Inhibition of Aqueous Chemical Reactions by Strong Liquid Structure Makers: Experimental Demonstration with Amides and Acid-Base Reactions.

The chemical absorption of CO2 and H2S in aqueous tertiary amines is a well-known acid-base reaction. Kinetic and vapor-liquid equilibrium experiments show that the addition of an amide such as HMPA, which is known to be a strong liquid structure maker, significantly inhibits the acid-base reactions. The impact is more pronounced for CO2 than for H2S absorption. Despite the presence of water in the solvent, the absorption becomes almost physical. Due to hydrogen bonding and the hydrophobic effect, each amide molecule is involved in a cluster containing several water molecules, thus rendering the water molecules less available to participate in the reaction and to solvate HS- and HCO3- ions. This effect is absent when ethylene glycol, a weak structure maker, is added, even in large quantities. This study demonstrates the importance of solvent structure in the study of chemical reactions. State-of-the-art molecular dynamics simulations of the water-HMPA system could not reproduce the strongly negative excess volume of the mixture. This illustrates the need for more accurate force fields to simulate the structuring effect and their impact on chemical reactions.

Computational screening methodology identifies effective solvents for CO2 capture.

Carbon capture and storage technologies are projected to increasingly contribute to cleaner energy transitions by significantly reducing CO2 emissions from fossil fuel-driven power and industrial plants. The industry standard technology for CO2 capture is chemical absorption with aqueous alkanolamines, which are often being mixed with an activator, piperazine, to increase the overall CO2 absorption rate. Inefficiency of the process due to the parasitic energy required for thermal regeneration of the solvent drives the search for new tertiary amines with better kinetics. Improving the efficiency of experimental screening using computational tools is challenging due to the complex nature of chemical absorption. We have developed a novel computational approach that combines kinetic experiments, molecular simulations and machine learning for the in silico screening of hundreds of prospective candidates and identify a class of tertiary amines that absorbs CO2 faster than a typical commercial solvent when mixed with piperazine, which was confirmed experimentally.

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.