Single‐Crystalline Ni‐Rich LiNi<i><sub>x</sub></i>Mn<i><sub>y</sub></i>Co<sub>1−</sub><i><sub>x</sub></i><sub>−</sub><i><sub>y</sub></i>O<sub>2</sub> Cathode Materials: A Perspective
Bibliographic record
Abstract
Abstract To drive electrical vehicles for long‐range, the energy density of Li‐ion batteries must be further enhanced, which requires high‐energy cathode materials. Among them, Ni‐rich LiNi x Mn y Co 1− x – y O 2 ( x > 0.5, NMC) is one of the most promising candidates. However, traditional poly‐crystal (PC) NMC materials, whose particles are secondary clusters consisting of many primary crystalline particles, are susceptible to pulverization along the inter grain/particle boundaries, resulting in poor cycle stability. Recently, single crystal (SC)‐NMC cathodes are proposed as they are believed to have several merits such as high structural integrity, lower interface with electrolyte, and potentially better energy storage performance. Nevertheless, several queries, such as the merits of SC structure and the mechanism for their structure degradation, are still controversial and required to be addressed. Herein, the synthesis parameters, properties, energy storage as well as safety of SC‐ and PC‐NMC cathodes with various Ni contents are critically reviewed, for clarifying the merits and drawbacks of SC. Furthermore, the study focuses on the recent advances of insight mechanisms and strategies for stabilizing the structure/interface by doping, morphology engineering, surface coating, and composition tailoring. The remaining challenges, safety concerns, and perspectives to enhance the electrochemical performance of SC‐NMC cathodes for electric vehicle application are followed.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.011 | 0.013 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".