Understanding Alkaline Hydrogen Oxidation Reaction on PdNiRuIrRh High-Entropy-Alloy by Machine Learning Potential

Area of Science:

• Materials Science
• Electrochemistry
• Catalysis

Background:

• High-entropy alloys (HEAs) are promising electrocatalysts due to their unique properties.
• Understanding the atomic structure of HEAs is crucial for optimizing their catalytic mechanisms.
• The hydrogen oxidation reaction (HOR) is a key process in fuel cells and electrolyzers.

Purpose of the Study:

• To develop and characterize a novel HEA catalyst for the alkaline hydrogen oxidation reaction (HOR).
• To elucidate the atomic structure-activity relationship governing the HOR performance of the HEA.
• To provide fundamental insights into HEA catalytic mechanisms for advanced electrocatalyst design.

Main Methods:

• Synthesis and characterization of a PdNiRuIrRh high-entropy alloy catalyst.
• Electrochemical testing to evaluate HOR activity and stability in alkaline media.
• Machine learning potential-based Monte Carlo simulations to investigate surface atomic structures and bonding environments.

Main Results:

• The synthesized HEA-PdNiRuIrRh catalyst exhibited a mass activity of 3.25 mA μg⁻¹, an 8-fold enhancement over commercial Pt/C.
• Simulations identified dominant Pd-Pd-Ni and Pd-Pd-Pd bonding environments contributing to optimized *H adsorption/desorption.
• The presence of Ni/Ru oxophilic sites was found to enhance *OH adsorption, crucial for the HOR mechanism.

Conclusions:

• The HEA-PdNiRuIrRh catalyst demonstrates superior performance for the alkaline HOR.
• Specific atomic arrangements and electronic structures within the HEA are key to its enhanced catalytic activity.
• This study offers valuable insights for designing next-generation high-entropy alloy electrocatalysts for energy applications.