Pourbaix Machine Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Suppressed Ruthenium Dissolution.

The demand for green hydrogen has raised concerns over the availability of iridium used in oxygen evolution reaction catalysts. We identify catalysts with the aid of a machine learning-aided computational pipeline trained on more than 36,000 mixed metal oxides. The pipeline accurately predicts Pourbaix decomposition energy () from unrelaxed structures with a mean absolute error of 77 meV per atom, enabling us to screen 2070 new metallic oxides with respect to their prospective stability under acidic conditions.

Tuning Two-Electron Oxygen-Reduction Pathways for H O Electrosynthesis via Engineering Atomically Dispersed Single Metal Site Catalysts.

The hydrogen peroxide (H O ) generation via the electrochemical oxygen reduction reaction (ORR) under ambient conditions is emerging as an alternative and green strategy to the traditional energy-intensive anthraquinone process and unsafe direct synthesis using H and O . It enables on-site and decentralized H O production using air and renewable electricity for various applications. Currently, atomically dispersed single metal site catalysts have emerged as the most promising platinum group metal (PGM)-free electrocatalysts for the ORR.

Optimization of contact angle and interfacial tension measurements for fluid/rock systems at ambient conditions.

Quantification of interfacial tension (IFT) and contact angles is essential to characterize reservoir fluid-fluid and rock-fluid interactions. However, these measurements are highly dependent on chemical structure, surface residues, system conditions, flow dynamics and interface interactions. Interfacial interactions are specifically important for secondary and tertiary oil recovery processes where water or brine is injected to recover residual oil through complex multi-phase - rock/brine/oil- interactions Due to the delicate interactions across the interfaces, these systems are subjected to variations and inconsistencies that might be difficult to eliminate, thus, affecting the reliability, reproducibility, and certainty of the data.