Density Of Deep Eutectic Solvents Research Articles

Thermal conductivity, viscosity, and density of deep eutectic solvents containing choline chloride and triethylene glycol

Thermal conductivity, viscosity, and density of deep eutectic solvents containing choline chloride and triethylene glycol

Machine learning coupled with group contribution for predicting the density of deep eutectic solvents

Deep Eutectic Solvents (DESs) are a recently introduced class of green solvents with unique and favorable characteristics. Despite their recent debut, the scientific community has begun to place greater emphasis on them as alternatives to ionic liquids (ILs). Knowledge of the various physical properties of DESs is essential for various applications in the chemical industries and related fields. In this study, a comprehensive database including 1410 density data points, from 166 different DESs at various temperatures and atmospheric pressure, were retrieved from open literature to develop models to increase the accuracy of density predictions. The densities of DESs were used to develop two commonly used machine learning models, namely Multilayer Perceptron Artificial Neural Network (MLPANN) and Least Square Support Vector Machine (LSSVM), in conjunction with the group contribution (GC) method. Based on the GC method, each fragment of a compound contributes a specific amount to the physical property's value. By considering this, the prediction ability was improved by applying the GC method in the model development procedure. Both models predict the DES densities by taking into account the effect of 35 functional groups, the temperature, and the HBA/HBD molar ratios. The optimum MLPANN model structure consists of a single hidden layer with five neurons and a logarithmic sigmoid transfer function. By employing this MLPANN-GC model, the values of the squared correlation coefficient, R2, and absolute average relative deviation percent, AARD%, were 0.99 and 0.61%, respectively, while for the LSSVM-GC model (with the radial basis function (RBF) kernel), they were 0.99 and 0.56%, respectively. Also, K-fold cross-validation was used to assess the performance of the LSSVM-GC model. The presented machine learning models in this study were found to perform more accurately than those obtained using the best current correlations and GC models for DES densities in the open literature. The more accurate results, in addition to the enhanced predictability behavior of the developed models, give these models a preference for use in industrial and academic applications.

Estimating the density of deep eutectic solvents applying supervised machine learning techniques

Deep eutectic solvents (DES) are recently synthesized to cover limitations of conventional solvents. These green solvents have wide ranges of potential usages in real-life applications. Precise measuring or accurate estimating thermophysical properties of DESs is a prerequisite for their successful applications. Density is likely the most crucial affecting characteristic on the solvation ability of DESs. This study utilizes seven machine learning techniques to estimate the density of 149 deep eutectic solvents. The density is anticipated as a function of temperature, critical pressure and temperature, and acentric factor. The LSSVR (least-squares support vector regression) presents the highest accuracy among 1530 constructed intelligent estimators. The LSSVR predicts 1239 densities with the mean absolute percentage error (MAPE) of 0.26% and R2 = 0.99798. Comparing the LSSVR and four empirical correlations revealed that the earlier possesses the highest accuracy level. The prediction accuracy of the LSSVR (i.e., MAPE = 0. 26%) is 74.5% better than the best-obtained results by the empirical correlations (i.e., MAPE = 1.02%).

Chemical structure-based models for prediction of density of ammonium and phosphonium-based deep eutectic solvents

In this work, linear and nonlinear quantitative structure–property relationship (QSPR) models are proposed to predict the density of deep eutectic solvents (DESs). A large experimental database of 2005 density data belonging to chloride and bromide-based DESs with ammonium and phosphonium-based cations and various hydrogen bond donors (HBDs) was collected from literatures. Using a modified particle swarm optimization (MPSO) variable-selection approach, subsets of relevant descriptors were selected to relate the density of DESs to their corresponding cation and HBD structures. To develop the nonlinear models, an adaptive neuro-fuzzy inference system (ANFIS) method was used with the same inputs and outputs of linear models. The squared correlation coefficient (R2) of linear models for chloride and bromide-based DESs are 0.897 and 0.960, while the density of DESs with anion part of chloride and bromide is predicted by ANFIS with the R2 of 0.956 and 0.980, respectively. The internal and external validations of the proposed models indicate that these models can be used for prediction of DESs densities with high accuracy and reliability.

Simple and global correlation for the densities of deep eutectic solvents

Deep eutectic solvents (DESs) mostly comply with the principles of green chemistry. Therefore, they are being widely investigated due to their great potential for a variety of applications in the industries. However, it is necessary to have accurate (thermo)-physical property information on any solvent before it can pave its way into the industries. Among such properties, density as a function of temperature is one of the most important. In this study, a simple, accurate and global correlation is proposed to estimate the densities of a large number of DESs of different natures. The proposed model is a function of the critical temperature, critical volume and acentric factor of the DES, as well as the temperature of the system. In this way, the model is capable of accurately predicting the densities of DESs without the need for any reference temperature density data, in contrast to most of the literature models that do require a reference data point. The average relative error for 149 DESs of different natures was estimated to be 3.12% by the proposed model, indicating its accuracy compared to previous models.

Prediction of refractive index and density of deep eutectic solvents using atomic contributions

Deep eutectic solvents (DESs) are considered as potential alternatives for ionic liquids (ILs). The evaluation of DESs as new generation of solvents for various practical application requires enough knowledge about some main physical, chemical, and thermodynamic properties. In this study, due to lack of data for DESs’ refractive indices, the refractive indices of twenty four DESs based on ammonium and phosphonium salts were measured and predicted using atomic contribution method. The atomic contributions data for molar refraction proposed by Wildman and Crippen which was developed for neutral compounds were employed to calculate the molar refractions of DESs. Subsequently, the refractive indices of DESs were predicted using Lorentz–Lorenz equation through the calculated DESs’ molar refractions and experimental DESs’ densities values. The absolute relative percentage error (ARPE) value of 0.56% and a regression coefficient (R2) value of 0.9822 both confirmed the highly possible application of the proposed method for predicting the refractive indices. In addition, the effect of DESs’ composition on refractive index of DESs was investigated and it was found that the refractive index of the DESs lies between that of the salt and HBD. Moreover, the densities of DESs were also predicted using Lorentz–Lorenz equation employing the calculated molar refraction and values of experimental DESs’ refractive indices. The ARPE of 1.43% shows that this method for prediction of densities of the synthesized DESs is applicable as well.