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We developed a new Bayesian method to analyze carbon monoxide (CO) adsorption and desorption on palladium surfaces. This approach enhances model interpretability and uncertainty quantification for chemical processes.

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Area of Science:

  • Physical Chemistry
  • Surface Science
  • Computational Chemistry

Background:

  • Adsorption and desorption of carbon monoxide (CO) on palladium (Pd(111)) surfaces are crucial in catalysis and chemical processes.
  • Time-resolved infrared spectroscopy provides detailed kinetic data, necessitating advanced analytical methods.
  • Interpretable models and uncertainty quantification are vital for understanding complex chemical dynamics.

Purpose of the Study:

  • To develop a novel Bayesian approach for studying CO adsorption/desorption on Pd(111).
  • To learn key parameters: time-dependent coverage, rate constants, activation energies, and pre-exponential factors.
  • To integrate physical constraints and quantify uncertainties within the chemical process model.

Main Methods:

  • A probabilistic model was designed for the adsorption-desorption system.
  • Particle Markov chain Monte Carlo (PMCMC) sampling was employed to infer hidden coverages and rate constants.
  • Two Bayesian formulations were utilized to determine activation energies and pre-exponential factors.

Main Results:

  • The Bayesian approach successfully learned parameters characterizing CO adsorption and desorption kinetics.
  • Inferred activation energies and pre-exponential factors align with existing experimental literature.
  • The method demonstrated flexibility in incorporating physical constraints and quantifying parameter uncertainties.

Conclusions:

  • The novel Bayesian framework provides a robust method for analyzing surface adsorption/desorption processes.
  • The approach offers improved model interpretability and uncertainty quantification for chemical kinetics.
  • The methodology is applicable to other chemical systems and spectroscopic data.