There are several methods to predict adsorption energies, which can be broadly classified into experimental and computational approaches.
Experimental Approaches 1. Temperature-Programmed Desorption (TPD): Measures the amount of adsorbate desorbed as a function of temperature to estimate adsorption energies. 2. Calorimetry: Directly measures the heat released or absorbed during adsorption. 3. Spectroscopic Methods: Techniques like Infrared Spectroscopy (IR) and X-ray Photoelectron Spectroscopy (XPS) can provide insights into the adsorption energies indirectly.
Computational Approaches 1. Density Functional Theory (DFT): A quantum mechanical method that provides accurate adsorption energies by solving the Schrödinger equation for the interacting systems. 2. Molecular Dynamics (MD) Simulations: These simulations can predict adsorption energies by modeling the interactions over time. 3. Machine Learning Models: These models can predict adsorption energies by learning from large datasets of known adsorption energies.
