Kyeounghak Kim
Hanyang University · Materials Science
About the Lab
Professor Kyeounghak Kim's research lab specializes in the design and mechanistic understanding of heterogeneous catalysts for sustainable energy and environmental applications. The lab focuses on tailoring the electronic and structural properties of oxide-based nanomaterials—particularly ceria and perovskites—through doping and nanostructuring to enhance catalytic activity in reactions such as CO oxidation, dry reforming of methane, and nitrous oxide reduction. Advanced theoretical approaches, especially density functional theory (DFT), are systematically integrated with experimental synthesis and characterization to uncover reaction mechanisms and guide the rational design of efficient catalysts. The lab also explores metal ex-solution phenomena and core–shell nanostructures to achieve high activity and stability in electrochemical and thermal catalytic processes.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Ceria (CeO 2 ) is an attractive catalyst because of its unique properties, such as facile redoxability and high stability. Thus, many researchers have examined a wide range of catalytic reactions on ceria nanoparticles (NPs). Among those contributions are the reports of the dopant-dependent catalytic activity of ceria. On the other hand, there have been few mechanistic studies of the effects of a range of dopants on the chemical reactivity of ceria NPs. In this study, we examined the catalytic a
Tuning of the cation–oxygen bond strength effectively promotes B-site ex-solution in a perovskite, thereby boosting the catalytic activity of CO oxidation.
Decalin is more easily dehydrogenated on Pt catalyst than Pd while the dehydrogenation of tetralin is more facile on Pd than Pt.
The selective formation of oxygen vacancies plays a key role in the phase transition to layered perovskite with B-metal ex-solution.
Because of the recent global warming environmental issues, dry reforming of methane (DRM)─which converts greenhouse gases (CO2 and CH4) into syngases (CO and H2)─is receiving significant attention. Recently, density functional theory (DFT) calculations have been effectively used to obtain fundamental information on DRM reactions. The DFT calculations can provide valuable theoretical knowledge in various heterogeneous catalyst systems, which is difficult to derive from experiments alone due to th
Nitrous oxide (N2O) is a notorious greenhouse gas because of its higher global warming potential and longer lifetime than those of CO2 and CH4. Here, we present a rational design of a highly stable and active electrocatalyst that surpasses the activity of conventional Pd catalysts for N2O reduction. Theoretical calculations predicted that the catalytic activity of surface Pd atoms in an Au@Pd core–shell structure can be increased by optimizing the thickness of the Pd shell. This prediction was c
Caffeic acid (CA) is well known for its strong adsorption on metal or metal oxide surfaces mostly due to the catecholic functional group. On the other hand, the detailed adsorption configurations and the effects of functional groups on molecular adsorption have not been clarified yet. In this study, first-principles calculations were implemented to elucidate the adsorption phenomena of CA and its deprotonated forms on Au(100), (110) and (111), and then predict the morphology of Au nanoparticles
Metal oxides possessing distinctive physical/chemical properties due to different crystal structures and stoichiometries play a pivotal role in numerous current technologies, especially heterogeneous catalysis for production/conversion of high-valued chemicals and energy. To date, many researchers have investigated the effect of the structure and composition of these materials on their reactivity to various chemical and electrochemical reactions. However, metal oxide surfaces evolve from their i
Brain-inspired neuromorphic computing systems, based on a crossbar array of two-terminal multilevel resistive random-access memory (RRAM), have attracted attention as promising technologies for processing large amounts of unstructured data. However, the low reliability and inferior conductance tunability of RRAM, caused by uncontrollable metal filament formation in the uneven switching medium, result in lower accuracy compared to the software neural network (SW-NN). In this work, we present a hi
Research Areas
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