In the digital age, various computational chemistry techniques provide molecular-level insights into heterogeneous catalysts. This project investigates how factors such as catalyst composition and metal-support interaction influence selectivity, enabling the design of superior catalysts for selective CO2 hydrogenation.
This project leverages computational chemistry techniques to gain molecular-level understanding of principles underlying the selectivity of bimetallic supported CO2 hydrogenation catalysts. While experimental studies suggest that factors such as catalyst composition, nanoparticle size and metal-support interactions influence reaction selectivity, the underlying mechanisms remain unclear. To address this question, advanced computational chemistry methods are employed to predict how reaction conditions, composition, and other factors affect performance, while taking the often-overlooked dynamic nature of these systems into account. The chemical space of bimetallic supported catalysts is systematically explored to identify (meta)stable configurations, reactive states, and potential deactivation pathways. By integrating these insights, this project aims to bridge fundamental understanding and catalyst design, guiding the development of superior catalysts for selective CO2 hydrogenation into valuable chemicals such as methanol.
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