A Comparison of the Performance of Different Morphologies of LiNi<sub>0.8</sub>Mn<sub>0.1</sub>Co<sub>0.1</sub>O<sub>2</sub> Using Isothermal Microcalorimetry, Ultra-High Precision Coulometry, and Long-Term Cycling
Bibliographic record
Abstract
Ni-rich positive electrode materials for Li-ion batteries have the dual benefit of achieving high energy density while reducing the amount of Co used in cells. However, limitations in cycle life are still an issue for the widespread adoption of these materials. The benefit of using single crystal materials has been demonstrated for LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC532), LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622), and now LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811). This work uses long-term cycling, ultra-high precision coulometry (UHPC), and isothermal microcalorimetry to investigate the effect of particle morphology on the lifetime of NMC811/graphite pouch cells. NMC811 with uncoated single crystal (SC) particles, coated polycrystalline (PC) particles, and a composite “bimodal” (BM) material are studied with electrolyte systems that have shown excellent cycle life in other NMC materials. Results from this work show that SC cells have improved cycle life in long-term cycling, as well as higher coulombic efficiency (CE) and lower charge endpoint capacity slippage as seen in the UHPC measurements. This correlates well with the isothermal microcalorimetry results, in which SC cells show the lowest parasitic heat flow over a range of upper-cutoff voltages. This study suggests excellent lifetimes can be achieved in single crystal NMC811/graphite cells with further electrolyte optimization.
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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 | 0.001 |
| 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".