Design of Layer Structure Metal Oxide Material with Dual-Ion Defects for High-Performance Aqueous Zn Ion Batteries
Bibliographic record
Abstract
Layer structure metal oxides are promising energy storage materials for rechargeable batteries. However, they are still hindered by insufficient ion storage sites and sluggish ion diffusion kinetics during ion insertion/extraction, leading to unsatisfactory battery performance. Herein, we have successfully designed layer structure metal oxides with regulated dual-ion defects via the ion exchange and annealing processes. As for demonstration, a K iv TO ev @Ti anode with dual-ion defects by incorporating with the interlaminar K vacancies and layer edge O vacancies in layer structure potassium titanate (KTO) was synthesized for Zn ion batteries. The bionic defects in the K iv TO ev @Ti anode are indicated to provide extra space for potent Zn ion storage and enhance the Zn ion diffusion rate. Complete inner layer structure and residual interlayer K ion pillars ensure that the K iv TO ev @Ti anode has highly structural stability and reversible electrochemistry. Therefore, K iv TO ev @Ti delivers a favorable Zn ion storage capability of 179.2 mAh g – 1 at 0.05 A g –1, and a remarkable cycling stability of 82% capacity retention after 5000 cycles at 0.5 A g –1 . The Zn x MnO 2 //K iv TO ev @Ti full cell presents an excellent power/energy density of 583.5 W kg –1 /97.8 Wh kg –1, respectively, and maintains a capacity retention of 90% after 5000 cycles. This work can enlighten material engineering for energy storage area.
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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".