Void Space Control in Porous Carbon for High-Density Supercapacitive Charge Storage
Bibliographic record
Abstract
High-density charge (energy) storage under supercapacitive mode requires an electrode that would deliver larger space for charge accumulation and offer a larger electrochemical potential difference at an electrode–electrolyte interface. Porous carbon has been a preferred electrode for commercial supercapacitors; however, its charge storability is much lower than that of state-of-the-art charge-storage devices such as lithium-ion batteries. We show that one of the primary limiting factors is the voids in porous carbon, which do not contribute to the capacitance because their sizes are much larger than the size of the solvated/unsolvated ions in the electrolyte. We partially activate these voids by filling them with a flower-shaped 3D hierarchical pseudocapacitive material (MnCo 2 O 4 ) by assuming that flower-shaped fillers would provide an additional easily accessible surface for charge adsorption. Less than 10 wt % MnCo 2 O 4 in the porous carbon from palm kernel shells through simple wet impregnation results in a five-fold increase in the charge storability. Laboratory prototypes of symmetric supercapacitors are fabricated using the MnCo 2 O 4 -filled carbon electrodes, which show five times higher specific energy than pure carbon and are cycled over 5000 times with >95% capacitance retention. The present strategy of activating the voids by hierarchical 3D nanostructures could be applied to build high-performance energy-storage devices.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".