Quantitative structure-property relationship (QSPR) for prediction of CO2 Henry’s law constant in some physical solvents with consideration of temperature effects

Chemoinformatics-Driven Design of New Physical Solvents for Selective CO2 Absorption

The removal of CO₂ from gases is an important industrial process in the transition to a low-carbon economy. The use of selective physical (co-)solvents is especially perspective in cases when the amount of CO₂ is large as it enables one to lower the energy requirements for solvent regeneration. However, only a few physical solvents have found industrial application and the design of new ones can pave the way to more efficient gas treatment techniques. Experimental screening of gas solubility is a labor-intensive process, and solubility modeling is a viable strategy to reduce the number of solvents subject to experimental measurements. In this paper, a chemoinformatics-based modeling workflow was applied to build a predictive model for the solubility of CO₂ and four other industrially important gases (CO, CH₄, H₂, and N₂). A dataset containing solubilities of gases in 280 solvents was collected from literature sources and supplemented with the new data for six solvents measured in the present study. A modeling workflow based on the usage of several state-of-the-art machine learning algorithms was applied to establish quantitative structure-solubility relationships. The best models were used to perform virtual screening of the industrially produced chemicals. It enabled the identification of compounds with high predicted CO₂ solubility and selectivity toward other gases. The prediction for one of the compounds, 4-methylmorpholine, was confirmed experimentally.

Prediction of critical properties of sulfur-containing compounds: New QSPR models

In this study, new models have been proposed for the prediction of different critical properties (critical temperature (T_C), critical pressure (P_C), critical volume (V_C), and acentric factor (ω)) of the sulfur-containing compounds based on quantitative structure-property relationship (QSPR). An extensive data set containing experimental data of over 130 different sulfur-containing compounds was employed. Enhanced Replacement Method (ERM) was applied for subset variable selection. Based on ERM selected descriptors, two different models, including linear model and genetic programming (GP) based non-linear model have been proposed for each critical property. The predicted values of each target were in good agreement with the experimental data. For GP-based models, the values of the coefficient of determination (R²) were 0.936, 0.976, 0.990, and 0.917 for T_C, P_C, V_C, and ω, respectively. After revisiting the available QSPR models, it was found that the domain of applicability of new models has been expanded.

Catalytic synthesis of glycol dicarbonate from glycol and dimethyl carbonate by transesterification

Glycol dicarbonate is successfully synthesized via transesterification of glycol with dimethyl carbonate. A catalyst screening is performed by studying the effect of acidity and alkalinity on the catalytic performance. The results indicate that compared with acidic catalysts, high conversions and yields are obtained with basic catalysts. Ca(OH)2, with medium-strength alkalinity, exhibits excellent catalytic activity with glycol dicarbonate yields of 95%-99% when using a fractionation reaction system.