Statistical mechanics and artificial intelligence to model the thermodynamic properties of pure and mixture of ionic liquids

In this paper, the volumetric properties of pure and mixture of ionic liquids are predicted using the developed statistical mechanical equation of state in different temperatures, pressures and mole fractions. The temperature dependent parameters of the equation of state have been calculated using corresponding state correlation based on only the density at 298.15 K as scaling constants. The obtained mean of deviations of modified equation of state for density of all pure ionic liquids for 1662 data points was 0.25%. In addition, the performance of the artificial neural network(ANN) with principle component analysis(PCA) based on back propagation training with28 neurons in hidden layer for predicting of behavior of binary mixtures of ionic liquids was investigated. The AADs of a collection of 568 data points for all binary systems using the EOS and the ANN at various temperatures and mole fractions are 1.03% and 0.68%, respectively. Moreover, the excess molar volume of all binary mixtures is predicted using obtained densities of EOS and ANN, and the results show that these properties have good agreement with literature.

Molar volume of eutectic solvents as a function of molar composition and temperature

The conventional Rackett model for predicting liquid molar volume has been modified to cater for the effect of molar composition of the Deep Eutectic Solvents(DES). The experimental molar volume data for a group of commonly used DES has been used for optimizing the improved model. The data involved different molar compositions of each DES. The validation of the new model was performed on another set of DESs. The average relative deviation of the model on the training and validation datasets was approximately 0.1% while the Rackett model gave a relative deviation of more than 1.6%. The modified model deals with variations in DES molar composition and temperature in a more consistent way than the original Rackett model which exhibits monotonic performance degradation as temperature moves away from reference conditions. Having the composition of the DES as a model variable enhances the practical utilization of the predicting model in diverse design and process simulation applications.

Novel manganese(Ⅱ)-based deep eutectic solvents: Synthesis and physical properties analysis

Type IV deep eutectic solvent（DES） involves the formation of metal-based eutectics from metal salts or metal salt hydrate in combination with various hydrogen-bond donors（HBDs） such as urea, ethylene glycol or acetamide. In current study, two distinguished approaches were used to synthesize potential DESs, given as the direct heating and the evaporating methods. Successful synthesized DESs were subjected for physical properties characterization by Fourier Transform Infrared（FTIR） Spectroscopy, thermal stability, viscosity,and conductivity analyses. Five novel manganese（II）-based DESs were successfully synthesized as reported in this study. Data obtained indicated that the Mn Cl2á4H2Oáacetamide DES exhibits the lowest freezing point（27.5℃）, highest thermal stability（193℃ point of dehydration）, lowest viscosity（E = 112.8cP） and the highest conductivity（0.12723 mS/cm）. The findings obtained reveal the characteristics, nature or features of synthesized DESs as potential industrial solvents.