Liu Cijie, Park Jiyun, De Santiago Héctor A, Xu Boyuan, Li Wei, Zhang Dawei, Zhou Lingfeng, Qi Yue, Luo Jian, Liu Xingbo
Department of Mechanical, Materials and Aerospace Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, West Virginia 26506, United States.
School of Engineering, Brown University, 184 Hope Street, Providence, Rhode Island 02912, United States.
ACS Catal. 2024 Sep 25;14(19):14974-15013. doi: 10.1021/acscatal.4c03357. eCollection 2024 Oct 4.
Solar-driven thermochemical hydrogen (STCH) production represents a sustainable approach for converting solar energy into hydrogen (H) as a clean fuel. This technology serves as a crucial feedstock for synthetic fuel production, aligning with the principles of sustainable energy. The efficiency of the conversion process relies on the meticulous tuning of the properties of active materials, mostly commonly perovskite and fluorite oxides. This Review conducts a comprehensive review encompassing experimental, computational, and thermodynamic and kinetic property studies, primarily assessing the utilization of perovskite oxides in two-step thermochemical reactions and identifying essential attributes for future research endeavors. Furthermore, this Review delves into the application of machine learning (ML) and density functional theory (DFT) for predicting and classifying the thermochemical properties of perovskite materials. Through the integration of experimental investigations, computational modeling, and ML methodologies, this Review aspires to expedite the screening and optimization of perovskite oxides, thus enhancing the efficiency of STCH processes. The overarching objective is to propel the advancement and practical integration of STCH systems, contributing significantly to the realization of a sustainable and carbon-neutral energy landscape.
太阳能驱动的热化学制氢(STCH)是一种将太阳能转化为氢气(H)作为清洁燃料的可持续方法。该技术是合成燃料生产的关键原料,符合可持续能源原则。转化过程的效率依赖于对活性材料(最常见的是钙钛矿和萤石氧化物)性能的精确调控。本综述进行了全面的回顾,涵盖实验、计算以及热力学和动力学性质研究,主要评估钙钛矿氧化物在两步热化学反应中的应用,并确定未来研究工作的关键属性。此外,本综述深入探讨了机器学习(ML)和密度泛函理论(DFT)在预测和分类钙钛矿材料热化学性质方面的应用。通过整合实验研究、计算建模和ML方法,本综述旨在加速钙钛矿氧化物的筛选和优化,从而提高STCH过程的效率。总体目标是推动STCH系统的进步和实际整合,为实现可持续和碳中和能源格局做出重大贡献。