Unraveling the Quantum Capacitance of Graphene Slit Pores with Ions under Extreme Confinement: A First-Principles Study
Bibliographic record
Abstract
Molecular simulations have played a crucial role in developing analytical models for microporous supercapacitor electrodes. However, these models overlook vital quantum mechanical details, which can only be captured through computationally demanding first-principles methods like density functional theory (DFT). We address this gap by introducing a computationally feasible DFT-based approach to simulate 2D slit pores with highly confined ions. Initial conditions for the simulations are derived from the latest literature’s virtual image obtained via in situ experiments and molecular simulations of extremely confined 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMI-TFSI) ions in graphene slit pores. The periodicity of the slit pores and the quasi-static state of the highly confined ions enable computationally feasible periodic plane-wave DFT calculations, yielding atomic-level insights into the pore and ion atomic structures, charge distribution, and, most importantly, the quantum capacitance ( C q ). Furthermore, we investigate the impact of pore-doping using Nitrogen on the C q, revealing potential-dependent results that hold particular significance for ion-saturated nanoporous systems. The proposed first-principles atomistic model represents a leap in the modeling resolution of microporous slit pore systems with confined ions. It unveils the contributions of ions and the pore atomic structure to the overall C q of the system, offering a comprehensive understanding of the intricate interplay between pore morphology, including defects and adatoms, and pore–ion interactions and their collective impact on the capacitance of pore/ion systems. The model complements existing electrochemical double-layer models and provides key insights for optimizing electrolyte and pore material selection in microporous electrochemical double-layer capacitors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".