Organic cation‐supported layered vanadate cathode for high‐performance aqueous zinc‐ion batteries
Bibliographic record
Abstract
Abstract Layered vanadates are ideal energy storage materials due to their multielectron redox reactions and excellent cation storage capacity. However, their practical application still faces challenges, such as slow reaction kinetics and poor structural stability. In this study, we synthesized [Me2NH2]V3O7 (MNVO), a layered vanadate with expended layer spacing and enhanced pH resistance, using a one‐step simple hydrothermal gram‐scale method. Experimental analyses and density functional theory (DFT) calculations revealed supportive ionic and hydrogen bonding interactions between the thin‐layered [Me2NH2]+ cation and [V3O7]− anion layers, clarifying the energy storage mechanism of the H+/Zn2+ co‐insertion. The synergistic effect of these bonds and oxygen vacancies increased the electronic conductivity and significantly reduced the diffusion energy barrier of the insertion ions, thereby improving the rate capability of the material. In an acidic electrolyte, aqueous zinc‐ion batteries employing MNVO as the cathode exhibited a high specific capacity of 433 mAh g−1 at 0.1 A g−1. The prepared electrodes exhibited a maximum specific capacity of 237 mAh g−1 at 5 A g−1 and maintained a capacity retention of 83.5% after 10,000 cycles. This work introduces a novel approach for advancing layered cathodes, paving the way for their practical application in energy storage devices.
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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".