Electrical double layer and capacitance of TiO2 electrolyte interfaces from first principles simulations

TL;DR

This study uses advanced ab initio-based machine learning simulations to reveal the microscopic structure and capacitance behavior of the electrical double layer at TiO2-electrolyte interfaces, highlighting limitations of mean-field models.

Contribution

It introduces a novel simulation approach combining ab initio methods and machine learning to accurately model the EDL at TiO2 interfaces, including electrostatic potential calculations.

Findings

Simulations match experimental capacitance values.

Basic solutions show higher interfacial capacitance than acidic ones.

Distinct charging mechanisms are identified for positive and negative surfaces.

Abstract

The electrical double layer (EDL) at aqueous solution-metal oxide interfaces critically affects many fundamental processes in electrochemistry, geology and biology, yet understanding its microscopic structure is challenging for both theory and experiments. Here we employ ab initio-based machine learning potentials including long-range electrostatics in large-scale atomistic simulations of the EDL at the TiO2-electrolyte interface. Our simulations provide a molecular-scale picture of the EDL that demonstrates the limitations of standard mean-field models. We further develop a method to accurately calculate the electrostatic potential drop at the interface. The computed capacitance originating from the adsorbed charges and the potential drop agrees with experiments, supporting the reliability of our description of the EDL. The larger interfacial capacitance of basic relative to acidic solutions originates from the higher affinity of the cations for the oxide surface and gives rise to distinct charging mechanisms on negative and positive surfaces.