Recent developments in modeling the electric double layer with density functional theory

01 October 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Improving our fundamental understanding of charge transfer processes at the electrified double layer currently relies heavily on density functional theory (DFT) simulations, as many in situ and operando spectroscopic methods are hindered by the aqueous electrolyte. However, modeling charged states with semi-local DFT faces serious challenges, and several bifurcating strategies have been developed in an attempt to address them. In this Mini Review, we present a highly abridged overview of some of the challenges faced when modeling charge transfer processes across the electric double layer with DFT. Focusing primarily on charge transfer kinetics, we highlight polarizable continuum models (PCMs) and their use in evaluating energetics in the adiabatic limit of electron transfer, i.e. treating electrons grand canonically during a coupled proton-electron transfer (CPET) reaction. We highlight their use in understanding electrocatalytic processes, in particular the ability to localize transition states at constant potential. Finally, we present our outlook on opportunities for improvement in this critical research area, and nascent methods being developed to test the validity of PCMs and evaluating energetics in the grand canonical ensemble.

Keywords

catalysis
electrochemistry

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.