Oxygen Non-Stoichiometry and Thermochemical Hydrogen Production of New Machine Learned Oxides

Thermochemical hydrogen production (TCH) is a promising approach to convert water into hydrogen by utilizing thermal energy from sources such as concentrated solar energy, as opposed to using electricity in electrochemical methods. This process consists of two main steps: first, a material undergoes thermal reduction at elevated temperatures (∼1300 – 1500 °C) under low oxygen partial pressures (pO₂) of ∼10⁻³ – 10⁻⁵ atm, which results in the formation of oxygen vacancies. Next, the temperature of the system is reduced to ∼800 − 1000 °C in the presence of steam, enabling water splitting through material oxidation. The commercial feasibility of TCH is dependent on identifying a material with an optimal reduction enthalpy that supports both the thermal reduction of redox-active cations and their re-oxidation, while also possessing high cycle stability. Perovskite metal oxides have received significant interest as potential TCH materials due to their ability to accommodate high concentrations of oxygen vacancies and their highly tunable thermodynamic properties. However, these materials often exhibit low hydrogen production due to re-oxidation and poor cycle stability challenges.
Our team has developed a machine learning algorithm to identify materials suitable for TCH applications. Based on the predictions generated by this algorithm, we have focused on exploring compounds with redox-active cations beyond the commonly studied first-row transition metals such as Fe, Co, and Mn. From the thousands of compounds predicted by the model, we have narrowed our selection to candidates based on criteria for oxygen vacancy formation energy and stability. In this presentation, we will discuss the oxygen vacancy concentrations of selected compounds, derived from thermogravimetric measurements conducted at 10⁻⁵ – 1 atm pO₂ and temperatures ranging from 1000 °C to 1450 °C. We will also present an analysis of point defect equilibria and hydrogen production data obtained from flow reactor experiments. Comparisons with existing high-temperature experimental and modeling studies will be included to provide context for, and validation of, our findings.

SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525