Radially Oriented Single‐Crystal Primary Nanosheets Enable Ultrahigh Rate and Cycling Properties of LiNi<sub>0.8</sub>Co<sub>0.1</sub>Mn<sub>0.1</sub>O<sub>2</sub> Cathode Material for Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Ni‐rich Li[Ni x Co y Mn 1− x − y ]O 2 ( x ≥ 0.8) layered oxides are the most promising cathode materials for lithium‐ion batteries due to their high reversible capacity of over 200 mAh g −1 . Unfortunately, the anisotropic properties associated with the α‐NaFeO 2 structured crystal grains result in poor rate capability and insufficient cycle life. To address these issues, a micrometer‐sized Ni‐rich LiNi 0.8 Co 0.1 Mn 0.1 O 2 secondary cathode material consisting of radially aligned single‐crystal primary particles is proposed and synthesized. Concomitant with this unique crystallographic texture, all the exposed surfaces are active {010} facets, and 3D Li + ion diffusion channels penetrate straightforwardly from surface to center, remarkably improving the Li + diffusion coefficient. Moreover, coordinated charge–discharge volume change upon cycling is achieved by the consistent crystal orientation, significantly alleviating the volume‐change‐induced intergrain stress. Accordingly, this material delivers superior reversible capacity (203.4 mAh g −1 at 3.0–4.3 V) and rate capability (152.7 mAh g −1 at a current density of 1000 mA g −1 ). Further, this structure demonstrates excellent cycling stability without any degradation after 300 cycles. The anisotropic morphology modulation provides a simple, efficient, and scalable way to boost the performance and applicability of Ni‐rich layered oxide cathode materials.
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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".