Catalytic Activity Maps for Alloy Nanoparticles

Area of Science:

• Materials Science
• Catalysis
• Computational Chemistry

Background:

• Rational design of alloy nanoparticle catalysts requires predictive models for activity.
• Understanding the influence of size, composition, and atomic structure on catalytic performance is crucial.

Purpose of the Study:

• To develop a computational approach for generating catalytic activity maps of alloy nanoparticles.
• To predict optimal alloy nanoparticle structures and compositions for specific catalytic reactions.

Main Methods:

• Utilized a quaternary cluster expansion to predict adsorbate binding energies on alloy nanoparticles.
• Incorporated cluster expansion into kinetic Monte Carlo simulations to predict nanoparticle structures and turnover frequencies.
• Applied the approach to Pt-Ni octahedral nanoparticle catalysts for the oxygen reduction reaction (ORR).

Main Results:

• Developed a method to map catalytic activity based on nanoparticle size and composition.
• Predicted optimal specific activity for ORR at Pt-Ni compositions of Pt_0.85Ni_0.15 and edge lengths > 5.5 nm.
• Predicted optimal mass activity for ORR at Pt-Ni compositions of Pt_0.8Ni_0.2 and edge lengths of 3.3-3.8 nm.

Conclusions:

• The developed approach enables rational design of alloy nanoparticle catalysts.
• Catalytic activity maps provide valuable insights for optimizing nanoparticle catalysts for reactions like ORR.