Long-Term Cycling and Mechanisms of Cell Degradation of Single Crystal LiNi<sub>0.95</sub>Mn<sub>0.04</sub>Co<sub>0.01</sub>O<sub>2</sub>/Graphite Cells
Bibliographic record
Abstract
Extremely high nickel content positive electrode materials have high specific capacity leading to high energy density Li-ion cells. The long-term cycling stability of pouch cells with a single crystal LiNi0.95Mn0.04Co0.01O2 positive electrode material was studied here. Cells with such high nickel content demonstrated excellent cycling when only charged to 4.04 V (about 75% state of charge (SOC)), while they showed more capacity loss when charged to 4.18 V or 100% SOC. Lowering the upper cut-off voltage is in favor of the cycling stability however decreases the cell energy density. The main reason for the capacity loss at 40 °C is due to positive electrode impedance growth, which originated from parasitic reactions between the positive electrode material and the electrolyte, especially when the cells are operated to 4.18 V. There was no noticeable positive electrode particle cracking by scanning electron microscopy and no significant active mass loss even for cells operated to 4.18 V. XRD of cycled positive electrodes indicated no appreciable amount of nickel migrating into the lithium layer, so the impedance growth mainly comes from the positive electrode particle surfaces. Using 1.2 M LiPF6 fluoroethylene carbonate: ethyl methyl carbonate 20:80 electrolyte with 1 wt% lithium difluorophosphate allows cycle life to be extended by reducing impedance growth of the cells.
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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 | 0.001 | 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".