Design of Nonideal Eutectic Mixtures Based on Correlations with Molecular Properties

Abstract

In this work, a statistical analysis was performed to reveal how the molecular properties are correlated with the nonideal behavior observed in eutectic mixtures. From this, a statistical model, combined with theory and experimental results, was developed to predict the nonideal behavior of a specific set of eutectic mixtures, consisting of quaternary ammonium bromides with dicarboxylic acids and polyols. The combination of this analysis and this model can be considered as a first step toward the a priori design of eutectic mixtures. The analysis performed is based on principal components. The descriptors used for this are molecular properties of the constituents of these mixtures. The molecular properties are a combination of experimental, theoretical, and computed properties. The analysis reveals that there are strong correlations between the nonideality of the mixtures and a measure of the acidity of the hydrogen bond donating protons, the displacement of the bromide anion, and the bulkiness of the quaternary ammonium salt. Our analysis highlights the design rules of deep eutectic systems (DES), enabling control over the extent of the liquid window. Our model enables prediction of the eutectic temperature for a range of related mixtures.

Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives

“…In the study by Xu et al, 42 key factors of DES pre-treatment of lignocellulosic biomass procedure were handled by principal component analysis (PCA) and partial least squares analysis methods to raise the possible efficiency of this industrial procedure [ 199 ]. Another case where PCA and regression analysis were used synergistically is the work of Kollau et al [ 200 ]. In this study, the authors used a combination of experimental, theoretical, and computed properties as input for their linear and non-linear models to predict the non-ideality of the DES mixtures and, thus, the eutectic temperatures.…”

“… Artificial neural network and genetic algorithm approach The feed-forward backpropagation neural network algorithm with 4 input layer neurons and 2 output layer neurons for 4 independent and 2 dependent variables. The optimum number of hidden layer neurons was 11 Experiment design for microwave-assisted extraction of phytochemical compounds from black jamun pulp [ 196 ] 12 DESs based on allyltriphenylphosphonium bromide: Triethylene glycol with molar ratios of 1:4, 1:10 and 1:16 Linear and quadratic regression models Estimation of carbon dioxide solubility in DESs [ 198 ] 13 DESs based on ChCl: Glycerol ChCl: P-coumaric acid Principal component analysis Partial least squares Furthermore, based on molecular simulation, the detailed relationships between key variables were further analyzed Revealing the biomass pretreatment mechanism by evaluating the inner relationships among 42 key process factors [ 199 ] 14 DESs based on tetraalkylammonium bromide Principal component analysis Regression analysis Prediction of DES eutectic temperatures [ 200 ] …”

Design and thermophysical characterization of betaine hydrochloride-based deep eutectic solvents as a new platform for CO2 capture

“…It is also previously reported that the incorporation second component as HBD to the DES decreases T f where hydrogen bonding should become paramount with both components of DES (HBA and HBD) rather than a single component. 33,34 Therefore, the molar ratio 1 : 4 : 2.5 combination is selected further for all experiments and CO 2 capture applications. The details regarding the prepared DESs are presented in Scheme 1.…”

Computer Simulations of Deep Eutectic Solvents. Challenges, Solutions, and Perspectives

“…In the study by Xu et al, 42 key factors of DES pretreatment of lignocellulosic biomass procedure were handled by principal component analysis (PCA) and partial least squares analysis methods to raise the possible efficiency of this industrial procedure [181]. Another case where PCA and regression analysis were used synergistically is the work of Kollau et al [182]. In this study, the authors used a combination of experimental, theoretical, and computed properties as input for their linear and non-linear models to predict the nonideality of the DES mixtures and thus the eutectic temperatures.…”