Analysis reveals significant improvements in electrocatalytic activity and stability using machine learning to synthesize Ru/TiMnOx electrodes, highlighting its promise in hydrogen production.
Electrocatalysis plays a central role in clean energy conversion and sustainable technologies. However, the trade-off between activity and stability of electrocatalysts largely hinders their practical applications, notably in the oxygen evolution reaction for producing hydrogen and solar fuels. Here we report a steam-assisted synthesis armed with machine learning screening of an integrated Ru/TiMnOx electrode, featuring intrinsic metal-support interactions. These atomic-scale interactions with self-healing capabilities radically address the activity-stability dilemma across all pH levels. Consequently, the Ru/TiMnOx electrode demonstrate enhanced mass activities—48.5×, 112.8×, and 74.6× higher than benchmark RuO2 under acidic, neutral, and alkaline conditions, respectively. Notably, it achieves stable operation for up to 3,000 h, representing a multi-fold stability improvement comparable to other state-of-the-art catalysts. The breakthrough in activity-stability limitations highlights the potential of intrinsic metal-support interactions for enhancing electrocatalysis and heterogeneous catalysis in diverse applications. Developing efficient catalysts that resolve the activity-stability trade-off remains challenging for hydrogen production. Here, the authors report a steam-assisted, machine-learning-screened synthesis of self-healing Ru/TiMnOx electrodes that resolve this challenge.
No takes yet. Share an insight, caveat, or question.
Zhou et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: