Molecular Guide for Selecting Green Deep Eutectic Solvents with High Monosaccharide Solubility for Food Applications.

Monosaccharides play a vital role in the human diet due to their interesting biological activity and functional properties. Conventionally, sugars are extracted using volatile organic solvents (VOCs). Deep eutectic solvents (DESs) have recently emerged as a new green alternative to VOCs. Nonetheless, the selection criterion of an appropriate DES for a specific application is a very difficult task due to the designer nature of these solvents and the theoretically infinite number of combinations of their constituents and compositions. This paper presents a framework for screening a large number of DES constituents for monosaccharide extraction application using COSMO-RS. The framework employs the activity coefficients at infinite dilution (γi∞) as a measure of glucose and fructose solubility. Moreover, the toxicity analysis of the constituents is considered to ensure that selected constituents are safe to work with. Finally, the obtained viscosity predictions were used to select DESs that are not transport-limited. To provide more insights into which functional groups are responsible for more effective monosaccharide extraction, a structure-solubility analysis was carried out. Based on an analysis of 212 DES constituents, the top-performing hydrogen bond acceptors were found to be carnitine, betaine, and choline chloride, while the top-performing hydrogen bond donors were oxalic acid, ethanolamine, and citric acid. A research initiative was presented in this paper to develop robust computational frameworks for selecting optimal DESs for a given application to develop an effective DES design strategy that can aid in the development of novel processes using DESs.

Natural deep eutectic solvents for Ultrasonic-Assisted extraction of nutritious date Sugar: Molecular Screening, Experimental, and prediction.

The aim of this study is to develop an environmentally friendly and effective method for the extraction of nutritious date sugar using natural deep eutectic solvents (NADES) and ultrasound-assisted extraction (USAE). The careful design of a suitable NADES-USAE system was systematically supported by COSMO-RS screening, response surface method (RSM) and artificial neural network (ANN). Initially, 26 natural hydrogen bond donors (HBDs) were carefully screened for sugar affinity using COSMO-RS. The best performing HBDs were then used for the synthesis of 5 NADES using choline chloride (ChCl) as HBA. Among the synthesized NADES, the mixture of ChCl, citric acid (CA) and water (1:1:1 with 20 wt% water) resulted in the highest sugar yield of 78.30 ± 3.91 g/100 g, which is superior to conventional solvents such as water (29.92 ± 1.50 g/100 g). Further enhancements using RSM and ANN led to an even higher sugar recovery of 87.81 ± 2.61 g/100 g, at conditions of 30 °C, 45 min, and a solvent to DFP ratio of 40 mL/g. The method NADES-USAE was then compared with conventional hot water extraction (CHWE) (61.36 ± 3.06) and showed 43.1% higher sugar yield. The developed process not only improves the recovery of the nutritious date sugar but also preserves the heat-sensitive bioactive compounds in dates, making it an attractive alternative to CHWE for industrial utilization. Overall, this study shows a promising approach for the extraction of nutritive sugars from dates using environmentally friendly solvents and advanced technology. It also highlights the potential of this approach for valorizing underutilized fruits and preserving their bioactive compounds.

Critical Properties of Ternary Deep Eutectic Solvents Using Group Contribution with Extended Lee-Kesler Mixing Rules.

One of the most commonly used molecular inputs for ionic liquids and deep eutectic solvents (DESs) in the literature are the critical properties and acentric factors, which can be easily determined using the modified Lydersen-Joback-Reid (LJR) method with Lee-Kesler mixing rules. However, the method used in the literature is generally applicable only to binary mixtures of DESs. Nevertheless, ternary DESs are considered to be more interesting and may provide further tailorability for developing task-specific DESs for particular applications. Therefore, in this work, a new framework for estimating the critical properties and the acentric factor of ternary DESs based on their molecular structures is presented by adjusting the framework reported in the literature with an extended version of the Lee-Kesler mixing rules. The presented framework was applied to a data set consisting of 87 ternary DESs with 334 distinct compositions. For validation, the estimated critical properties and acentric factors were used to predict the densities of the ternary DESs. The results showed excellent agreement between the experimental and calculated data, with an average absolute relative deviation (AARD) of 5.203% for ternary DESs and 5.712% for 260 binary DESs (573 compositions). The developed methodology was incorporated into a user-friendly Excel worksheet for computing the critical properties and acentric factors of any ternary or binary DES, which is provided in the Supporting Information. This work promotes the creation of robust, accessible, and user-friendly models capable of predicting the properties of new ternary DESs based on critical properties, thus saving time and resources.

The curious case of polyphenols as green corrosion inhibitors: a review on their extraction, design, and applications.

Over the past century, a substantial amount of research focused on developing corrosion inhibitors, with a special focus on green "plant-based" corrosion inhibitors. Among the various types of inhibitors, polyphenols emerged as a promising candidate due to their advantageous characteristics, which include being inexpensive, biodegradable, renewable, and, most importantly, safe for both the environment and humans. Their performance as sustainable corrosion inhibitors have encouraged many electrochemical experiments as well as theoretical, mechanistic, and computational studies, with many papers reporting inhibition efficiencies of over 85%. In this review, the majority of literature contributions on the inhibition of various types of polyphenols, their natural extraction techniques, and their applications as "greener" corrosion inhibitors for metals are thoroughly described and discussed with a focus on their preparation, inhibition mechanism, and performance. Based on the reviewed literature, it can be concluded that polyphenols have a very promising potential to be used as both green and powerful corrosion inhibitors; therefore, further investigations, experimental or computational, are still required to realize higher inhibition efficiencies reaching up to ≈ 100%.

Solvent Regeneration Methods for Combined Dearomatization, Desulfurization, and Denitrogenation of Fuels Using Deep Eutectic Solvents.

Deep eutectic solvents (DESs) can be used as potential solvents for various applications. However, their recovery depends on both economic and environmental considerations. In this study, the possibilities for the recovery of methyl triphenyl phosphonium bromide/triethylene glycol (MTPPB/TEG 1:4) after the application of combined dearomatization, desulfurization, and denitrogenation of fuels are investigated. The DES was first prepared and characterized for its density, viscosity, and water content. Then, the single-stage liquid-liquid extraction was conducted in addition to testing the repetitive use of the DES. After that, two regeneration methods were studied: the stripping method (with n-heptane) and the washing method (with distilled water or diethyl ether). In addition, a parametric study was conducted to optimize the regeneration methods. The results showed that washing the used DES with distilled water was significantly more effective than stripping the DES with n-heptane. In terms of quinoline reduction, distilled water reduced the quinoline content in the DES from 3.2 to 2.1 wt %, while n-heptane showed a minor reduction in the quinoline content (3.2 to 3 wt %). It was also found that a much more effective recovery could be achieved by (i) increasing the DES-to-regeneration solvent mass ratio and (ii) increasing the number of wash cycles. Furthermore, the regeneration temperature did not have a significant effect on the recyclability of the DES. The results demonstrated that the regenerated DES was as effective in extraction as a fresh batch of DES.

Biosorption of zinc (II) from synthetic wastewater by using Inula Viscosa leaves as a low-cost biosorbent: Experimental and molecular modeling studies.

The use of biosorption as a strategy for lowering the amount of pollution caused by heavy metals is particularly encouraging. In this investigation, a low-cost and efficient biosorbent, Inula Viscosa leaves were used to remove zinc ions (Zn2+) from synthetic wastewater. A Fourier transform infrared spectroscopy experiment, a scanning electron microscopy experiment, and an energy dispersive X-ray spectroscopy experiment were used to describe the support. Several different physicochemical factors, such as the beginning pH value, contact duration, initial zinc concentration, biosorbent dose, and temperature, were investigated in this study. When the Langmuir, Freundlich, Temkin, Toth, and Redlich-Peterson models were used to match the data from the Inula Viscosa leaves biosorption isotherms, it was found that the biosorption isotherms correspond most closely with the Langmuir isotherm. On the other hand, the kinetic biosorption process was investigated using pseudo-first-order, pseudo-second-order (PS2), and Elovich models. The PS2 model was the one that provided the most accurate description of the biosorption kinetics. The thermodynamics process shows the spontaneous and endothermic character of Zn2+ sorption on Inula Viscosa leaves, which also entails the participation of physical interactions. In addition, the atom-in-molecule analysis, density functional theory, and the conductor like screening model for real solvents, were used to investigate the relationship that exists between quantum calculations and experimental outcomes.

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.

Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning.

We present here a novel integrated approach employing machine learning algorithms for predicting thermophysical properties of fluids. The approach allows obtaining molecular parameters to be used in the polar soft-statistical associating fluid theory (SAFT) equation of state using molecular descriptors obtained from the conductor-like screening model for real solvents (COSMO-RS). The procedure is used for modeling 18 refrigerants including hydrofluorocarbons, hydrofluoroolefins, and hydrochlorofluoroolefins. The training dataset included six inputs obtained from COSMO-RS and five outputs from polar soft-SAFT parameters, with the accurate algorithm training ensured by its high statistical accuracy. The predicted molecular parameters were used in polar soft-SAFT for evaluating the thermophysical properties of the refrigerants such as density, vapor pressure, heat capacity, enthalpy of vaporization, and speed of sound. Predictions provided a good level of accuracy (AADs = 1.3-10.5%) compared to experimental data, and within a similar level of accuracy using parameters obtained from standard fitting procedures. Moreover, the predicted parameters provided a comparable level of predictive accuracy to parameters obtained from standard procedure when extended to modeling selected binary mixtures. The proposed approach enables bridging the gap in the data of thermodynamic properties of low global warming potential refrigerants, which hinders their technical evaluation and hence their final application.