Constructing Nanoparticle-Integrated CoWO <sub>4</sub> Microflakes as a Promising Cathode Material for Aqueous Zinc-Ion Batteries
Bibliographic record
Abstract
Exploring a satisfactory cathode material for aqueous zinc-ion batteries (AZIBs) represents a critical advancement toward sustainable, safe, and cost-effective energy storage solutions. In this study, cobalt tungstate (CoWO 4 ) hierarchical microflakes are successfully fabricated via a facile, scalable chemical precipitation-calcination strategy with the aid of hydrazine. The structural evolution is systematically investigated through calcination at 400–700 °C. When assembled by CoWO 4 /Zn batteries, a redox couple at approximately 1.4/1.7 V appears on the cyclic voltammetry profiles. The hierarchical microflakes demonstrate impressive zinc-ion storage performance compared to their microrod counterparts. Specifically, the optimized architecture delivers enhanced initial discharge capacity (177.6 mAh g –1 ), exceptional cycling stability (92.9% capacity retention with reference to the 50th after 1000 cycles) at 0.1 A g –1, improved rate capability (73.7 mAh g –1 at 2 A g –1 ), and a faster zinc-ion diffusion coefficient (9.85 × 10 –13 cm 2 s –1 ). This is attributed to the enlarged active surface area and optimized ion transport pathways. This study not only presents the first demonstration of Zn/CoWO 4 half-cell performance in an aqueous electrolyte but also establishes a viable strategy for designing hierarchical transition-metal tungstate architectures for advanced energy storage applications.
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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".