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Published: 2012

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Since current technologies for capturing CO2 to fight global climate change are still too energy intensive, there is a critical need for development of new materials that can capture CO2 reversibly with acceptable energy costs. Accordingly, solid sorbents have been proposed to be used for CO2 capture applications through a reversible chemical transformation. By combining thermodynamic database mining with first principles density functional theory and phonon lattice dynamics calculations, a theoretical screening methodology to identify the most promising CO2 sorbent candidates from the vast array of possible solid materials has been proposed and validated. The calculated thermodynamic properties of different classes of solid materials versus temperature and pressure changes were further used to evaluate the equilibrium properties for the CO2 adsorption/desorption cycles. According to the requirements imposed by the pre- and post- combustion technologies and based on our calculated thermodynamic properties for the CO2 capture reactions by the solids of interest, we were able to screen only those solid materials for which lower capture energy costs are expected at the desired pressure and temperature conditions. Only those selected CO2 sorbent candidates were further considered for experimental validations. The ab initio thermodynamic technique has the advantage of identifying thermodynamic properties of CO2 capture reactions without any experimental input beyond crystallographic structural information of the solid phases involved. Such methodology not only can be used to search for good candidates from existing database of solid materials, but also can provide some guidelines for synthesis new materials. In this presentation, we first introduce our screening methodology and the results on a testing set of solids with known thermodynamic properties to validate our methodology. Then, by applying our computational method to several different kinds of solid systems, we demonstrate that our methodology can predict the useful information to help developing CO2 capture Technologies.