Machine-learning-driven integrated probing of oxygen-vacancy distribution and ionomer morphology in iridium oxide catalyst–ionomer nanocomposite electrode for water electrolyzer

Key Points

• This research aims to enhance the efficiency and stability of water electrolyzers by optimizing the oxygen vacancy content and ionomer distribution in IrO2-x electrodes.
• Utilized machine learning algorithms to analyze the spatial distribution of oxygen vacancies.
• Investigated the morphology of ionomer within IrO2-x–ionomer nanocomposite electrodes.
• Conducted experiments to assess the performance of the optimized electrodes.
• Improved understanding of the correlation between oxygen vacancy distribution and ionomer morphology.
• Identified optimal vacancies that enhance catalytic performance.
• Demonstrated significant efficiency gains in water electrolysis with optimized electrode structures.