Probabilistic microkinetic modeling: Species balance equations for a catalyst surface containing multiple short-range...

Area of Science:

• Chemical Engineering
• Surface Science
• Computational Chemistry

Background:

• Adsorbed molecules on catalysts exhibit non-random spatial correlations due to inter-species interactions and surface processes.
• Current species balance equations in heterogeneous catalysis often neglect these crucial spatial correlations.
• Probabilistic microkinetic modeling (p-MKM) offers a framework to incorporate spatial correlations using short-ranged order (SRO) parameters.

Purpose of the Study:

• To extend the p-MKM framework to more complex catalytic systems with longer-range interactions and multiple adsorbed species.
• To investigate the impact of including multiple pair probabilities on modeling accuracy.
• To determine the optimal number of pair probabilities needed to capture system complexity.

Main Methods:

• Development of an extended p-MKM framework incorporating multiple pair probabilities.
• Simulation and analysis of catalytic systems with increased complexity.
• Probing the interplay between different SRO parameters.

Main Results:

• The extended p-MKM successfully incorporates multiple pair probabilities for higher complexity systems.
• The study analyzes the interplay and significance of various SRO parameters.
• Insights are gained into the number of pair probabilities required for accurate modeling.

Conclusions:

• The enhanced p-MKM framework provides a more accurate representation of heterogeneous catalytic systems by including spatial correlations.
• The inclusion of multiple pair probabilities is essential for capturing complex adspecies arrangements.
• This approach advances the modeling of catalytic processes by accounting for molecular-level ordering.