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| Type |
Symposium |
| Area |
R&D beyond Carbon Society II |
| Room No. |
Room 306A |
| Time |
THU 15:40-: |
| Code |
ENVR-1 |
| Subject |
Computational Design of Efficient Catalyst for NH3 Decomposition and Synthesis |
| Authors |
Hyung Chul Ham
Fuel Cell Research Center, Korea Institute of Science and Technology (KIST), Korea |
| Abstract |
Today, the human society strongly relies on the fossil fuels for the energy sources. However, the environmental crisis (such as the global warming, and pollutant discharge) caused by the use of fossil fuels and the depletion of fossil fuels drives the human beings to develop the new alternatives to fossil fuels. The hydrogen energy has been considered to be the promising option for simultaneously solving such energy and environmental issues. One of bottlenecks for attaining the hydrogen-powered society is to find the efficient hydrogen carrier. Among various hydrogen carriers, ammonia has received much attention in recent years due to the high gravimetric (17.8wt% H2) and volumetric (121 kg H2 m-3 in the liquid form) hydrogen density. For the effective application of ammonia to the hydrogen carrier, the highly active catalyst for ammonia decomposition and synthesis should be developed. However, a detailed understanding of how to control the activity of catalysts is still lacking, despite its importance in designing and developing new effective ammonia decomposition and synthesis catalysts. This is in large part due to the difficulty of direct characterization. Alternatively, quantum mechanics-based computational approaches have emerged as a powerful and flexible means to unravel the complex catalysis in nanocatalysts. In this talk, I will present the recent research activity on the design of highly efficient ammonia-related catalysts using first-principles density functional theory(DFT) and machine learning approach. |
| E-mail |
hchahm@kist.re.kr |
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122nd General Meeting of the KCS