Influence of Cu(111) and Ni(111) Substrates on the Capacitances of Monolayer and Bilayer Graphene Supercapacitor Electrodes
Bibliographic record
Abstract
The quantum capacitance model based on graphene’s fixed-band density of states (DOS) is one of the most popular approaches to modeling the capacitive behavior of graphene-based supercapacitor electrodes. This model, however, consistently overestimates the capacitance of graphene electrodes by an order of magnitude compared to experimental measurements. Moreover, the influence of conducting substrates used as electrical contacts for graphene is typically excluded altogether by its representation as an infinite capacitance connected in series with the quantum capacitance of pristine graphene. This is despite the significant change in the electrode’s total DOS because of graphene’s adsorption to the substrate. Using insights from density functional theory calculations, we present a general model for calculating electrode capacitance based on space charge distribution in graphene–metal junctions. The model predicts capacitance values ranging between 1.4 and 1.7 μF cm –2 for graphene on Cu(111) and Ni(111), which match closely with the experimentally reported range of 2–6 μF cm –2 . The model also predicts a constant capacitance for monolayer and bilayer graphene on Cu(111) and Ni(111), which challenges the popular assumption that the slightly field-tunable capacitance observed in practical supercapacitors can be attributed to the quantum capacitance of graphene in isolation from interactions with substrates and electrolytes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".