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Korean J. Met. Mater.
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Korean Journal of Metals and Materials
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Discovery of Alloy Catalysts for Ammonia Decomposition by Machine Learning-Based Prediction of Adsorption Energies
기계 학습 기반 흡착에너지 예측을 통한 암모니아 분해용 합금 촉매 탐색
Byung Chul Yeo, So Yun Jeong, Jun Su Kim, Donghun Kim여병철, 정소윤, 김준수, 김동훈
Korean J. Met. Mater.
2024;62(11):920-927. Published online 2024 Nov 5
DOI:
https://doi.org/10.3365/KJMM.2024.62.11.920
Abstract
Ammonia decomposition has gained significant attention as an eco-friendly method for hydrogen production because it creates no carbon dioxide emissions. While Ru catalysts are known for their high activity in ammonia decomposition, their high cost makes them uneconomical for commercial use. Therefore, it is essential to explore novel alloy catalysts.....
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Crossref 1
High-throughput Screening Computation for Discovery of Porous Zeolites for Hydrogen Storage
수소 저장용 다공성 제올라이트 탐색을 위한 고속 대량 스크리닝 계산
Byung Chul Yeo여병철
Korean J. Met. Mater.
2022;60(7):537-544. Published online 2022 Jun 3
DOI:
https://doi.org/10.3365/KJMM.2022.60.7.537
Abstract
Hydrogen is considered an attractive energy resource because it is eco-friendly in contrast with fossil fuels. Hydrogen storage remains as essential technology for increasing the use of the hydrogen in applications such as hydrogen vehicles and fuel cells. Hydrogen storage requires retaining a high density of hydrogen molecules at ambient.....
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Web of Science 1
Crossref 3
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Journal Impact Factor 1.4