Large‐scale doping‐engineering enables boron/nitrogen dual‐doped porous carbon for high‐performance zinc ion capacitors
Bibliographic record
Abstract
Abstract Zinc ion capacitors (ZICs) have drawn increasing interest in energy storage devices because of their economic benefits, high safety, and long cycling life. Nevertheless, the lack of high‐performance cathodes for ZICs remains a key challenge. Here, we fabricated B, N co‐doped porous carbon (BN‐C) via a salt template strategy. The aqueous ZICs assembled from BN‐C cathode delivered a high capacity of 190.2 mAh·g −1 and a remarkable energy density of 105.1 Wh·kg −1 . Moreover, systematic characterization verifies that B/N dual‐doping promotes the physical adsorption/desorption kinetics of anion and the chemical absorption/desorption kinetics of Zn 2+ , thus improving the electrochemical performance of ZICs. In addition, the quasi‐solid‐state pouch‐type battery exhibited excellent electrochemical durability and mechanical flexibility, demonstrating its vast application potential as a flexible power source. Overall, this research not only presents a reasonable approach to the large‐scale production of carbon cathode materials with excellent electrochemical performance but also strengthens the essential recognition of the charge storage mechanism of heteroatoms‐doped carbon materials.
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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".