High‐Performance Reversible Aqueous Zn‐Ion Battery Based on Porous MnO<i><sub>x</sub></i> Nanorods Coated by MOF‐Derived N‐Doped Carbon
Bibliographic record
Abstract
Abstract Rechargeable aqueous zinc‐ion batteries (ZIBs) have been emerging as potential large‐scale energy storage devices due to their high energy density, low cost, high safety, and environmental friendliness. However, the commonly used cathode materials in ZIBs exhibit poor electrochemical performance, such as significant capacity fading during long‐term cycling and poor performance at high current rates, which significantly hinder the further development of ZIBs. Herein, a new and highly reversible Mn‐based cathode material with porous framework and N‐doping (MnO x @N‐C) is prepared through a metal–organic framework template strategy. Benefiting from the unique porous structure, conductive carbon network, and the synergetic effect of Zn 2+ and Mn 2+ in electrolyte, the MnO x @N‐C shows excellent cycling stability, good rate performance, and high reversibility for aqueous ZIBs. Specifically, it exhibits high capacity of 305 mAh g −1 after 600 cycles at 500 mA g −1 and maintains achievable capacity of 100 mAh g −1 at a quite high rate of 2000 mA g −1 with long‐term cycling of up to 1600 cycles, which are superior to most reported ZIB cathode materials. Furthermore, insight into the Zn‐storage mechanism in MnO x @N‐C is systematically studied and discussed via multiple analytical methods. This study opens new opportunities for designing low‐cost and high‐performance rechargeable aqueous ZIBs.
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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".