation of computed values from the experimental ones but also to ensuring high predictive power and accurate solubility computations for new systems. Furthermore, the environmental friendliness characteristics determined based on the common green solvent selection criteria, were included in the analysis. Our applied protocol led to the conclusion that the solubility space defined by ordinary solvents is limited, and it is unlikely to find solvents that are better suited for edaravone dissolution than those described in this manuscript. The theoretical framework presented in this study provides a precise guideline for conducting experiments, as well as saving time and resources in the pursuit of new findings. [ABSTRACT FROM AUTHOR]

The refractive index (RI) of liquids is a key physical property of molecular compounds and materials. In addition to its ubiquitous role in physics, it is also exploited to impart specific optical properties (transparency, opacity, and gloss) to materials and various end-use products. Since few methods exist to accurately estimate this property, we have designed a graph machine model (GMM) capable of predicting the RI of liquid organic compounds containing up to 16 different types of atoms and effective in discriminating between stereoisomers. Using 8267 carefully checked RI values from the literature and the corresponding 2D organic structures, the GMM provides a training root mean square relative error of less than 0.5%, i.e., an RMSE of 0.004 for the estimation of the refractive index of the 8267 compounds. The GMM predictive ability is also compared to that obtained by several fragment-based approaches. Finally, a Docker-based tool is proposed to predict the RI of organic compounds solely from their SMILES code. The GMM developed is easy to apply, as shown by the video tutorials provided on YouTube. [ABSTRACT FROM AUTHOR]

Solid–liquid equilibria (SLE) of binary mixtures of several n-alkanes (n-octadecane, n-eicosane, n-tetracosane, n-pentacosane, n-triacontane) and dibenzofuran covering the whole composition range were measured by differential scanning calorimetry (DSC) in a temperature range of 301–356 K. The dibenzofuran and the n-alkanes are completely miscible in the liquid state but non-miscible in the solid state, and in the context of this work, they seem to exhibit eutectic behavior. A linear trend is obtained for the eutectic temperature and eutectic composition versus the number of carbon atoms of n-alkane. The experimental data were compared to predictions made by using the ideal solution model, the DISQUAC model and several versions of the UNIFAC model, including the classical UNIFAC, the modified versions of Lyngby and Dortmund, and the recently proposed modified UNIFAC (NIST) model, to account for non-ideality in the liquid phase. Moreover, the experimental data were also compared with COSMO-RS predictions that utilize quantum chemical calculations and statistical thermodynamics to interpret the behavior of molecular interactions in the binary mixture. A good agreement was obtained between the predicted and the experimental temperatures. [ABSTRACT FROM AUTHOR]

Dapsone is an effective antibacterial drug used to treat a variety of conditions. However, the aqueous solubility of this drug is limited, as is its permeability. This study expands the available solubility data pool for dapsone by measuring its solubility in several pure organic solvents: N-methyl-2-pyrrolidone (CAS: 872-50-4), dimethyl sulfoxide (CAS: 67-68-5), 4-formylmorpholine (CAS: 4394-85-8), tetraethylene pentamine (CAS: 112-57-2), and diethylene glycol bis(3-aminopropyl) ether (CAS: 4246-51-9). Furthermore, the study proposes the use of intermolecular interactions as molecular descriptors to predict the solubility of dapsone in neat solvents and binary mixtures using machine learning models. An ensemble of regressors was used, including support vector machines, random forests, gradient boosting, and neural networks. Affinities of dapsone to solvent molecules were calculated using COSMO-RS and used as input for model training. Due to the polymorphic nature of dapsone, fusion data are not available, which prohibits the direct use of COSMO-RS for solubility calculations. Therefore, a consonance solvent approach was tested, which allows an indirect estimation of the fusion properties. Unfortunately, the resulting accuracy is unsatisfactory. In contrast, the developed regressors showed high predictive potential. This work documents that intermolecular interactions characterized by solute–solvent contacts can be considered valuable molecular descriptors for solubility modeling and that the wealth of encoded information is sufficient for solubility predictions for new systems, including those for which experimental measurements of thermodynamic properties are unavailable. [ABSTRACT FROM AUTHOR]

Solubilities of mono‐aminoacridines (AAs) in a variety of solvents were studied computationally using COSMO‐RS method and selectively confirmed by experiment (liquid chromatography and gravimetry). The results confirm the reliability of COSMO‐RS for ranking solvents on the basis of dissolving a specific AA, but not ranking the solubilities of different AAs in the same solvent. Pyrrolidine and some pyrrolidine‐based mixtures were predicted to deliver outstandingly high solubilities of AAs (10–30 % by mass, depending on the compound). Hydrogen bonding was found to have a considerable role in the solubility of AAs, which can act as hydrogen bond donors and acceptors. The obtained data and conclusions can facilitate solvent selection for purification of AAs, sample preparation for MALDI‐MS if an AA is used as a matrix, and can be helpful for solubility‐related applications involving other compounds. [ABSTRACT FROM AUTHOR]

Formaldehyde is an emerging human carcinogen from gaseous spark ignition engines and in aqueous stream. In addition to causing leukaemia and nasal cancer, the emissions harm the environment. The present study examined ionic liquids (ILs) as greener alternatives for removing formaldehyde from different sources. Because of its reliability and excellent predicting ability a conductor-like screening model for real solvents (COSMO-RS) were employed as a simulation means to evaluate potential ILs for formaldehyde. Screened IL can be used as an extracting agent, an adsorbent, a carrier, or an absorbent in formaldehyde separation. 392 different cation-anion combinations were screened. To measure the efficacy of IL, the activity coefficient at infinite dilution, capacity, selectivity, and interaction energies were predicted for all 1600 ILs combinations. Short-chain quaternary ammonium cations were found to be potentially effective compared to long-chain and aromatic cations. Moreover, halogenated and food-grade anions such as fluoride, acetate, and lysinate showed higher capacity and selectivity. Whereas than weakly coordinating anions such as [AsF 6 ] and [BCl 4 ] were observed to be least effective for formaldehyde separation. ILs tetramethylammonium hydroxide [TMAmm][OH], tetramethylammonium fluoride [TMAmm][F], tetramethylammonium acetate [TMAmm][Ac], and [TMAmm][Lys] were found to be the most effective separating agents for formaldehyde. This study will facilitate the selection and design of biobased ILs for the separation of formaldehyde. [Display omitted] • Conductor-like screening model for real solvents was used to screen ILs for carcinogenic formaldehyde. • A total of 1600 cation-anion combinations were screened for formaldehyde separation. • Tetramethylammonium cation along with highly electronegative anions [F-], [OH-] were potential for formaldehyde separation. • Food grade anions [Ac], [Lys], etc showed significant results and can be designed for the separation of formaldehyde. • The screened ILs can be used as an adsorbent, absorbent, carrier, polymer etc for formaldehyde separation. [ABSTRACT FROM AUTHOR]

The SAMPL8 blind prediction challenge, which addresses the acid/base dissociation constants (pKa) and the distribution coefficients (logD), was addressed by the Conductor like Screening Model for Realistic Solvation (COSMO-RS). Using the COSMOtherm implementation of COSMO-RS together with a rigorous conformational sampling, yielded logD predictions with a root mean square deviation (RMSD) of 1.36 log units over all 11 compounds and seven bi-phasic systems of the data set, which was the most accurate of all contest submissions (logD). For the SAMPL8 pKa competition, participants were asked to report the standard state free energies of all microstates, which were then used to calculate the macroscopic pKa. We have used COSMO-RS based linear free energy fit models to calculate the requested energies. The assignment of the calculated and experimental pKa values was made on the basis of the popular transitions, i.e. the transition hat was predicted by the majority of the submissions. With this assignment and a model that covers both, pKa and base pKa, we achieved an RMSD of 3.44 log units (18 pKa values of 14 molecules), which is the second place of the six ranked submissions. By changing to an assignment that is based on the experimental transition curves, the RMSD reduces to 1.65. In addition to the ranked contribution, we submitted two more data sets, one for the standard pKa model and one or the standard base pKa model of COSMOtherm. Using the experiment based assignment with the predictions of the two sets we received a RMSD of 1.42 log units (25 pKa values of 20 molecules). The deviation mainly arises from a single outlier compound, the omission of which leads to an RMSD of 0.89 log units. [ABSTRACT FROM AUTHOR]

Peanut hulls (Arachis hypogaea, Leguminosae), which are a side stream of global peanut processing, are rich in bioactive flavonoids such as luteolin, eriodictyol, and 5,7-dihydroxychromone. This study aimed to isolate these flavonoid derivatives by liquid-liquid chromatography with as few steps as possible. To this end, luteolin, eriodictyol and 5,7-dihydroxychromone were isolated from peanut hulls using two different techniques, high-performance countercurrent chromatography (HPCCC) and fast-centrifugal partition chromatography (FCPC). The suitability of the biphasic solvent system composed of n-hexane/ethyl acetate/methanol/water (1.0/1.0/1.0/1.5; v/v/v/v) was determined by the Conductor like Screening Model for Real Solvents (COSMO-RS), which allowed the partition ratio KD-values of the three main flavonoids to be calculated. After a one-step HPCCC separation of ~1000 mg of an ethanolic peanut hull extract, 15 mg of luteolin and 8 mg of eriodictyol were isolated with purities over 96%. Furthermore, 3 mg of 5,7-dihydroxychromone could be isolated after purification by semi-preparative reversed-phase liquid chromatography (semi-prep. HPLC) in purity of over 99%. The compounds were identified by electrospray ionization mass spectrometry (ESI-MS) and nuclear magnetic resonance spectroscopy (NMR). [ABSTRACT FROM AUTHOR]

This study investigated the solubility of benzenesulfonamide (BSA) as a model compound using experimental and computational methods. New experimental solubility data were collected in the solvents DMSO, DMF, 4FM, and their binary mixtures with water. The predictive model was constructed based on the best-performing regression models trained on available experimental data, and their hyperparameters were optimized using a newly developed Python code. To evaluate the models, a novel scoring function was formulated, considering not only the accuracy but also the bias–variance tradeoff through a learning curve analysis. An ensemble approach was adopted by selecting the top-performing regression models for test and validation subsets. The obtained model accurately back-calculated the experimental data and was used to predict the solubility of BSA in 2067 potential solvents. The analysis of the entire solvent space focused on the identification of solvents with high solubility, a low environmental impact, and affordability, leading to a refined list of potential candidates that meet all three requirements. The proposed procedure has general applicability and can significantly improve the quality and speed of experimental solvent screening. [ABSTRACT FROM AUTHOR]

A combination of molecular modelling and experiments was conducted to study the separation of acrylic acid and propionic acid through extractive distillation with polar aprotic entrainers. It was found that acrylic acid and propionic acid can be efficiently separated through extractive distillation with a polar aprotic entrainer, resulting in the entrainment of acrylic acid and making propionic acid the most volatile compound. Vapour-liquid equilibrium experiments were conducted with 25 wt% acrylic acid, 25 wt% propionic, and 50% wt% entrainer. Dimethyl sulfoxide (DMSO) was found to induce the highest relative volatility of 1.56 at 25 mbar, which agrees well with the predicted value of 1.66 using the simulation software COSMO-RS. The difference in pKa (ΔpKa = 0.63) between acrylic acid and propionic acid is the main cause for the enhanced relative volatility. The corresponding difference in hydrogen bonding strength was calculated using COSMO-RS to result in ΔE = 1.8 kJ/mol at 25 °C between acrylic acid and propionic acid with the total mixture. When dodecane was selected as an entrainer, acrylic acid became the most volatile compound instead of propionic acid. This is explained by the fact that propionic acid has two extra hydrogen atoms available to have a Van der Waals interaction with the carbon atoms of dodecane. The difference in total Van der Waals interaction between the two carboxylic acids in the mixture with dodecane is 1.34 kJ/mol at 25 °C. This difference results in a relative volatility of 0.91, which is too small to be exploited in a distillation process, and it was concluded that for this mixture, DMSO was the best entrainer in the study. [Display omitted] ● Acrylic acid and propionic acid were separated by extractive distillation (ED). ● COSMO-RS and Spartan calculations predicted and explained relative volatilities. ● The ΔpKa of 0.63 is sufficient for entrainers to induce relative volatility in ED. ● DMSO induced a relative volatility of 1.56 at 25 mbar using 50 wt% entrainer. ● Alkanes reverse the relative volatility to 0.9. [ABSTRACT FROM AUTHOR]

The flash point (FP) of pure or mixtures of flammable substances is an important indicator in designing fire risk mitigation and prevention measures in the chemical and oil industry. Although FP data of fuel constituents and blends are often found in the literature, studies addressing other flammable mixtures, such as essential oils (EOs) and their constituents (terpenes and terpenoids), are scarce. EOs are aromatic, volatile liquid mixtures extracted from plant matrices that present diverse biological properties and find numerous applications in the food, pharmaceutical, cosmetic, and fragrance industries. In this work, experimental FP measurements of binary and ternary mixtures of four structurally diverse monoterpenes (carvone, eucalyptol, limonene, and linalool), widely found in different EO profiles, were carried out. Besides, the Liaw-UNIFAC model and the COSMO-RS model were used to calculate the FP data of the studied mixtures, resulting in global root-mean-square deviations (RMSD) of 2.0 K and 0.7 K, respectively. Both models deliver better predictions than the ideal approach (RMSD = 2.4 K) for the studied systems, demonstrating the importance of considering nonideal effects when estimating the FP data of terpene mixtures. These results provide essential information for accurate process safety and fire risk assessment in the EO industry. [ABSTRACT FROM AUTHOR]

In this work, the solid–liquid equilibrium (SLE) of four binary systems combining two active pharmaceutical ingredients (APIs) capable of forming co-amorphous systems (CAMs) was investigated. The binary systems studied were naproxen-indomethacin, naproxen-ibuprofen, naproxen-probucol, and indomethacin-paracetamol. The SLE was experimentally determined by differential scanning calorimetry. The thermograms obtained revealed that all binary mixtures investigated form eutectic systems. Melting of the initial binary crystalline mixtures and subsequent quenching lead to the formation of CAM for all binary systems and most of the compositions studied. The experimentally obtained liquidus and eutectic temperatures were compared to theoretical predictions using the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state and conductor-like screening model for real solvents (COSMO-RS), as implemented in the Amsterdam Modeling Suite (COSMO-RS-AMS). On the basis of the obtained results, the ability of these models to predict the phase diagrams for the investigated API–API binary systems was evaluated. Furthermore, the glass transition temperature (Tg) of naproxen (NAP), a compound with a high tendency to recrystallize, whose literature values are considerably scattered, was newly determined by measuring and modeling the Tg values of binary mixtures in which amorphous NAP was stabilized. Based on this analysis, erroneous literature values were identified. [ABSTRACT FROM AUTHOR]

Utilization of ionic liquids (ILs) for carbon dioxide (CO2) capture is continuously growing, and further understanding of the factors that influence its solubility (notably for new ILs) is crucial. Herein, CO2 absorption of two 1,2,4-triazolium-based ILs was compared with imidazolium-based Ils of different anions, namely bis(trifluoromethylsulfonyl)imide, tetrafluoroborate, and glycinate. The CO2 absorption capacity was determined using an isochoric saturation method and compared with predicted solubility employing COnductor-like Screening Model for Real Solvents (COSMO-RS). To gain an understanding of the effects of cations and anions of the ILs on the CO2 solubility, the molecular orbitals energy levels were calculated using TURBOMOLE. Triazolium-based ILs exhibit higher absorption capacity when compared to imidazolium-based ILs for the same anions. The results also showed that the anions' energy levels are more determinant towards solubility than the cations' energy levels, which can be explained by the higher tendency of CO2 to accept electrons than to donate them. [ABSTRACT FROM AUTHOR]

Ibuprofen (Ibf) is a biologically active drug (BADs) and an emerging contaminant of concern (CECs) in aqueous streams. Due to its adverse effects upon aquatic organisms and humans, the removal and recovery of Ibf are essential. Usually, conventional solvents are employed for the separation and recovery of ibuprofen. Due to environmental limitations, alternative green extracting agents need to be explored. Ionic liquids (ILs), emerging and greener alternatives, can also serve this purpose. It is essential to explore ILs that are effective for recovering ibuprofen, among millions of ILs. The conductor-like screening model for real solvents (COSMO-RS) is an efficient tool that can be used to screen ILs specifically for ibuprofen extraction. The main objective of this work was to identify the best IL for the extraction of ibuprofen. A total of 152 different cation–anion combinations consisting of eight aromatic and non-aromatic cations and nineteen anions were screened. The evaluation was based upon activity coefficients, capacity, and selectivity values. Furthermore, the effect of alkyl chain length was studied. The results suggest that quaternary ammonium (cation) and sulfate (anion) have better extraction ability for ibuprofen than the other combinations tested. An ionic liquid-based green emulsion liquid membrane (ILGELM) was developed using the selected ionic liquid as the extractant, sunflower oil as the diluent, Span 80 as the surfactant, and NaOH as the stripping agent. Experimental verification was carried out using the ILGELM. The experimental results indicated that the predicted COSMO-RS and the experimental results were in good agreement. The proposed IL-based GELM is highly effective for the removal and recovery of ibuprofen. [ABSTRACT FROM AUTHOR]

The separation of dimethyl carbonate (DMC) and methanol is of great significance in industry. In this study, ionic liquids (ILs) were employed as extractants for the efficient separation of methanol from DMC. Using the COSMO-RS model, the extraction performance of ILs consisting of 22 anions and 15 cations was calculated, and the results showed that the extraction performance of ILs with hydroxylamine as the cation was much better. The extraction mechanism of these functionalized ILs was analyzed by molecular interaction and the σ-profile method. The results showed that the hydrogen bonding energy dominated the interaction force between the IL and methanol, and the molecular interaction between the IL and DMC was mainly Van der Waals force. The molecular interaction changes with the type of anion and cation, which in turn affects the extraction performance of ILs. Five hydroxyl ammonium ILs were screened and synthesized for extraction experiments to verify the reliability of the COSMO-RS model. The results showed that the order of selectivity of ILs predicted by the COSMO-RS model was consistent with the experimental results, and ethanolamine acetate ([MEA][Ac]) had the best extraction performance. After four regeneration and reuse cycles, the extraction performance of [MEA][Ac] was not notably reduced, and it is expected to have industrial applications in the separation of methanol and DMC. [ABSTRACT FROM AUTHOR]

For an extraction process to be economically feasible, selecting a suitable solvent is imperative. This work extends the computer‐aided molecular and process design (CAMPD) framework COSMO‐CAMPD for solvent design in an extraction‐distillation process by replacing a pinch‐based process model with a hybrid, rate‐based extraction‐distillation process model. The resulting CAMPD framework is able to evaluate solvent candidates by investment costs in addition to operating costs in a fully predictive manner. In a case study for purifying acetone, we show that our framework's guided exploration of a vast design space yields solvents with superior performance compared to a benchmark solvent from the literature. [ABSTRACT FROM AUTHOR]

Edaravone, acting as a cerebral protective agent, is administered to treat acute brain infarction. Its poor solubility is addressed here by means of optimizing the composition of the aqueous choline chloride (ChCl)-based eutectic solvents prepared with ethylene glycol (EG) or glycerol (GL) in the three different designed solvents compositions. The slurry method was used for spectroscopic solubility determination in temperatures between 298.15 K and 313.15 K. Measurements confirmed that ethaline (ETA = ChCl:EG = 1:2) and glyceline (GLE = ChCl:GL = 1:2) are very effective solvents for edaravone. The solubility at 298.15 K in the optimal compositions was found to be equal xE = 0.158 (cE = 302.96 mg/mL) and xE = 0.105 (cE = 191.06 mg/mL) for glyceline and ethaline, respectively. In addition, it was documented that wetting of neat eutectic mixtures increases edaravone solubility which is a fortunate circumstance not only from the perspective of a solubility advantage but also addresses high hygroscopicity of eutectic mixtures. The aqueous mixture with 0.6 mole fraction of the optimal composition yielded solubility values at 298.15 K equal to xE = 0.193 (cE = 459.69 mg/mL) and xE = 0.145 (cE = 344.22 mg/mL) for glyceline and ethaline, respectively. Since GLE is a pharmaceutically acceptable solvent, it is possible to consider this as a potential new liquid form of this drug with a tunable dosage. In fact, the recommended amount of edaravone administered to patients can be easily achieved using the studied systems. The observed high solubility is interpreted in terms of intermolecular interactions computed using the Conductor-like Screening Model for Real Solvents (COSMO-RS) approach and corrected for accounting of electron correlation, zero-point vibrational energy and basis set superposition errors. Extensive conformational search allowed for identifying the most probable contacts, the thermodynamic and geometric features of which were collected and discussed. It was documented that edaravone can form stable dimers stabilized via stacking interactions between five-membered heterocyclic rings. In addition, edaravone can act as a hydrogen bond acceptor with all components of the studied systems with the highest affinities to ion pairs of ETA and GLE. Finally, the linear regression model was formulated, which can accurately estimate edaravone solubility utilizing molecular descriptors obtained from COSMO-RS computations. This enables the screening of new eutectic solvents for finding greener replacers of designed solvents. The theoretical analysis of tautomeric equilibria confirmed that keto-isomer edaravone is predominant in the bulk liquid phase of all considered deep eutectic solvents (DES). [ABSTRACT FROM AUTHOR]

[Display omitted] • Boric acid-based deep eutectic solvents intensified the esterification of glycerol with lauric acid. • The intensified process of the selective esterification predicted by COSMO-RS theory. • Deep eutectic solvent formed from tetrapropylammonium bromide and boric acid exhibited superior monoester selectivity. • Temperature controlled recovery of DES and unreacted glycerol. Glyceryl monolaurate (GML), as one of widely used industrial chemicals, can be synthesized via Fischer esterification of lauric acid (LA) and glycerol (GL). In this work, we screened boric acid-based deep eutectic solvent (DES) formed from tetrapropylammonium bromide (TPAB) and boric acid to intensify the selective Fischer esterification through conductor-like screening model for realistic solvents (COSMO-RS) theory. The solid liquid equilibrium (SLE) phase behavior, hydrogen bond formation process, and thermal stability of the DES were characterized by differential scanning calorimetry (DSC), Fourier transform infrared spectrometry (FT-IR), and thermogravimetric analyzer (TGA). The results verified that equimolar TPAB and boric acid formed DES [TPAB:B(OH) 3 ] with the lowest freezing point and very good thermal stability could efficiently intensify the Fischer esterification of GL and LA to GML. The hydrogen bond accepter TPAB of the DES made the esterification system homogeneous, which promoted the selective esterification process. In addition, the hydrogen bond donor B(OH) 3 of the [TPAB:B(OH) 3 ] combined with diol unit of GL to cyclic boric esters and led to a special selective space effect, which also improved the selectivity to GML. Thus, the esterification intensified of GL and LA by 20 wt% DES achieved GML selectivity of 85.7 % and yield of 76.4 % under the optimal reaction conditions. The esterification kinetics suggested the reaction followed pseudo-second-order model and the reaction activation energy was 33.98 kJ mol−1, similar to and/or lower than those reported works. Furthermore, DES and unreacted glycerol were readily recovered and reused through temperature controlled gravity-based separation due to the ability of homogeneity under reaction conditions and splitting phase behavior during the DES recovery. The results can expand the boric acid-based DESs for other potential intensification applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • The solubility of ten main food flavonoids in NADES systems were compared. • Flavonoid solubility depended mainly on the structural characteristics. • Catechin demonstrated the highest solubility across all solvent systems. • NADES based on 1,2-PD notably enhanced the solubility of quercetin and luteolin. This study investigates the solubility of ten primary food flavonoids in both conventional solvents and natural deep eutectic solvents (NADES), utilizing five choline chloride-based NADES. These NADES were formulated with lactic acid, ascorbic acid, urea, sorbitol, and 1,2-propanediol as hydrogen bond donors (HBD). Conventional solvents used in the comparison included water, menthol, and ethanol. The flavonoids studied—catechin, daidzein, quercetin, naringenin, luteolin, genistein, kaempferol, apigenin, diosmetin, and chrysin—were analyzed for solubility and extractability via both solvents. Flavonoid solubility was observed to vary based on both the structural characteristics of the flavonoid and the choice of HBD. Notably, catechin demonstrated the highest solubility across all solvent systems, followed by naringenin and quercetin. Among the NADES systems, the blend based on 1,2-propanediol notably enhanced the solubility of quercetin and luteolin to 53.4 mg/mL and 26.9 mg/mL, respectively, surpassing their solubility in conventional solvents. The empirical findings were consistent with theoretical predictions made using the COSMO-RS method, confirming the efficacy of NADES in solubilizing food flavonoids more effectively than conventional solvents. [ABSTRACT FROM AUTHOR]

[Display omitted] • Functional groups significantly impact quinoline adsorption strength on Fe (110) surfaces. • QN1 and QN4 exhibit strong physical interactions, while QN2 and QN3 coordinate with Fe atoms. • PDOS analysis shows charge redistribution enhances the stability of quinoline coordination. • COSMO-RS highlights the role of hydrogen bonds in quinoline-water molecule interactions. Quinoline derivatives have been the subject of extensive research due to their excellent electronic properties and wide range of applications. This study conducts a comprehensive computational examination of the adsorption properties of substituted quinoline derivatives on Fe(110) surfaces. Four specific compounds, namely 2-amino-7-hydroxy-4-phenyl-1,4-dihydroquinoline-3-carbonitrile (QN1), 2-amino-7-hydroxy-4-(p-tolyl)-1,4-dihydroquinoline-3-carbonitrile (QN2), 2-amino-7-hydroxy-4-(4-methoxyphenyl)-1,4-dihydroquinoline-3-carbonitrile (QN3), and 2-amino-4-(4-(dimethylamino)phenyl)-7-hydroxy-1,4-dihydroquinoline-3-carbonitrile (QN4) were investigated using first-principles density functional theory (DFT) calculations along with COSMO-RS analysis for solvation properties. Our results revealed that the presence of functional groups significantly influence the adsorption strength on Fe(110) surfaces. Quinoline molecules have adsorbed on the iron surface through complex mechanisms involving physical interactions and charge transfer. Specifically, QN1 and QN4 showed strong physical interactions with iron atoms while QN2 and QN3 exhibited high affinity to coordinate with Fe atoms. The stability of coordinated quinolines was enhanced by a notable charge redistribution and bond formation as observed via projected density of states (PDOS). On the other hand, electron density difference (EDD) and electron localization function (ELF) iso -surfaces highlighted the critical role of van der Waals interactions, predominantly influenced by nitrogen atoms, in stabilizing the adsorbed molecules. The COSMO-RS analysis elucidated the solvation characteristics, emphasizing the importance of hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA) in the interaction of quinolines with water molecules. Overall, this study provides crucial insights into the molecular mechanisms underlying the corrosion inhibition properties of quinoline derivatives, emphasizing the influence of functional groups and solvation effects on adsorption behavior and stability. [ABSTRACT FROM AUTHOR]

[Display omitted] • Tris-IDA exhibited the strongest interactions with the iron surface. • Significant hybridization occurred between inhibitor s, p orbitals and Fe 3d orbitals. • Protonation reduced diffusion coefficients due to enhanced electrostatic interactions. • Protonated state formed denser inhibitor films, lowering corrosive particle diffusion. Theoretical investigations into the adsorption of molecules on metal surfaces play a pivotal role in the development of more efficient corrosion inhibitors. This study offers a novel approach by combining Density Functional Tight Binding (DFTB), Molecular Dynamics (MD) simulations, and conductor-like screening model for realistic solvation (COSMO-RS) calculations to comprehensively assess the interaction and diffusion properties of glycine (Gly), 2,2′-azanediyldiacetic acid (IDA), and 5-aminopentanoic acid (5-APA) with two 1,3,5-triazine (Tris) derivatives (Tris-Gly and Tris-IDA) for corrosion protection of carbon steel in acidic medium. The solvation properties of these molecules were evaluated alongside their interaction strength with the Fe(1 1 0) surface, considering both neutral and protonated forms. DFTB simulations revealed a clear trend in interaction energies, following the order: Tris-IDA > Tris-Gly > 5-APA > IDA > Gly. It ranges from −1.191 eV to −1.853 eV, with the highest stability observed for protonated Tris-IDA (−1.853 eV), while protonated Glycine exhibits the lowest interaction energy (−1.191 eV). These theoretical results diverged slightly from experimental observations, with Tris-Gly outperforming Tris-IDA, which was attributed to steric effects and solvent interactions. The DFTB results also indicated strong covalent interactions, particularly involving acetic groups, between the inhibitors' orbitals and the iron's 3d orbitals. MD simulations further demonstrated the impact of protonation on inhibitor mobility, with reduced diffusion coefficients due to enhanced electrostatic interactions and hydrogen bonding. COSMO-RS solvation analysis confirmed the strong hydrogen bonding capabilities of these molecules with water. Overall, this study elucidates the mechanisms of corrosion inhibition by integrating multiple simulation techniques, providing key molecular insights that can guide the design of more effective corrosion inhibitors. The findings emphasize the significance of electronic interactions and molecular structure in enhancing inhibitor performance, making this a valuable contribution to corrosion science. [ABSTRACT FROM AUTHOR]

[Display omitted] • Combination of COSMO-RS theory and machine learning to a mechanism-data hybrid-driven model from small training sample. • Accurate prediction of acetylene solubility by the model with fewer parameters. • Better generalization to predict acetylene solubility in solvents beyond training sample. • Data-driven discovery of a charged fragment interaction energy form different from classical COSMO-RS. In this work, a mechanism-data hybrid-driven machine learning model is built to predict acetylene solubility from COSMO-derived molecular descriptors, overcoming the challenge of limited training samples. We have successfully enhanced the data-learning capabilities of the model by mechanism-level modelling, and generated new mechanism-level understanding from the results of data learning. On the basis of the mechanism-level understanding of molecular interactions, this model gives more accurate predictions than the baseline models, even though it contains only 37 model parameters. Meanwhile, it presents better generalization to predict solubility in solvents beyond the training sample. Furthermore, by analyzing the implication of the parameters of this model, we find a charged segment interaction energy form different from that described by the classic COSMO-RS theory. This work not only produces available acetylene solubility data, but also presents a proposed framework to build a mechanism-data hybrid-driven model for thermodynamic properties and discovering interpretable chemical laws from small training samples. [ABSTRACT FROM AUTHOR]

[Display omitted] • Accurate and rapid screening of solvents for separation of Phe from 9-Fluo. • Optimum liquid–solid ratios were determined by solubility calculation method. • Hydrogen bonding and van der Waals interactions are keys to separation. Phenanthrene (Phe) and fluorene (Flu) are crucial chemical intermediates with applications in materials, medicine, and pesticides. However, conventional separation methods struggle to effectively isolate phenanthrene from fluorene due to their similar physical properties. To address this challenge, a reaction–separation coupling technique was employed to convert fluorene into 9-fluorenone (9-Fluo), followed by the separation of phenanthrene from 9-fluorenone using solvent-cooling crystallization. Initially, the relative solubilities of phenanthrene and 9-fluorenone in 2072 solvents were calculated using COSMO-RS for preliminary screening. Among various conventional short-chain aliphatic alcohols and low-carbon saturated hydrocarbons, methanol, and cyclohexane were identified as the most effective solvents based on their solubility selectivity (SS). The molecular interactions between phenanthrene or 9-fluorenone and these solvents were analyzed using theoretical methods such as σ-potential/profile, infinite dilution activity coefficients in COSMO-RS, and Hansen solubility parameters, with further confirmation from FTIR tests. The optimal liquid–solid ratios were determined using both phase diagram measurements and solubility calculations. The reaction conversion of fluorene to 9-fluorenone was complete regardless of the raw material used. Phenanthrene and 9-fluorenone were effectively separated using methanol and cyclohexane with optimum liquid–solid mass ratios of 1.6:1 and 2.8:1, respectively. In the model mixture, the contents of phenanthrene and 9-fluorenone exceeded 98 wt%, with yields above 91 wt%. For anthracene residue, the content of 9-fluorenone reached 93.98 wt% with a yield of 84.52 wt%, while its content was only 76.58 wt% due to the accumulation of other components in phenanthrene. [ABSTRACT FROM AUTHOR]

[Display omitted] • Tris-IDA exhibited the strongest interactions with the iron surface. • Significant hybridization occurred between inhibitor s, p orbitals and Fe 3d orbitals. • Protonation reduced diffusion coefficients due to enhanced electrostatic interactions. • Protonated state formed denser inhibitor films, lowering corrosive particle diffusion. Theoretical investigations into the adsorption of molecules on metal surfaces play a pivotal role in the development of more efficient corrosion inhibitors. This study offers a novel approach by combining Density Functional Tight Binding (DFTB), Molecular Dynamics (MD) simulations, and conductor-like screening model for realistic solvation (COSMO-RS) calculations to comprehensively assess the interaction and diffusion properties of glycine (Gly), 2,2′-azanediyldiacetic acid (IDA), and 5-aminopentanoic acid (5-APA) with two 1,3,5-triazine (Tris) derivatives (Tris-Gly and Tris-IDA) for corrosion protection of carbon steel in acidic medium. The solvation properties of these molecules were evaluated alongside their interaction strength with the Fe(1 1 0) surface, considering both neutral and protonated forms. DFTB simulations revealed a clear trend in interaction energies, following the order: Tris-IDA > Tris-Gly > 5-APA > IDA > Gly. It ranges from −1.191 eV to −1.853 eV, with the highest stability observed for protonated Tris-IDA (−1.853 eV), while protonated Glycine exhibits the lowest interaction energy (−1.191 eV). These theoretical results diverged slightly from experimental observations, with Tris-Gly outperforming Tris-IDA, which was attributed to steric effects and solvent interactions. The DFTB results also indicated strong covalent interactions, particularly involving acetic groups, between the inhibitors' orbitals and the iron's 3d orbitals. MD simulations further demonstrated the impact of protonation on inhibitor mobility, with reduced diffusion coefficients due to enhanced electrostatic interactions and hydrogen bonding. COSMO-RS solvation analysis confirmed the strong hydrogen bonding capabilities of these molecules with water. Overall, this study elucidates the mechanisms of corrosion inhibition by integrating multiple simulation techniques, providing key molecular insights that can guide the design of more effective corrosion inhibitors. The findings emphasize the significance of electronic interactions and molecular structure in enhancing inhibitor performance, making this a valuable contribution to corrosion science. [ABSTRACT FROM AUTHOR]

[Display omitted] • Combination of COSMO-RS theory and machine learning to a mechanism-data hybrid-driven model from small training sample. • Accurate prediction of acetylene solubility by the model with fewer parameters. • Better generalization to predict acetylene solubility in solvents beyond training sample. • Data-driven discovery of a charged fragment interaction energy form different from classical COSMO-RS. In this work, a mechanism-data hybrid-driven machine learning model is built to predict acetylene solubility from COSMO-derived molecular descriptors, overcoming the challenge of limited training samples. We have successfully enhanced the data-learning capabilities of the model by mechanism-level modelling, and generated new mechanism-level understanding from the results of data learning. On the basis of the mechanism-level understanding of molecular interactions, this model gives more accurate predictions than the baseline models, even though it contains only 37 model parameters. Meanwhile, it presents better generalization to predict solubility in solvents beyond the training sample. Furthermore, by analyzing the implication of the parameters of this model, we find a charged segment interaction energy form different from that described by the classic COSMO-RS theory. This work not only produces available acetylene solubility data, but also presents a proposed framework to build a mechanism-data hybrid-driven model for thermodynamic properties and discovering interpretable chemical laws from small training samples. [ABSTRACT FROM AUTHOR]

[Display omitted] • Accurate and rapid screening of solvents for separation of Phe from 9-Fluo. • Optimum liquid–solid ratios were determined by solubility calculation method. • Hydrogen bonding and van der Waals interactions are keys to separation. Phenanthrene (Phe) and fluorene (Flu) are crucial chemical intermediates with applications in materials, medicine, and pesticides. However, conventional separation methods struggle to effectively isolate phenanthrene from fluorene due to their similar physical properties. To address this challenge, a reaction–separation coupling technique was employed to convert fluorene into 9-fluorenone (9-Fluo), followed by the separation of phenanthrene from 9-fluorenone using solvent-cooling crystallization. Initially, the relative solubilities of phenanthrene and 9-fluorenone in 2072 solvents were calculated using COSMO-RS for preliminary screening. Among various conventional short-chain aliphatic alcohols and low-carbon saturated hydrocarbons, methanol, and cyclohexane were identified as the most effective solvents based on their solubility selectivity (SS). The molecular interactions between phenanthrene or 9-fluorenone and these solvents were analyzed using theoretical methods such as σ-potential/profile, infinite dilution activity coefficients in COSMO-RS, and Hansen solubility parameters, with further confirmation from FTIR tests. The optimal liquid–solid ratios were determined using both phase diagram measurements and solubility calculations. The reaction conversion of fluorene to 9-fluorenone was complete regardless of the raw material used. Phenanthrene and 9-fluorenone were effectively separated using methanol and cyclohexane with optimum liquid–solid mass ratios of 1.6:1 and 2.8:1, respectively. In the model mixture, the contents of phenanthrene and 9-fluorenone exceeded 98 wt%, with yields above 91 wt%. For anthracene residue, the content of 9-fluorenone reached 93.98 wt% with a yield of 84.52 wt%, while its content was only 76.58 wt% due to the accumulation of other components in phenanthrene. [ABSTRACT FROM AUTHOR]

The effective separation of ethyl acetate (EAC)/isopropanol(IPA)/water azeotrope from industrial wastewater significantly reduces environmental risks and recycling resources. An azeotrope separation technique was proposed, combining ionic liquid mixed entrainer and dividing wall column extractive distillation (DWC-ED). The selectivity and solubility of 225 ionic liquids were calculated based on the COSMO-RS model. The molecular polarity of EAC, IPA, H 2 O, dimethyl sulfoxide (DMSO), and [EMIM][AC] was studied, and 1-ethyl-3-methylimidazolium acetate ([EMIM][AC]) was determined as the best solvent. In addition, the interaction mechanism between [EMIM][AC] (or DMSO) and EAC/IPA/H 2 O was studied using the quantum chemistry (QC) theory. Independent gradient model (IGM), interaction energy, and atoms in molecules (AIM) analysis showed that hydrogen bonds and vdW forces were formed between [AC]− anion and H 2 O/IPA/EAC, and the interaction energy was gradually weakened, which significantly improved the performance of extractive distillation process. The results showed that the total annual cost (TAC) and energy consumption of IL-based mixed solvent extractive distillation were reduced by 36.04 % and 37.72 %, respectively, compared to the single DMSO extractive distillation process. The TAC and energy consumption of the DWC-ED process were 44.61 % and 51.43 % lower than the benchmark processes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Solid-liquid phase diagrams for two osmolyte systems made using DSC and IR data. • No eutectic temperature drop observed, proving systems aren't classical DESs. • Sarcosine-urea eutectic solution didn't crystallize due to strong H-bonding. The concept of deep eutectic solvents (DESs) has been recently applied to osmolytes (kosmotropes or chaotropes), small organic molecules responsible for stabilization of proteins and other biomolecules in various biological systems. In this study the combinations of the kosmotrope sarcosine (Sar) with chaotrope urea (U) or guanidine hydrochloride (G) across various molar ratios was studied experimentally by the differential scanning calorimetry and infrared spectroscopy. Theoretical prediction of thermodynamic interaction terms based on screening of the charge density was used to support interpretation of experimental findings. Phase diagrams were constructed for both Sar-U and Sar-G systems and eutectic behaviour was identified. Neither of the two systems showed depression of the eutectic temperature compared to its ideal solution values, and therefore neither of the systems could be classified as a deep eutectic solvent in the classical sense. The Sar-G system did show at least partial crystallization of its eutectic solution upon cooling. However, the eutectic solution of the Sar-U system remained amorphous at decreased temperatures, and the phenomenon was attributed to be kinetic in nature and originating from strong intermolecular hydrogen bonding interactions of the components. Thus, it was speculated that the role of osmolytes in stabilizing cellular biomolecules might not come from the thermodynamics only. [ABSTRACT FROM AUTHOR]

[Display omitted] Stimuli-responsive lyogels are considered to be smart materials due to their capability of undergoing significant macroscopic changes in response to external triggers. Due to their versatile and unique properties, smart lyogels exhibit great potential in various applications such as drug delivery or actuation processes. While Poly-N-isopropylacrylamide (pNIPAM) is widely known as a thermo-responsive material, it also shows significant solvent-responsive swelling behavior. For the systems tested, polar solvents induce strong swelling due to hydrogen bonding with the amide group, nonpolar solvents lead to significant shrinkage of the lyogels. The aim of this study is to investigate and to model this behavior for future application in chemical or biochemical reactors. As a current area of research, incorporating smart lyogel technology into (bio-)chemical reactors facilitates the development of smart reactor systems. Applying thermodynamic modelling with the gE model COSMO-RS on the monomer or oligomers of pNIPAM, the correlation between solvent-polymer interactions and the degree of swelling can be observed. pNIPAM derivatives exhibit low infinite dilution activity coefficients (IDACs) in polar solvents with large degrees of swelling, while displaying an increase of IDACs in nonpolar solvents. Hydrogen bonds dominate the swelling behavior of lyogels not only in pure solvents but also in mixtures of solvents with varying polarity. Even in mixtures containing high amounts of nonpolar solvents, large degrees of swelling were observed due to the uptake of the polar solvent in the lyogel matrix. This effect can be observed in binary solvent mixtures but also in representative mixtures along an esterification reaction with varying carboxylic chain length of alcohol and carboxylic acids. [ABSTRACT FROM AUTHOR]

Estimation of interfacial tension (IFT) and % reduction in IFT of Brazil and China regions' heavy crude oil is employed by Artificial Intelligence (AI) led frameworks. The uniqueness of proposed approach lies in using AI frameworks that are constructed using Artificial Neural Network (ANN) and meta-heuristic algorithms i.e. Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) forms three AI frameworks namely ANN, ANN-GA, and ANN-GWO is employed for IFT estimation of heavy oil and deep eutectic solvent-brine interface. The hyper-parameters of the frameworks are trained, validated, and tested over 18228 IFT data points obtained from the Omani heavy crude oil. The intermolecular interactions between oil, brine, and DES are obtained by generating surface segments in COnductor like Screening MOdel for Real Solvent (COSMO-RS) framework. The composition of brine, mole ratio of oil, DES, and temperature are other input parameters for AI model. The IFT estimation leads to the selection of 3 hydrophilic DESs capable of greater than 75 % IFT reduction and 14 hydrophilic DESs capable of 50–75 % IFT reduction. ANN-GWO scores best AI framework among 3 whereas ANN-GA fails to estimate IFT reduction greater than 50 %. Analysis of IFT with respect to mole fraction reveals the most suitable range of DES addition for IFT reduction. [Display omitted] • AI frameworks are used in estimating interfacial tension between heavy crude oil and brine mixed with deep eutectic solvent. • Reduction of interfacial tension facilitates as key descriptor for deep eutectic solvent selection. • Three hydrophilic DESs have more than 75 % IFT reduction capability. • AI frameworks consider structural properties, concentration, variation for model development. [ABSTRACT FROM AUTHOR]