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 (MnCo2O4) by assuming that flower-shaped fillers would provide an additional easily accessible surface for charge adsorption. Less than 10 wt % MnCo2O4 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 MnCo2O4-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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".