Gradient Porous Carbon Superstructures for High‐efficiency Charge Storage Kinetics
Bibliographic record
Abstract
Abstract Zinc‐ion hybrid supercapacitors (ZHSCs) are emerging for high‐efficiency energy storage. However, single‐layer cathode materials often suffer from low‐charge storage kinetics. Herein, an innovative gradient porous carbon superstructure with enhanced charge storage kinetics is developed, achieved by designing a concentration gradient carbon superstructure to facilitate rapid, directional ion transport and efficient ion storage. The gradient pore design with optimized micropore sizes (0.88 to 0.96 nm) and mesopore size (≈4 nm) enhances the hydrated zinc ion ([Zn(H2O)6]2+) diffusion, facilitating efficient desolvation and Zn2+ ion storage. Furthermore, N/O co‐doping provides pseudo‐capacitance by lowering the energy barrier for C‐O‐Zn bond formation, increasing the defect density and conductivity of the carbon material. Further graphitization improves conductivity and wettability, while a high specific surface area (SSA) offers abundant active sites. ZHSCs fabricated with this gradient porous carbon superstructure exhibit a remarkably high energy density of 101.8 Wh kg−1 at a substantial power density of 503.6 W kg−1, outperforming the reported benchmark materials. The exceptional charge–discharge cycling stability is also demonstrated over 10 000 cycles. This work presents an effective strategy for enhancing charge storage kinetics in supercapacitors.
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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) | 0.001 | 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".