Effects of Crumpling Stage and Porosity of Graphene Electrode on the Performance of Electrochemical Supercapacitor
Bibliographic record
Abstract
Abstract The performance characteristics of supercapacitors composed of crumpled graphene electrodes and aqueous NaCl electrolytes are investigated through Molecular Dynamics (MD) simulations using a newly developed crumpled graphene-based supercapacitor model. Results suggest that the three-dimensional configuration of crumpled graphene boosts electrolyte-electrode interaction. This improved interaction, which includes a larger ion-accessible zone, increases the specific capacitance of the supercapacitor by roughly 400% (16.4 μF/cm2) compared to planar graphene electrodes. Examining the effect of different stages of crumpling and the inclusion of pores on the electrode surface shows that the stages of crumpling substantially influence the supercapacitor performance. A smaller crumpling radius, meaning fully crumpled stage, improves the performance as increased crumpling leads to better packing efficiency, which aids in more ion separation. Furthermore, adding pores on the surface of crumpled graphene improves the ion accessibility by creating additional adsorption sites. An exceptional capacitance of 19.8 μF/cm2 is obtained for a porosity of 20%. However, the results suggest that the in-plane-porosity of the electrode needs to be optimized as there is a decline in specific capacitance after that point (20% porosity), indicating a suboptimal relationship between the charge distribution, specific surface area (SSA) and the porosity of the electrode.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".