Yoshihiro Yamazaki

Barium zirconate has attracted particular attention among candidate proton conducting electrolyte materials for fuel cells and other electrochemical applications because of its chemical stability, mechanical robustness, and high bulk proton conductivity. Development of electrochemical devices based on this material, however, has been hampered by the high resistance of grain boundaries, and, due to limited grain growth during sintering, the high number density of such boundaries. Here, we demonst

Recent literature indicates that cation non-stoichiometry in proton-conducting perovskite oxides (ABO3) can strongly influence their transport properties. Here we have investigated A-site non-stoichiometry in Ba1−xZr0.8Y0.2O3−δ, a candidate electrolyte material for fuel cell and other electrochemical applications. Synthesis is performed using a chemical solution approach in which the barium deficiency is precisely controlled. The perovskite phase is tolerant to barium deficiency up to x = 0.06 a

Thermogravimetry has been used to evaluate the equilibrium constants of the water incorporation reaction in yttrium-doped BaZrO3 with 20−40% yttrium in the temperature range 50−1000 °C under a water partial pressure of 0.023 atm. The constants, calculated under the assumption of a negligible hole concentration, were found to be linear in the Arrhenius representation only at low temperatures (≤500 °C). Nonlinearity at high temperatures is attributed to the occurrence of electronic defects. The hy

Hydrogen production increases with increasing Sr content, but at a kinetic penalty; intermediate Sr levels are advantageous for solar thermochemical fuel production.

Abstract The environmental benefits of fuel cells and electrolyzers have become increasingly recognized in recent years. Fuel cells and electrolyzers that can operate at intermediate temperatures (300–450 °C) require, in principle, neither the precious metal catalysts that are typically used in polymer‐electrolyte‐membrane systems nor the costly heat‐resistant alloys used in balance‐of‐plant components of high‐temperature solid oxide electrochemical cells. These devices require an electrolyte wi

Proton-conducting perovskite oxides are attractive as electrolytes for environmentally friendly electrochemical devices, giving rise to a demand for a variety of oxides. However, complex phenomena occurring during hydration present challenges for expanding the materials library. Herein, we demonstrate the accelerated discovery of a proton-conducting oxide using data-driven structure–property maps for hydration of 8613 hypothetical perovskite oxides in descriptor spaces characterized as important

Proton-conducting oxides, specifically doped barium zirconates, have garnered much attention as electrolytes for solid-state electrochemical devices operable at intermediate temperatures (400–600 °C). In chemical terms, hydration energy, Ehyd, and proton–dopant association energy, Eas, are two key parameters that determine whether an oxide exhibits fast proton conduction, but to date ab initio studies have for the most part studied each parameter separately, with no clear correlation with proton

Water splitting using a semiconductor photocatalyst has been extensively studied as a means of solar-to-hydrogen energy conversion. Powder-based semiconductor photocatalysts, in particular, have tremendous potential in cost mitigation due to system simplicity and scalability. The control and implementation of powder-based photocatalysts are, in reality, quite complex. The identification of the semiconductor–photocatalytic activity relationship and its limiting factor has not been fully solved in

Abstract High‐throughput computational screening and machine learning hold significant potential for exploring diverse chemical compositions and discovering novel inorganic solids. However, the complexity of point defects, which occur in all inorganic solids and are often crucial to their functionality and synthesizability, presents significant challenges. Here, this study presents a defect‐chemistry‐trained, interpretable machine learning approach, designed to accelerate the exploration and dis