Substituting Na for Excess Li in Li<sub>1+x</sub>(Ni<sub>0.6</sub>Mn<sub>0.4</sub>)<sub>1−x</sub>O<sub>2</sub> Materials
Bibliographic record
Abstract
NMC640, a series of Li1+x(Ni0.6Mn0.4)1−xO2 materials, are important Co-free mid-Ni cathode materials for Li-ion batteries, offering high energy density and better cost-efficiency than Ni-rich counterparts. These materials require excess Li compared to stoichiometric composition to improve the electrochemical performance in terms of rate capability and cycling stability. Although lithium-to-transition metal ratios up to 1.15 can be used to optimize the performance, less than 80% of this lithium is electrochemically active during cycling up to a 4.4 V upper cut off. This study explores whether some percentage of the inactive Li can be replaced by sodium to make these materials more cost-effective and bring potential improvements in electrochemical performance. Various amounts of excess Li were substituted by sodium in the structure. The results show that sodium can be integrated into the layered oxide structure without forming any impurity phases and effectively decreases the cation mixing observed in these layered structures. However, this does compromise cycling stability and rate capability. Na tends to occupy Li sites rather than transition metal sites, resulting in electrochemical instability and capacity loss. Even though excess Li is not electrochemically active, it cannot be effectively replaced by sodium without compromising battery performance of Li1+x(Ni0.6Mn0.4)1−xO2 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".