Group contribution and atomic contribution models for the prediction of various physical properties of deep eutectic solvents

Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach

Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES's suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.

Editorial for Special Issue: "Recent Advances in Green Solvents"

Today, environmental conservation is one of the most urgent targets [...].

Deep Eutectic Solvents: Properties and Applications in CO2 Separation

Nowadays, many researchers are focused on finding a solution to the problem of global warming. Carbon dioxide is considered to be responsible for the "greenhouse" effect. The largest global emission of industrial CO₂ comes from fossil fuel combustion, which makes power plants the perfect point source targets for immediate CO₂ emission reductions. A state-of-the-art method for capturing carbon dioxide is chemical absorption using an aqueous solution of alkanolamines, most frequently a 30% wt. solution of monoethanolamine (MEA). Unfortunately, the usage of alkanolamines has a number of drawbacks, such as the corrosive nature of the reaction environment, the loss of the solvent due to its volatility, and a high energy demand at the regeneration step. These problems have driven the search for alternatives to that method, and deep eutectic solvents (DESs) might be a very good substitute. Many types of DESs have thus far been investigated for efficient CO₂ capture, and various hydrogen bond donors and acceptors have been used. Deep eutectic solvents that are capable of absorbing carbon dioxide physically and chemically have been reported. Strategies for further CO₂ absorption improvement, such as the addition of water, other co-solvents, or metal salts, have been proposed. Within this review, the physical properties of DESs are presented, and their effects on CO₂ absorption capacity are discussed in conjunction with the types of HBAs and HBDs and their molar ratios. The practical issues of using DESs for CO₂ separation are also described.

Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks

Studies on deep eutectic solvents (DESs), a new class of "green" solvents, are attracting increasing attention from researchers, as evidenced by the rapidly growing number of publications in the literature. One of the main advantages of DESs is that they are tailor-made solvents, and therefore, the number of potential DESs is extremely large. It is essential to have computational methods capable of predicting the physicochemical properties of DESs, which are needed in many industrial applications and research. Surface tension is one of the most important properties required in many applications. In this work, we report a relatively generalized artificial neural network (ANN) for predicting the surface tension of DESs. The database used can be considered comprehensive because it contains 1571 data points from 133 different DES mixtures in 520 compositions prepared from 18 ions and 63 hydrogen bond donors in a temperature range of 277-425 K. The ANN model uses molecular parameter inputs derived from the conductor-like screening model for real solvents (S _σ-profiles). The training and testing results show that the best performing ANN architecture consisted of two hidden layers with 15 neurons each (9-15-15-1). The proposed ANN was excellent in predicting the surface tension of DESs, as R ² values of 0.986 and 0.977 were obtained for training and testing, respectively, with an overall average absolute relative deviation of 2.20%. The proposed models represent an initiative to promote the development of robust models capable of predicting the properties of DESs based only on molecular parameters, leading to savings in investigation time and resources.

Viscosity Investigations on the Binary Systems of (1 ChCl:2 Ethylene Glycol) DES and Methanol or Ethanol

In this study, the viscosity behavior of two mixtures of Ethaline (1 ChCl:2 ethylene glycol) with either methanol or ethanol were investigated over the temperature range of 283.15–333.15 K at atmospheric pressure. The measured viscosities of neat Ethaline, methanol, and ethanol showed reliable agreement with the corresponding reported literature values. The mixture viscosities were modeled by an Arrhenius-like model to determine the behavior of viscosity with respect to temperature. The data were also modeled by the four well-known mixture viscosity models of Grunberg–Nissan, Jouyban–Acree, McAllister, and Preferential Solvation. All of the model results were reliable, with the Jouyban–Acree and Preferential Solvation models showing the most accurate agreement with the experimental measurements. The Jones–Dole viscosity model was also investigated for the measured viscosities, and by analyzing the results of this model, strong interactions among Ethaline and the alcohol molecules were proposed for both systems. As a final analysis, viscosity deviations of the investigated systems were calculated to study the deviations of the viscosity behaviors with respect to ideal behavior. Both systems showed negative viscosity deviations at all of the investigated temperatures, with the negative values tending towards zero, and hence more ideal behavior, with increasing temperatures. Moreover, in order to correlate the calculated viscosity deviations, the Redlich–Kister model was successfully used for both systems and at each investigated temperature.

The Role of Hydrogen Bond Donor on the Extraction of Phenolic Compounds from Natural Matrices Using Deep Eutectic Systems

Recently, deep eutectic systems (DESs) as extraction techniques for bioactive compounds have surfaced as a greener alternative to common organic solvents. In order to study the effect of these systems on the extraction of phenolic compounds from different natural sources, a comprehensive review of the state of the art was carried out. In a first approach, the addition of water to these systems and its effect on DES physicochemical properties such as polarity, viscosity, and acidity was investigated. This review studied the effect of the hydrogen bond donor (HBD) on the nature of the extracted phenolics. The effects of the nature of the HBD, namely carbon chain length as well as the number of hydroxyl, methyl, and carbonyl groups, have shown to play a critical role in the extraction of different phenolic compounds. This review highlights the differences between DES systems and systematizes the results published in the literature, so that a more comprehensive evaluation of the systems can be carried out before any experimental trial.