First-principles simulation of capacitive charging of graphene and implications for supercapacitor design

TL;DR

This paper introduces a first-principles simulation method to study the charge storage in graphene electrodes, revealing how interfacial fields and electrode-electrolyte gaps influence capacitance, with implications for supercapacitor design.

Contribution

It presents a novel first-principles approach using the effective screening medium framework to simulate graphene-based supercapacitors more accurately.

Findings

Capacitance decreases in electrodes thinner than a few graphene layers due to space-charge screening.

Interfacial electric fields significantly affect capacitance in thin graphene electrodes.

Capacitance can be tuned by engineering the electrode-electrolyte gap.

Abstract

Supercapacitors store energy via the formation of an electric double layer, which generates a strong electric field at the electrode-electrolyte interface. Unlike conventional metallic electrodes, graphene-derived materials suffer from a low electronic density of states (i.e., quantum capacitance), which limits their ability to redistribute charge and efficiently screen this field. To explore these effects, we introduce a first-principles approach based on the effective screening medium framework, which is used to directly simulate the charge storage behavior of single- and multi-layered graphene in a way that more closely approximates operating devices. We demonstrate that the presence of the interfacial field significantly alters the capacitance in electrodes thinner than a few graphene layers, deriving in large part from intrinsic space-charge screening limitations. The capacitance is also found to be highly sensitive to the gap between the electrode and the solvent (contact layer), which offers possibilities for tuning the interfacial capacitance of the electrode by proper engineering of the electrolyte. Our results offer an alternative interpretation of discrepancies between experimental measurements and fixed-band models, and provide specific implications for improving graphene-based devices.