Statistical Sampling-Driven Design for Supported Bimetallic Nanocatalysts for CO₂ Reduction

Area of Science:

• Materials Science
• Nanotechnology
• Catalysis

Background:

• Tailoring supported bimetals into alloyed or phase-separated structures is crucial but challenging due to support interferences.
• Conventional methods rely on trial-and-error, lacking efficiency and generality.

Purpose of the Study:

• To present a rational and systematic design strategy for bimetallic nanocatalysts (NCs).
• To overcome limitations in controlling supported bimetal structures using statistical sampling and advanced simulation techniques.

Main Methods:

• Utilized a metadynamics-based gas-solid nanoreactor approach for statistical sampling of alloy formation.
• Employed a proof-of-concept system using metal oxide-supported PdAu coupling for CO2 reduction.
• Integrated theoretical simulations with experimental validation.

Main Results:

• Successfully predicted and realized alloy formation or phase separation of supported PdAu NCs.
• PdAu alloys on CeO2 enhanced CO2 hydrogenation to CO via a formate pathway.
• Phase-separated PdAu on TiO2 improved formic acid production efficiency through enhanced CO2 activation via a bicarbonate pathway.

Conclusions:

• Statistical sampling provides a general framework for the rational design of advanced bimetallic NCs.
• The developed strategy enables precise control over bimetal structures for tailored catalytic performance.