The University of Tokyo · 공학
Du Wen 교수의 연구실은 에너지 전환과 지속가능성 기반의 기술 혁신을 중심으로 활동하고 있습니다. 주요 연구 분야는 암모니아 기반 수소 에너지 시스템, 재생 가능 에너지 연료의 환경적 영향 평가, 그리고 고효율 에너지 변환 장치(예: 고온 산화물 연료전지)의 설계 및 성능 분석입니다. 또한, 교육 현장에서의 기술 통합, 특히 AI 기반 수학 교육 및 학생 참여 촉진 전략에 대한 연구도 병행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Ammonia serves as a promising hydrogen carrier and energy storage medium due to its high hydrogen content, ease of transport, and well-established production infrastructure. This study presents a comprehensive techno-economic analysis of ammonia-to‑hydrogen (A2H) and ammonia-to-power (A2P) pathways, comparing various process configurations for hydrogen production and power generation. High-temperature ammonia crackers (600 °C) achieve a maximum energy efficiency of 87.55 % and a maximum exergy e
Renewable synthesis fuels play a crucial role in enabling a circular economy. This study assesses the environmental impacts of power-to-hydrogen and biomass-to-hydrogen routes, considering four hydrogen storage options: hydrogen, ammonia, methane, and methanol with a function unit of 1 liter of a stored hydrogen-derived product. The assessment encompasses metrics such as carbon footprint, use of fossil and nuclear energy, ecosystem quality, human health impact, and water scarcity. The results re
Hydrogen, known for its high energy content and clean combustion, is promising in the energy transition. This study explores the environmental impact of a solid oxide fuel cell (SOFC) system. 1 kg of hydrogen production at 1 bar serves as the functional unit. The SOFC system generates hydrogen, electricity, and heat across five modes. Results indicate that the SOFC system achieves a global warming potential of 0.17–9.50 kg CO 2 -eq/FU using the system expansion method. Regional analysis shows th
This study refines the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to explore the factors influencing the adoption and utilization of artificial intelligence (AI) by Chinese mathematics teachers in STEM education, aiming to promote broader integration of AI within this domain. Utilising structural equation modelling (SEM) on survey data collected from 503 in-service mathematics teachers across China, the findings indicate that performance expectancy (PE), hedonic motivat