Structure and Charge Regulation Strategy Enabling Superior Cyclability for Ni‐Rich Layered Cathode Materials
Bibliographic record
Abstract
Abstract Ni‐rich layered oxides are significantly promising cathode materials for commercial high‐energy‐density lithium‐ion batteries. However, their major bottlenecks limiting their widespread applications are capacity fading and safety concerns caused by their inherently unstable crystal structure and highly reactive surface. Herein, surface structure and bulk charge regulation are concurrently achieved by introducing high‐valence Ta5+ ions in Ni‐rich cathodes, which exhibit superior electrochemical properties and thermal stability, especially a remarkable cyclic stability with a capacity retention of 80% for up to 768 cycles at a 1C rate versus Li/Li+. Due to the partial Ta enrichment on surface, the regulated surface enables high reversibility of Li+ insertion/extraction by preventing surface Ni reduction in deep charging. Moreover, bulk charge regulation that boosts charge density and its localization on oxygen remarkably suppresses microcracks and oxygen loss, which in turn prevents the fragmentation of the regulated surface and structural degradation associated with oxygen skeleton. This study highlights the significance of an integrated optimization strategy for Ni‐rich cathodes and, as a case study, provides a novel and deep insights into the underlying mechanisms of high‐valence ions substitution of Ni‐rich layered cathodes.
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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".