The Influence of Nickel and Alkali Metal Additives on Manganese Dioxide Structure and Performance for Rechargeable Zinc-Ion Batteries
Bibliographic record
Abstract
Aqueous rechargeable zinc-ion batteries (ZIBs) are a promising addition to the energy storage landscape, particularly supporting the decarbonization of the power sector (i.e., deployment of wind and solar) as a low-cost, high-safety alternative to lithium-ion batteries. Unfortunately, wide-scale ZIB utilization is hindered in part by limited energy storage capacity and operational durability of state-of-the-art Mn oxide cathode materials. As such, we take a combined approach to investigate the impact of simultaneous inclusion of alkali metal (Li, Na, K, Rb, and Cs) and Ni additives on the structural and electrochemical properties of Mn oxide-based cathode materials for ZIBs. We used a facile, scalable synthesis approach to prepare 15 unique Mn oxide-based cathode materials and identified several materials capable of delivering a practical rate (i.e., C/10) discharge capacity of >150 mAh g –1 and a fast charge (i.e., 1C) capacity retention of >90% after 200 cycles. Detailed characterization of the prepared materials revealed the use of alkali metal additives during synthesis impacted both phase structure and electrochemical performance. Modifying the phase structure of Mn oxides resulted in an elevated Zn 2+ diffusion coefficient for K-containing materials and a subsequent increase in deliverable capacity. Furthermore, the incorporation of Ni in the structure was shown to have no meaningful contribution to improved capacity within the aqueous electrolyte stability window, although a slight uptick in capacity retention was observed for all prepared materials as Ni content was increased. Considering the narrow range of reported cathodes for ZIBs, this work provides insight on the important interplay between structure, composition, and improved performance of Mn oxide-based cathodes, helping accelerate future material development efforts necessary for ZIB commercialization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".