Publications by authors named "Hector A De Santiago"
Article Synopsis
- Solar-driven thermochemical hydrogen (STCH) production is an eco-friendly method for converting solar energy into hydrogen fuel, vital for creating synthetic fuels in a sustainable energy framework.
- This Review focuses on the performance of perovskite and fluorite oxides in thermochemical reactions, emphasizing the need for precise tuning of their properties for better efficiency.
- It also explores how machine learning and density functional theory can predict and optimize the thermochemical characteristics of perovskite materials to improve STCH processes, aiming for a more sustainable, carbon-neutral energy system.
Mixing multiple cations can result in a significant configurational entropy, offer a new compositional space with vast tunability, and introduce new computational challenges. For applications such as the two-step solar thermochemical hydrogen (STCH) generation techniques, we demonstrate that using density functional theory (DFT) combined with Metropolis Monte Carlo method (DFT-MC) can efficiently sample the possible cation configurations in compositionally complex perovskite oxide (CCPO) materials, with (LaSr)(MnFeCoAl)O as an example. In the presence of oxygen vacancies (), DFT-MC simulations reveal a significant increase of the local site preference of the cations (short-range ordering), compared to a more random mixing without .
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