Resolving sequential electron-proton transfer kinetics for electrochemical CO₂ reduction at the Cu(100)/H₂O interface...

Area of Science:

• Computational chemistry and materials science
• Electrocatalysis and energy conversion
• Quantum mechanics and molecular dynamics

Background:

• Electron-transfer (ET) and proton-transfer (PT) are crucial for electrocatalysis but challenging to model theoretically due to complex interactions.
• Understanding these dynamics is key to improving energy conversion and chemical synthesis efficiency.
• Existing models struggle to unify electronic, nuclear, and solvent effects in ET/PT processes.

Purpose of the Study:

• To develop and apply a quantum-classical multiscale framework for investigating ET and PT dynamics.
• To elucidate the mechanisms of CO2 reduction on Cu(100) in explicit water, focusing on the interplay of ET and PT.
• To provide a theoretical basis for designing more efficient electrocatalysts.

Main Methods:

• Integration of constrained density functional theory (CDFT) with machine learning accelerated molecular dynamics (MLMD).
• Training distinct ML potentials for adiabatic and diabatic states to enable efficient sampling and maintain quantum fidelity.
• Application of diabatic free-energy surface calculations and vibronic proton-coupled electron transfer (PCET) theory.

Main Results:

• CO2 reduction proceeds via sequential inner-sphere ET to form CO2- followed by PT to form COOH.
• Solvent reorganization kinetically constrains ET but stabilizes the CO2- intermediate via ion-dipole interactions.
• Solvent relaxation dynamically adjusts distances, enhancing vibronic coupling and facilitating nonadiabatic PT.
• The sequential ET-PT pathway is significantly faster (5 orders of magnitude) than concerted PCET at the potential of zero charge (PZC).

Conclusions:

• The developed quantum-classical framework provides a robust method for analyzing ET/PT kinetics at electrochemical interfaces.
• The study highlights the critical interplay between quantum nuclear effects, vibronic couplings, and solvent dynamics in electrocatalysis.
• Findings offer insights into optimizing CO2 reduction pathways for enhanced energy conversion efficiency.