Design High‐Entropy Core‐Shell Nickel‐Rich Cobalt‐Free Cathode Material Toward High Performance Lithium Batteries
Bibliographic record
Abstract
Abstract The structural instability of lithium‐based transition metal layered oxides during electrochemical cycling‐exacerbated by phenomena such as metal dissolution and phase transitions‐induces rapid capacity degradation, thus constraining their applicability in high‐energy‐density lithium batteries. While coating these materials can bolster stability, the employment of electrochemically inactive coatings may inadvertently undermine energy storage performance, presenting a significant trade‐off. In response to this challenge, an innovative core‐shell cathode architecture is presented, wherein high entropy doped LiNi 1/6 Mn 1/6 Al 1/6 Ti 1/6 Mo 1/6 Ta 1/6 O 2 serves as the shell and nickel‐rich cobalt‐free LiNi 0.89 Mn 0.11 O 2 constitutes the core, synthesized through a simple two‐step co‐precipitation methodology (designated as LHECNM). This high‐entropy shell preserves the core's electrochemical performance while effectively mitigating phase transformations and transition metal ion dissolution, thereby enhancing structural robustness. Moreover, the core‐shell configuration significantly diminishes the energy barrier for Li + diffusion, facilitating superior ion transport dynamics. Consequently, LHECNM demonstrates remarkable electrochemical performance, achieving a discharge capacity of 201.57 mAh g −1 , a commendable rate capability up to 5C, and an impressive 92% capacity retention over prolonged cycling. This investigation elucidates a promising paradigm for the design of high‐entropy cathode materials, offering profound insights for the advancement of future energy storage technologies.
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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 | 0.001 |
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".