Theoretical prediction of interfacial capacitance in graphene-based supercapacitors is crucial to accelerating materials’ design and development cycles. However, there is currently a significant gap between ab initio predictions and experimental reports, particularly in the case of nitrogen-doped graphene. Analyses based on changes to the density of states of freestanding graphene upon doping do not account for the electronic interactions between the electrode, dopants, and substrates. The result is an overestimation of the doping-induced capacitance increase by up to two orders of magnitude. Moreover, it is unclear whether electrolyte and solvent interactions can further complicate matters by inducing changes to the band structure and, therefore, the capacitive properties of the electrode. A third complication lies in the fixed-band approximation, where materials are simulated without accounting for the influence of an external electrical field. In this work, we present an interfacial modeling and characterization procedure that leverages the combined strengths of ab-initio molecular dynamics, density functional theory, and microscopic polarization theory to produce reliable predictions of interfacial capacitance. The procedure is applied to two case studies of interest in supercapacitor design: (1) nitrogen-doped graphene on a Cu(111) substrate and (2) an interface between bulk water and Cu(111)-supported graphene at room temperature. Results show that water alters graphene’s band structure from a semi-metallic to an n-doped-semiconducting character and that metallic substrates dominate the band structure of the electrode interface even in the presence of dopants. The water interface also shows an asymmetric capacitive response relative to the polarity of the applied field.
Skip Nav Destination
Article navigation
14 December 2023
Research Article|
December 11 2023
Influence of doping and solvent interactions on the electronic and capacitive properties of metal-supported graphene: A combined DFT and AIMD study
Special Collection:
Carbon-based Materials for Energy Conversion and Storage
Mohamed K. Elshazly
;
Mohamed K. Elshazly
a)
(Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft)
1
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto
, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
a)Author to whom correspondence should be addressed: m.elshazly@mail.utoronto.ca
Search for other works by this author on:
Ahmed Huzayyin;
Ahmed Huzayyin
(Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing)
1
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto
, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
2
Electrical Power and Machines Department, Faculty of Engineering, Cairo University
, Giza 12316, Egypt
Search for other works by this author on:
Francis Dawson
Francis Dawson
(Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing)
1
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto
, 10 King’s College Road, Toronto, Ontario M5S 3G4, Canada
Search for other works by this author on:
a)Author to whom correspondence should be addressed: m.elshazly@mail.utoronto.ca
J. Chem. Phys. 159, 224704 (2023)
Article history
Received:
September 24 2023
Accepted:
November 17 2023
Citation
Mohamed K. Elshazly, Ahmed Huzayyin, Francis Dawson; Influence of doping and solvent interactions on the electronic and capacitive properties of metal-supported graphene: A combined DFT and AIMD study. J. Chem. Phys. 14 December 2023; 159 (22): 224704. https://doi.org/10.1063/5.0177808
Download citation file:
Sign in
Don't already have an account? Register
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Pay-Per-View Access
$40.00
368
Views
Citing articles via
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.