Some Fluorinated Carbonates as Electrolyte Additives for Li(Ni<sub>0.4</sub>Mn<sub>0.4</sub>Co<sub>0.2</sub>)O<sub>2</sub>/Graphite Pouch Cells
Bibliographic record
Abstract
The effects of four fluorinated carbonates including fluoroethylene carbonate, difluoroethylene carbonate, bis(2,2,2-trifluoroethyl) carbonate and 2,2,3,4,4,4-hexafluorobutyl methyl carbonate as electrolyte additives were studied in Li(Ni 0.4 Mn 0.4 Co 0.2 )O 2 /graphite pouch cells using ultra-high precision coulometry, in situ measurements of gas evolution, gas chromatography, electrochemical impedance spectroscopy, and long-term cycling experiments. The differential capacity vs. voltage curves during formation showed that fluoroethylene carbonate and difluoroethylene carbonate are solid electrolyte interphase (SEI) forming additives, while bis(2,2,2-trifluoroethyl) carbonate and 2,2,3,4,4,4-hexafluorobutyl methyl carbonate do not alter the SEI on the negative electrode during formation. Cells containing difluoroethylene carbonate have the highest coulombic efficiency and lowest charge end point capacity slippage rate at both 4.2 and 4.4 V. However the performance was not as good as that of cells containing a state of the art additive blend. Long-term cycle-hold-rest tests at 55°C showed that at 4.4 V all cells with fluorinated additives that gave promising capacity retention generated unacceptable quantities of gas. Only difluoroethylene carbonate during cycling tests to 4.2 V at 55°C provided promising capacity retention and moderate gas generation. These results suggest that the use of only these fluorinated additives in Li(Ni 0.4 Mn 0.4 Co 0.2 )O 2 /graphite pouch cells with ethylene carbonate-based electrolytes is not competitive to alternative approaches.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".