Modeling-Making-Modulating High-Entropy Alloy with Activated Water-Dissociation Centers for Superior...

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• High-entropy alloys (HEAs) offer tunable electronic structures and unique binding sites, making them promising electrocatalysts.
• The vast compositional space of HEAs poses challenges for rational catalyst design, often relying on inefficient trial-and-error methods.

Purpose of the Study:

• To accelerate the rational design of high-performance quinary HEAs for multielectron transfer reactions using machine learning and first-principles calculations.
• To investigate the role of molybdenum (Mo) incorporation in activating HEA surfaces for enhanced methanol oxidation.

Main Methods:

• Employed a bottom-up research strategy combining machine learning with first-principles calculations.
• Designed and synthesized mesoporous PtPdRhRuMo catalysts in nanoparticle and thin-film forms.
• Evaluated electrocatalytic performance for methanol oxidation, including current density and mass activity.

Main Results:

• Successfully designed a quinary HEA, PtPdRhRuMo, with properties favoring the methanol oxidation reaction.
• Incorporation of Mo significantly enhanced methanol adsorption and water dissociation, facilitating CO2 production.
• Synthesized catalysts exhibited superior electrocatalysis with high current density (18.20 mA cm⁻²) and mass activity (9.89 A mgPt⁻¹).

Conclusions:

• The developed PtPdRhRuMo HEA demonstrates excellent electrocatalytic activity and durability for methanol oxidation.
• Machine learning-assisted first-principles calculations provide an efficient pathway for designing advanced HEA electrocatalysts.
• This work highlights the potential of HEAs in electrochemical applications, particularly for fuel cells.