Carbon cathode with heteroatom doping and ultrahigh surface area enabling enhanced capacitive behavior for potassium‐ion hybrid capacitors
Bibliographic record
Abstract
Abstract Potassium‐ion hybrid capacitors (PIHCs) are widely regarded as highly promising energy storage devices, due to their exceptional energy density, impressive power density, and abundant potassium resources. Unfortunately, restricted by the inherent capacitive storage mechanism, the carbon cathodes possess a much lower specific capacity than battery‐type anodes. Therefore, designing high‐performance carbon cathodes is extremely urgent for the development of PIHCs. Herein, N, O co‐doped porous carbon (NOPC) was fabricated through the NaCl hard template method and combined KOH/melamine chemical activation technique, displaying the characteristics of abundant N/O content (4.7 at%/16.9 at%), ultrahigh specific surface area (3092 m 2 ·g ?1 ) and hierarchical pore network. The designed NOPC cathode delivers a high specific capacity (164.4 mAh·g ?1 at 0.05 A·g ?1 ) and superior cyclability (95.1% retention ratio at 2 A·g ?1 over 2500 cycles). Notably, the adjustable ratio of micropores to mesopores facilitates the achievement of the optimal balance between capacity and rate capability. Moreover, the pseudocapacitance can be further augmented through the incorporation of N/O functional groups. As expected, the graphite//NOPC based PIHC possesses a high energy density of 113 Wh·kg ?1 at 747 W·kg ?1 and excellent capacity retention of 84.4% after 4000 cycles at 1.0 A·g ?1 . This work introduces a novel strategy for designing carbon cathodes that enhances the electrochemical performance of PIHCs.
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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 | 0.001 | 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".