Effect of the metallicity on the capacitance of gold - aqueous sodium chloride interfaces

Abstract

Electrochemistry experiments have established that the capacitance of electrode-electrolyte interfaces is much larger for good metals such as gold and platinum than for carbon-based materials. Despite the development of elaborate electrode interaction potentials, to date molecular dynamics simulations were not able to capture this effect. Here we show that changing the width of the Gaussian charge distribution used to represent the atomic charges in gold is an effective way to tune its metallicity. Larger Gaussian widths lead to a capacitance of aqueous solutions (pure water and 1 molar NaCl) in good agreement with recent ab initio molecular dynamics results. For pure water, the increase in the capacitance is not accompanied with structural changes, while in the presence of salt the Na${}^{+}$ cations tend to adsorb significantly on the surface. For a strongly metallic gold electrode, these ions can even form inner sphere complexes on hollow sites of the surface.

arXiv comment16 pages

References in corpus (7)

• Solvation at Aqueous Metal Electrodes
• Molecular Simulation of Electrode-Solution Interfaces
• Charge fluctuations from molecular simulations in the constant-potential ensemble
• A semiclassical Thomas-Fermi model to tune the metallicity of electrodes in molecular simulations
• Voltage equilibration for reactive atomistic simulations of electrochemical processes
• Size-dependence of hydrophobic hydration at electrified gold/water interfaces
• Characterizing heterogeneous dynamics at hydrated electrode surfaces

Cited by in corpus (7)

• ELECTRODE: An electrochemistry package for atomistic simulations
• Microscopic origin of the effect of substrate metallicity on interfacial free energies
• Frequency-dependent impedance of nanocapacitors from electrode charge fluctuations as a probe of electrolyte dynamics
• A molecular perspective on induced charges on a metallic surface
• Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities
• Deriving effective electrode-ion interactions from free-energy profiles at electrochemical interfaces
• Macro-Dipole-Constrainted Learning of Atomic Charges for Accurate Electrostatic Potentials at Electrochemical Interfaces