Na-doped Ni-rich LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub>cathode material with both high rate capability and high tap density for lithium ion batteries
Bibliographic record
Abstract
Na-doped Ni-rich LiNi0.5Co0.2Mn0.3O2 cathode material, Li0.97Na0.03Ni0.5Co0.2Mn0.3O2, is synthesized by a hydroxide co-precipitation route. The structural characterization reveals that the substitution of Na for Li results in a more ordered α-NaFeO2 structure, enlarges Li layer spacing, and reduces the degree of cation mixing. The Li0.97Na0.03Ni0.5Co0.2Mn0.3O2 material has a high tap density of 2.17 g cm(-3) that meets the commercial requirement in lithium ion batteries (LIBs). The galvanostatic charge/discharge results show that the electrochemical performance of the Li0.97Na0.03Ni0.5Co0.2Mn0.3O2 is significantly improved. At 0.2, 1, 10, 30 and 50 C, the specific capacities of the Li0.97Na0.03Ni0.5Co0.2Mn0.3O2 are 228.43, 163.12, 121.43, 95.56 and 60.09 mA h g(-1), respectively, which are superior to those of the undoped LiNi0.5Co0.2Mn0.3O2 due to the enlargement of Li layer spacing, the decreased degree of cation mixing, and the rapid diffusion of Li-ion in the bulk lattice after the substitution of Na for Li. Therefore, the Na-doped Ni-rich LiNi0.5Co0.2Mn0.3O2 material is a promising cathode candidate for the next generation of LIBs.
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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".