(220b) Computational Design of Efficient Catalysts for CO2 Hydrogenation

The atmospheric concentration of carbon dioxide (CO₂) has significantly increased over the last several decades, mainly caused by burning fossil fuels. The use of fossil fuels is likely to continue over the next several decades. A potential way to mitigate the increase of CO₂ in the atmosphere is carbon capture and recycling (CCR), where CO₂ is captured and combined with hydrogen obtained from renewable and nuclear energy sources to produce liquid fuels such as methanol. However, hydrogenation of CO₂ is very challenging due to its chemical inertness and thermodynamic stability. There is therefore a need to design efficient catalysts for CO₂ hydrogenation in a cost-effective way, such as computational simulation. This presentation will discuss three novel catalysts for CO₂ hydrogenation that covers heterogeneous catalysis, homogeneous catalysis and the ways to bridge the gap between both, which were computationally designed and investigated using density functional theory (DFT) combined with microkinetic modeling and multiconfiguration complete active space (MC-SCF) calculations. In the first part, I will briefly talk about indium oxide (In₂O₃). We have computationally predicted that In₂O₃ has good selectivity and stability for CO₂ hydrogenation to produce methanol. This prediction had recently been validated by experiments. Following that, I will present the mechanistic studies on bimetallic molecular catalyst for CO₂ hydrogenation in THF solvent at room temperature. Finally, I will focus on Lewis pair functionalized metal organic frameworks (MOFs), which is a material computationally designed by me, that has a low barrier pathway for CO₂ hydrogenation to produce methanol in this novel catalyst. Since MOFs have been shown to be useful for separating mixtures of gases, MOFs functionalized with optimal catalytic functional groups, such as Lewis pairs, allow us to combine CO₂ capture and conversion in a single material, which could greatly improve the economics through process intensification.