Controllable morphological transformations of nickel metal–organic frameworks for nickel–zinc batteries
Bibliographic record
Abstract
Abstract Constructing hierarchical nanostructures with highly exposed surfaces is a promising strategy for developing advanced cathode materials in aqueous batteries. Herein, we employed a competitive coordination strategy to optimize the characteristics of nickel metal–organic framework (Ni‐MOF). Specifically, the acetate ions were employed as precise regulators, exerting a distinct influence on the morphology of the Ni‐MOF and leading to a structural transition from a block structure to a two‐dimensional (2D) layered structure. The optimized Ni‐MOF exhibits a unique superstructure composed of hierarchical 2D layers assembled into flower‐like architectures. This distinctive superstructure increases the electrochemically active surface area of Ni‐MOF (N‐2) and provides abundant pathways for electron/ion transfer, thereby facilitating efficient electrochemical reactions. Remarkably, the assembled aqueous alkaline N‐2//Zn battery demonstrated enhanced specific capacity (0.446 mAh·cm −2 at 1 mA·cm −2 ) and excellent maximum energy/power density (0.789 mWh·cm −2 /17.262 mW·cm −2 ). This work not only offers valuable insights into regulating MOF morphology, but also makes a contribution toward enhancing the application potential of MOFs in aqueous batteries.
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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".