Elucidating the Role of Excess Li in the Electrochemical Performance of Li<sub>1+x</sub>[Ni<sub>0.5</sub>Mn<sub>0.5</sub>]<sub>1−x</sub>O<sub>2</sub> Layered Oxides
Bibliographic record
Abstract
Layered cathode materials comprising of Ni and Mn can possess comparable theoretical capacities to Ni-rich cathode materials. However, to draw upon this capacity, they need to overcome rate capability issues and operate to higher voltages. Incorporating excess Li during synthesis can allow much of this capacity to be accessed. This work compares the effects of excess Li on electrochemical properties of Li1+x[Ni0.5Mn0.5]1-xO2 layered oxides in the conventional voltage window as well as with higher upper cut-off voltages. Materials with different amounts of excess Li were systematically compared based on specific capacity, first-cycle irreversible loss, cycling stability, and rate capability in the voltage ranges of 3.0 V–4.3 V, 3.0 V–4.5 V, and 3.0 V–4.8 V. In all samples, excess Li improves the rate capability and cycling stability in all these voltage ranges while significant gains in specific capacity can only be attained when operating these materials at higher voltage cut-offs. The improved rate capability performance in presence of excess Li can be attributed to enhanced electronic conductivity and Li+ ion diffusion arising from reduced amounts of Ni in the Li layer.
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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".