Catalytic oxidation at surfaces: insight from first-principles statistical mechanics (abstract only)

Area of Science:

• Computational chemistry
• Materials science
• Chemical engineering

Background:

• First-principles modeling of heterogeneous catalysis is challenging due to the need to describe activity across diverse conditions.
• Ab initio electronic structure theory provides microscopic insights into elementary catalytic processes.
• Bridging microscopic events to mesoscopic/macroscopic material functionality requires understanding statistical interplays.

Purpose of the Study:

• To develop a multiscale modeling approach for predictive modeling of heterogeneous catalysis.
• To quantitatively describe catalytic activity by linking microscopic understanding to macroscopic properties.
• To investigate the influence of gas phase and reaction kinetics on catalyst surface properties.

Main Methods:

• Utilized a multiscale modeling strategy combining different theoretical frameworks.
• Employed density-functional theory (DFT) for accurate description of microscopic elementary processes.
• Integrated DFT results with thermodynamics and statistical mechanics for mesoscopic and macroscopic linking.

Main Results:

• Successfully applied the multiscale approach to model CO oxidation over the Pd(100) surface.
• Demonstrated the importance of the surrounding gas phase on catalyst surface structure and composition.
• Highlighted the role of reaction kinetics in determining catalyst surface properties.

Conclusions:

• The multiscale modeling approach enables predictive modeling of heterogeneous catalysis from first principles.
• This method accurately links microscopic catalytic events to macroscopic material behavior.
• Understanding gas phase and kinetic effects is crucial for designing efficient catalysts.