Computational design of catalysts for the selective conversion of carbon dioxide to valuable products
Dr. Ali Can Kizilkaya
Associate Professor
Department of Chemical Engineering, Izmir Institute of Technology, 35430 Izmir, Turkiye
alicankizilkaya@iyte.edu.tr
Carbon dioxide reduction based on green hydrogen is a promising long-term approach that can aid decarbonization of heavy industry and provide negative emissions to combat climate change. Catalysts are the key materials that can help us in this quest. However, most industrially applied catalysts are made from precious metals or designed with a trial-and-error approach. A much needed alternative is the so-called rational design, where we can design selective catalysts based on structure-activity relationships. In this aspect, molecular modeling of catalysts[1], combined with surface-sensitive experimental techniques can provide us these insights.
In this talk, I will demonstrate how important design principles for novel catalysts can be obtained via carefully integrating the understanding from experimental findings and molecular modeling. First, I will talk about the selectivity optimization of cobalt catalysts for carbon dioxide utilization. I will explain how the introduction of iron and alkali promoters have complimentary effects on key catalytic properties[2,3], which can be utilized to fine-tune the selectivity by changing their relative compositions. Secondly, I will focus on the production of nitriles[4], an important class of commodity chemicals, from a mixture of synthesis gas and ammonia on molybdenum nitride catalysts. The combined understanding obtained by detailed experimental analysis and an extensive investigation of the reaction mechanism elegantly shows how the active structure of the catalyst is formed under operando conditions and what these imply for the optimization of catalysts. Finally, I will talk about our collaborative work with Prof. Honkala and Dr. Melander in JYU. In this part, I will focus on how we focus on the rational design of molybdenum nitrides for electrocatalytic conversion of carbon dioxide by employing state-of-the-art Grand Canonical Ensemble-Density Functional Theory (GCE-DFT) simulations.
References
1) B.W.J. Chen, L. Xu, M. Mavrikakis, Chemical Reviews, 2021, 121, 1007-1048.
2) K. Jeske, A.C. Kizilkaya, I. López-Luque, N. Pfänder, M. Bartsch, P. Concepción, G. Prieto, ACS Catalysis,
2021, 11, 4784-4798.
3) D. Tuncer & A.C. Kizilkaya, Catalysts, 2023, 13, 1390.
4) A.C. Kizilkaya, M.E MartÃnez-Monje, G. Prieto, Catalysis Today, 2024, 442, 114947.
