Introduction of using molecular dynamics (MD) simulations for CO2 absorption

10.1039/C9CP03976G

“Complementing engineering studies, computational approaches are essential to reveal the underlying reaction mechanisms of CO₂ absorption and to develop new absorbing compounds.^5,6 Selectivity and reversibility of the absorbent, and its liquid structure properties such as diffusion coefficient and viscosities are important selection criteria. In computational solvent design, molecular dynamics (MD) simulations are able to yield information, which are difficult or sometimes impossible to obtain experimentally. For example, the diffusion coefficient of CO₂ in absorbing amine solution cannot be measured due to its reactivity and has to be extracted from nitrous oxide experiments. Recently, a wealth of information about liquid structure, intermolecular interactions and diffusion properties of aqueous alkanolamines, as well as solvation and mobility of CO₂ molecules within such solvents was obtained computationally.^7–18 However, all these studies were limited to the investigation of the pre-reaction state of CO₂ in aqueous amine solutions. During the chemical CO₂ absorption, carbamate and carbonate ions together with protonated alkanolamines are formed as products of the amine reaction with CO₂, which then would eventually change the complex mixed solvent properties. In addition, at increasing CO₂ loadings both non-reacted carbon dioxide and partially reacted solvents are present. The design and control of such a chemical absorption process requires information about the solvent properties changes during the reaction. Data about pre-reaction solvents are scarce in comparison to unreacted solvents. The kinetic network modelling CO₂ in aqueous alkanolamine solutions requires accurate diffusion coefficients of all solvent components present in solution.^19–21 Experimental viscosity studies of individual constituents do not allow an extrapolation of diffusion coefficients to the reactive solution. Therefore, structure-based simulations are indispensable to provide these missing data and only classical MD simulations allow a sufficient sampling and averaging over long trajectories and a large number of particles to give accurate results.”