Performance Prediction of High-Entropy Perovskites LaSrMnCoFeO with Automated High-Throughput Characterization of Combinatorial Libraries and Machine Learning.

Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition-structure-performance relationships of high-entropy LaSrMnCoFeO perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells.

A Self-Assembled Multiphasic Thin Film as an Oxygen Electrode for Enhanced Durability in Reversible Solid Oxide Cells.

The implementation of nanocomposite materials as electrode layers represents a potential turning point for next-generation of solid oxide cells in order to reduce the use of critical raw materials. However, the substitution of bulk electrode materials by thin films is still under debate especially due to the uncertainty about their performance and stability under operando conditions, which restricts their use in real applications. In this work, we propose a multiphase nanocomposite characterized by a highly disordered microstructure and high cationic intermixing as a result from thin-film self-assembly of a perovskite-based mixed ionic-electronic conductor (lanthanum strontium cobaltite) and a fluorite-based pure ionic conductor (samarium-doped ceria) as an oxygen electrode for reversible solid oxide cells.