Influence of doping and solvent interactions on the electronic and capacitive properties of metal-supported graphene: A combined DFT and AIMD study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".