Impact of Electrolyte Additive Content on the Lifetime of LiNi<sub>0.5</sub>Mn<sub>0.3</sub>Co<sub>0.2</sub>O<sub>2</sub>/Artificial and Natural Graphite Cells
Bibliographic record
Abstract
The electrolyte comprised of 2 wt% prop-1-ene-1,3 sultone (PES) + 1 wt% tris (trimethylsilyl) phosphite (TTSPi) + 1 wt % ethylene sulfate (DTD) in 1 M LiPF 6 in ethylene carbonate: ethyl methyl carbonate (3:7 by weight), called PES211 here, has been reported to function well in Li(Ni x Mn y Co z )O 2 /graphite cells operated up to 4.4 V. However, the use of PES211 electrolyte usually leads to unwanted lithium plating at room temperature during high rate charging as a result of large negative electrode charge transfer resistance. This problem can be mitigated by decreasing the fraction, x (0 < x < 1), of the additives used in PES211 (xPES211) in the electrolyte. This work studied the impacts on the cell lifetime when x in xPES211 is decreased in NMC532/artificial graphite (AG) and NMC532/natural graphite (NG) pouch cells. Ultra-high precision coulometry measurements and isothermal microcalorimetry tests were used to make comparative studies. All the diagnostic measurements suggested that cells with lower fractions of x in xPES211 would have a shorter lifetime. Long term cycling tests confirmed that the capacity retention decreased with a decreasing fraction of xPES211 in both NMC532/AG and NMC532/NG cells. Differences between artificial and natural graphite negative electrode materials are also discussed.
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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 | 0.000 | 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".