A Study of Li-Ion Cells Operated to 4.5 V and at 55°C
Bibliographic record
Abstract
The long-term cycling behavior of 24 promising electrolyte blends were systematically studied in LaPO 4 -coated Li(Ni 0.4 Mn 0.4 Co 0.2 )O 2 /graphite pouch type Li-ion cells tested to 4.5 V at 55°C. Capacity fade during cycling, charge-transfer resistance (R ct ) before and after cycling as well as gas evolution during formation and also during cycling were examined and compared head-to-head. Of all the electrolytes tested, triallyl phosphate containing electrolytes including 2% vinylene carbonate + 2% triallyl phosphate in 1 M LiPF 6 sulfolane:ethyl methyl carbonate and 2% prop-1-ene sultone + 2% triallyl phosphate in 1M LiPF 6 ethylene carbonate:ethyl methyl carbonate electrolytes showed the best capacity retention, the least impedance growth and manageable amounts of gas evolution during long-term cycling. Pyridine boron trifluoride -based additives also showed excellent cycling performance but cells with those additives had higher gas evolution during cycling. Cells containing fluorinated electrolytes had similar cycling performance to cells containing 2% prop-1-ene sultone in ethylene carbonate:ethyl methyl carbonate electrolyte but with much more gas evolution and higher impedance after long-term cycling. The results suggest that electrolytes containing the additives triallyl phosphate or pyridine boron trifluoride may be promising for high voltage Li-ion cells at elevated temperature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".