Generalizable Trends in Electrochemical Protonation Barriers

Area of Science:

• Computational Chemistry
• Surface Science
• Electrochemistry

Background:

• Accurate prediction of activation energies is crucial for catalytic process modeling.
• Electrochemical reactions present unique challenges for barrier computations.
• Generalizable energetic trends can aid in analyzing complex reaction networks.

Purpose of the Study:

• To investigate energetic trends in the electrochemical protonation of *C, *N, and *O adsorbates.
• To provide insights into the factors governing these trends.
• To inform catalyst design and reaction mechanism analysis.

Main Methods:

• Density Functional Theory (DFT) calculations.
• Machine learning nudged elastic band (ML-NEB) models.
• Simulation of electrochemical protonation on transition metal surfaces.

Main Results:

• Observed a consistent trend of decreasing protonation reaction energies from *O to *N to *C.
• Found increasing activation barriers in the same *O to *N to *C trend.
• Analysis of bond orders and reaction pathways explained the observed energetic trends.

Conclusions:

• The identified trends in protonation energetics are significant for understanding electrochemical reactions.
• These findings can simplify the analysis of reaction mechanisms for both monatomic and polyatomic adsorbates.
• The results offer valuable guidance for designing improved catalysts.