Zinc-spray-coated carbon fibres as lean anodes for low-cost zinc-ion batteries
Bibliographic record
Abstract
While moving towards sustainable Zn-ion batteries (ZIBs), it is crucial to research not only on their critical issues, such as dendrite growth, hydrogen evolution reaction (HER), and corrosion, but also on the sustainable utilization of spent materials. Lean anodes, which utilize reduced amounts of zinc, are pivotal in this context; however, the batteries reported in literature have yet to achieve superior performance levels. In this study we present a simple cost-effective approach of zinc spray coating onto carbon fibre substrate (SCZn) as a simplified lean anode production strategy while enhancing the performance of Zn-ion batteries. This SCZn anode demonstrates remarkable cyclability over 1000 cycles at 0.5 mA cm -2 with a 0.5 mAh cm -2 capacity, and 10 times the anodic depth of discharge (DOD) when compared to a standard Zn foil anode (FZn). Furthermore, the micro-scaled Zn particles embedded in the 3D structure of the carbon fibres effectively supress dendrite growth and enhance the charge transfer kinetics, as evidenced by lower polarization, less corrosion and favourable impedance. Full cell studies were carried out by pairing the SCZn anode with a cost-effective commercial V 2 O 5 -based cathode. The latter shows impressive performance with a 370 mAh g -1 specific capacity at 0.1 A g -1 , and 270 mAh g -1 with a capacity retention of 95% over 760 cycles under current density of 1 A g -1 . The proposed anode preparation method is highly scalable, cost-effective and ultimately simple. This paves the way for long-life, efficient, and durable Zn-ion batteries, offering an opportunity to engineer sustainable solutions for future grid scale 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".