Using MXene as a Chemically Induced Initiator to Construct High‐Performance Cathodes for Aqueous Zinc‐Ion Batteries
Bibliographic record
Abstract
MXene usually exhibits weak pseudo-capacitance behavior in aqueous zinc-ion batteries, which cannot provide sufficient reversible capacity, resulting in the decline of overall capacity when used as the cathode materials. Taking inspiration from polymer electrolyte engineering, we have conceptualized an in situ induced growth strategy based on MXene materials. Herein, 5.25 % MXene was introduced into the nucleation and growth process of vanadium oxide (HVO), providing the heterogeneous nucleation site and serving as an initiator to regulate the morphology and structural of vanadium oxide (T-HVO). The resulted materials can significantly improve the capacity and rate performance of zinc-ion batteries. The growth mechanism of T-HVO was demonstrated by both characterizations and DFT simulations, and the improved performance was systematically investigated through a series of in situ experiments related to dynamic analysis steps. Finally, the evaluation and comparison of various defect introduction strategies revealed the efficient, safety, and high production output characteristics of the in situ induced growth strategy. This work proposes the concept of in situ induced growth strategy and discloses the induced chemical mechanism of MXene materials, which will aid the understanding, development, and application of cathode in aqueous zinc-ion batteries.
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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 | 0.001 |
| 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".