| 학회 | 한국화학공학회 |
| 학술대회 | 2021년 봄 (04/21 ~ 04/23, 부산 BEXCO) |
| 권호 | 27권 1호, p.1128 |
| 발표분야 | 촉매 및 반응공학 |
| 제목 | A Machine Learning and DFT - based Design of Sub-nanometer Ruthenium Clusters for Electrochemical Nitrogen Reduction Reaction |
| 초록 | Ammonia synthesis via nitrogen reduction reaction (NRR) is one of the promising techniques to store and transport hydrogen. However, the Haber-Bosch process, the conventional approach for NH3 production, is considered one of the main reasons for global warming because it consumes large amount of fossil fuels and releases CO2 as a byproduct. Hence, electrochemical NRR at ambient conditions are considered a major alternative to this process. We studied electrochemical NRR on the sub-nano sized Ru clusters. In this report, the optimized structure of Run (n = 1-6) clusters and the adsorption energy of NRR intermediates were investigated using density functional theory (DFT) computation. From this result, we determined the NRR descriptor, reaction mechanism and the potential limiting steps on each catalyst. Furthermore, a three-layer artificial neural network (ANN) with geometrical and topological features was set up to predict NRR activities of Ru catalyst structures. This theoretical technique will provide efficient and high-throughput screening of sub-nano-scale clusters. |
| 저자 | 오제규1, 김승훈2, 송호창1, 함형철1 |
| 소속 | 1인하대, 2한국과학기술(연) |
| 키워드 | 촉매 |
| 원문파일 | 초록 보기 |
