Flash Joule Heating Synthesis of Layer-Stacked Vanadium Oxide/Graphene Hybrids within Seconds for High-Performance Aqueous Zinc-Ion Batteries
Bibliographic record
Abstract
Vanadium oxides have been regarded as highly promising cathodes for aqueous zinc-ion batteries (ZIBs). However, obtaining high-performance vanadium oxide-based cathodes suitable for industrial application remains a significant challenge due to the need for cost-effective, straightforward, and efficient preparation methods. Herein, we present a facile and rapid synthesis of a composite cathode, consisting of layer-stacked VO 2 /V 2 O 5 and graphene-like carbon nanosheets, in just 2.5 s by treating the commercial V 2 O 5 powder via a flash Joule heating strategy. When employed as the cathode for ZIBs, the resulting composite delivers a comparable rate capacity of 459 mA h g –1 at 0.2 A g –1 and remarkable cycle stabilities of 355.5 mA h g –1 after 2500 cycles at 1.0 A g –1 and 169.5 mA h g –1 after 10,000 cycles at 10 A g –1, respectively. Further electrochemical analysis reveals that the impressive performance is attributed to the accelerated charge transfer and the alleviated structure degradation, facilitated by the abundant sites and a built-in electric field of the layer-stacked VO 2 /V 2 O 5 heterostructure, as well as the excellent conductivity of graphene-like carbon nanosheets. This work introduces a unique approach for ultrafast and low-cost fabrication of high-performance vanadium oxide-based composite cathodes toward efficient ZIBs.
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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".