Boosting Ni‐Rich Cathode Stability Through Grain Boundary Strengthening and Phase Transition Degradation Suppression
Bibliographic record
Abstract
Abstract Nickel‐rich layered cathodes are promising for high‐energy‐density lithium‐ion batteries but suffer from rapid capacity fading, primarily due to intergranular cracking and structural degradation during the H2‐H3 phase transition, especially under high voltage. To address these challenges, a novel Ta5+/Ti4+ co‐doping strategy has been introduced that simultaneously stabilizes grain boundaries and enhances the mechanical strength of the cathode. The dopants effectively mitigate intergranular cracking and form a pre‐cation‐mixing layer, stabilizing the layered structure during deep delithiation and preventing structural collapse. Moreover, this co‐doping approach also improves the reversibility of the H2‐H3 phase transition and reduces lattice distortions, thereby enhancing cycling stability. As a result, the co‐doped cathode exhibits excellent capacity retention of 96.66% after 150 cycles at 1 C in liquid electrolyte. In solid‐state batteries, it demonstrates superior interfacial compatibility with significantly reduced side reactions with the solid electrolyte, achieving a high initial capacity of 181.4 mAh g−1 and retaining 89.3% of its capacity after 100 cycles. This marks a significant improvement over the pristine cathode. These results highlight the effectiveness of Ta5+/Ti4+ co‐doping as a pratical strategy for developing high‐performance nickel‐rich cathodes for next‐generation lithium‐ion batteries.
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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".