Atomic-Scale Understanding of Selectivity Control in Nitrate Reduction on Cu(100) Under Acidic and Alkaline...

Area of Science:

• Computational electrochemistry
• Surface science
• Catalysis

Background:

• Nitrate reduction reaction (NO3 RR) is crucial for nitrogen cycling and pollutant remediation.
• Understanding the selectivity of NO3 RR on metal surfaces is key for developing efficient catalysts.
• Computational methods are essential for elucidating reaction mechanisms at the atomic level.

Purpose of the Study:

• To investigate the pH-dependent selectivity of the nitrate reduction reaction (NO3 RR) on the Cu(100) surface.
• To develop and validate a computational framework for modeling electrocatalytic reactions under varying conditions.
• To predict the influence of electrode potential and pH on the formation of different reduction products.

Main Methods:

• Grand-canonical density functional theory (GC-DFT) simulations under constant electrode potential.
• Application of ionic correction schemes and gas-phase reference error corrections.
• DFT-based molecular dynamics with explicit water molecules and activation energy calculations for proton-electron transfer steps.

Main Results:

• Under acidic conditions and cathodic potentials (< 0.1 V RHE), NO3 RR favors nitric oxide and ammonium formation on Cu(100).
• Under alkaline conditions at comparable potentials, nitrite and hydroxylamine become the dominant products.
• The binding energies of key intermediates and transition states are modulated by pH and electrode potential.

Conclusions:

• The computational approach accurately reproduces experimentally observed potential- and pH-dependent selectivity trends.
• The developed modeling framework provides a general strategy for studying pH-dependent electrocatalysis.
• This study offers insights into designing selective catalysts for nitrate reduction by controlling reaction conditions.