Stabilizing Ru in Multicomponent Alloy as Acidic Oxygen Evolution Catalysts with Machine Learning-Enabled Structural...

Area of Science:

• Materials Science
• Electrochemistry
• Catalysis

Background:

• Developing active, stable, and cost-effective catalysts for the acidic oxygen evolution reaction (OER) is essential for electrochemical water splitting and large-scale hydrogen production.
• Ruthenium (Ru) is a highly active OER catalyst but suffers from poor long-term durability in acidic media.

Purpose of the Study:

• To stabilize active ruthenium sites within a multicomponent alloy (Ru(Ir,Fe,Co,Ni)_1-) to improve OER activity and durability.
• To investigate the phase formation, OER performance, and surface reconstruction of these alloys under acidic OER conditions.

Main Methods:

• Synthesis and characterization of Ru(Ir,Fe,Co,Ni)_1- multicomponent alloys.
• Electrochemical testing to evaluate OER activity and stability.
• Machine-learned interatomic potential (MLIP) coupled with replica-exchange molecular dynamics to model atomic mixing and phase behavior.
• Machine learning-accelerated high-throughput simulations to screen potential quinary alloys.

Main Results:

• The optimized Ru_0.20(Ir,Fe,Co,Ni)_0.80 catalyst demonstrated excellent OER activity (∼237 mV overpotential at 10 mA cm^-2) and enhanced stability (∼1.1 mV h^-1 degradation rate over 24 h).
• The alloy exhibited a multiphase structure (fcc and hcp) with well-mixed bulk phases, supported by MLIP simulations.
• Acidic OER conditions led to the formation of a RuIr-rich oxide shell, stabilizing Ru near the nanoparticle surface.

Conclusions:

• Stabilizing ruthenium in a multicomponent alloy matrix significantly enhances its activity and durability for acidic OER.
• The developed alloy presents a promising strategy for cost-effective and efficient hydrogen production via water splitting.
• Machine learning approaches accelerate the discovery and optimization of advanced electrocatalyst materials.