Cycling Behavior of NCM523/Graphite Lithium-Ion Cells in the 3–4.4 V Range: Diagnostic Studies of Full Cells and Harvested Electrodes
Bibliographic record
Abstract
Energy density of full cells containing layered-oxide positive electrodes can be increased by raising the upper cutoff voltage above the present 4.2 V limit. In this article we examine aging behavior of cells, containing LiNi 0.5 Co 0.2 Mn 0.3 O 2 (NCM523)-based positive and graphite-based negative electrodes, which underwent up to ∼400 cycles in the 3–4.4 V range. Electrochemistry results from electrodes harvested from the cycled cells were obtained to identify causes of cell performance loss; these results were complemented with data from X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectroscopy (SIMS) measurements. Our experiments indicate that the full cell capacity fade increases linearly with cycle number and results from irreversible lithium loss in the negative electrode solid electrolyte interphase (SEI) layer. The accompanying electrode potential shift reduces utilization of active material in both electrodes and causes the positive electrode to cycle at higher states-of-charge. Full cell impedance rise on aging arises primarily at the positive electrode and results mainly from changes at the electrode-electrolyte interface; the small growth in negative electrode impedance reflects changes in the SEI layer. Our results indicate that cell performance loss could be mitigated by modifying the electrode-electrolyte interfaces through use of appropriate electrode coatings and/or electrolyte additives.
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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 | 0.001 |
| 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) | 0.002 | 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".