Classification of organic solvents revisited by using the COSMO-RS approach

Comparison of predictions of developmental toxicity for compounds of solvent data set

We have considered a series of 235 compounds technically classified as solvents. Chemically, they belong to different classes. Their potential developmental toxicity was evaluated using two models available on platform VEGA HUB; model CAESAR and the Developmental/ Reproductive Toxicity library (PG) model. Models provide beside the prediction of developmental toxicity additional information on similar compounds from models' training sets. In the report, first, we compare the predictions. Second, the sets of similar compounds have been used to implement the clustering scheme. The Kohonen artificial neural network method has been applied as a clustering method. The clusters obtained have been discussed for both models.

Carvone and its eutectic mixtures as novel, biodegradable, and tunable solvents to extract hydrophobic compounds in substitution for volatile toxic solvents

Finding safe solvents with low viscosities has been in great demand in extraction processes. Herein, R-(-)-carvone, a natural monoterpenoid rich in spearmint, was mixed with naturally occurring fatty acids and terpenes. Most eutectic mixtures presented a wide liquid window in the solid-liquid equilibrium phase diagrams. Carvone mixtures at the ideal eutectic points were characterized for physicochemical properties. Despite varying properties, all the tested solvents were immiscible with water and displayed low viscosity with reasonable biodegradability. Sigma potentials of the mixtures were applied to machine learning algorithms, suggesting carvone mixtures as substitutes for polar protic and dipolar aprotic solvents. Carvone mixtures could be successfully applied to liquid-liquid extraction of a red algae called laver, which is rich in natural hydrophobic and hydrophilic pigments of high value. This study proposes carvone as a new bio-based source of hydrophobic solvents and the eutectic mixtures as biodegradable and tunable solvents to extract hydrophobic compounds.

The green solvent: a critical perspective

Solvents are important in most industrial and domestic applications. The impact of solvent losses and emissions drives efforts to minimise them or to avoid them completely. Since the 1990s, this has become a major focus of green chemistry, giving rise to the idea of the 'green' solvent. This concept has generated a substantial chemical literature and has led to the development of so-called neoteric solvents. A critical overview of published material establishes that few new materials have yet found widespread use as solvents. The search for less-impacting solvents is inefficient if carried out without due regard, even at the research stage, to the particular circumstances under which solvents are to be used on the industrial scale. Wider sustainability questions, particularly the use of non-fossil sources of organic carbon in solvent manufacture, are more important than intrinsic 'greenness'. While solvency is universal, a universal solvent, an alkahest, is an unattainable ideal.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10098-021-02188-8.

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

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.

A Method of Calculating the Kamlet-Abboud-Taft Solvatochromic Parameters Using COSMO-RS

There is demand for safer and bio-based solvents, brought on by legislation and sustainability objectives. The prediction of physical properties is highly desirable to help design new molecules. Here we present an in silico approach to obtain calculated Kamlet-Abboud-Taft solvatochromic parameters using virtual experiments. The tautomerisation equilibrium of methyl acetoacetate and dimedone was calculated in different solvents with COSMO-RS theory and converted into estimates of solvent dipolarity and hydrogen bond accepting ability, respectively. Hydrogen bond donating ability was calculated as a function of the electron deficient surface area on protic solvents. These polarity descriptors correlate with rate constants and equilibria, and so ability of calculated Kamlet-Abboud-Taft solvatochromic parameters to recreate experimental free energy relationships was tested with sixteen case studies taken from the literature. The accuracy of the calculated parameters was also satisfactory for solvent selection, as demonstrated with a 1,4-addition reaction and a multicomponent heterocycle synthesis.

Screening of Suitable Ionic Liquids as Green Solvents for Extraction of Eicosapentaenoic Acid (EPA) from Microalgae Biomass Using COSMO-RS Model

Omega-3 poly unsaturated fatty acids (PUFA) particularly eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), have many health benefits including reducing the risk of cancer and cardiovascular disease. Recently, the use of ionic liquids (ILs) in lipid extraction from microalgae provides the potential to overcome common drawbacks, offers several other benefits. To date, very limited researches are available to focus on extracting microalgae lipid and PUFA in particular by using ILs. The objective of current work is to screen the potential ILs that can be applied in EPA extraction. In this study, fast ILs screening was performed with the help of a conductor like screening model for real solvents (COSMO-RS) and the ILs with higher capacity values for use in extraction of EPA were compared. According to the results, the highest capacity for EPA extraction among 352 screened cation/anion combinations belongs to [TMAm][SO₄]. It is expected to achieve a higher yield of EPA once applying this combination as the solvent in the process of extraction. ILs with small anions were observed to have higher capacities, as well possessing higher charge density compared to larger ones, and therefore, they are more preferable for extraction purposes. Moreover, shorter alkyl chain cations are preferred when using imidazolium-based IL, which agrees with experimental data.

Intelligent Approach to Solvent Substitution: The Identification of a New Class of Levoglucosenone Derivatives

With the increasing restriction and control of hazardous solvents, safer alternatives need to be identified. Here a contemporary approach to solvent selection and substitution is presented that offers a more scientific alternative to the simple "like-for-like" exchange. A new family of levoglucosenonederived compounds is proposed, modeled to determine their solvent properties, synthesized, and tested. These new molecules show promise as replacements for polar aprotic solvents that have chronic toxicity issues, such as dichloromethane, nitrobenzene, and N-methylpyrrolidinone. The success of this approach makes it possible for academia and industry to make calculated, intelligent choices for solvent substitution in the future.

Quantitative structure-activity relationship to predict acute fish toxicity of organic solvents

REACH regulation requires ecotoxicological data to characterize industrial chemicals. To limit in vivo testing, Quantitative Structure-Activity Relationships (QSARs) are advocated to predict toxicity of a molecule. In this context, the topic of this work was to develop a reliable QSAR explaining the experimental acute toxicity of organic solvents for fish trophic level. Toxicity was expressed as log(LC50), the concentration in mmol.L(-1) producing the 50% death of fish. The 141 chemically heterogeneous solvents of the dataset were described by physico-chemical descriptors and quantum theoretical parameters calculated via Density Functional Theory. The best subsets of solvent descriptors for LC50 prediction were chosen both through the Kubinyi function associated with Enhanced Replacement Method and a stepwise forward multiple linear regressions. The 4-parameters selected in the model were the octanol-water partition coefficient, LUMO energy, dielectric constant and surface tension. The predictive power and robustness of the QSAR developed were assessed by internal and external validations. Several techniques for training sets selection were evaluated: a random selection, a LC50-based selection, a balanced selection in terms of toxic and non-toxic solvents, a solvent profile-based selection with a space filling technique and a D-optimality onions-based selection. A comparison with fish LC50 predicted by ECOSAR model validated for neutral organics confirmed the interest of the QSAR developed for the prediction of organic solvent aquatic toxicity regardless of the mechanism of toxic action involved.