Combinations of LiPO<sub>2</sub>F<sub>2</sub>and Other Electrolyte Additives in Li[Ni<sub>0.5</sub>Mn<sub>0.3</sub>Co<sub>0.2</sub>]O<sub>2</sub>/Graphite Pouch Cells
Bibliographic record
Abstract
Lithium difluorophosphate (LiPO 2 F 2 ) is a useful electrolyte additive for Li-ion cells.In this study, combinations of several selected additives and LiPO 2 F 2 were explored in Li[Ni 0.5 Mn 0.3 Co 0.2 ]O 2 (NMC532)/graphite pouch cells using high temperature storage testing (60 • C), ultra-high precision coulometry (UHPC), electrochemical impedance spectroscopy (EIS), isothermal microcalorimetry and long term charge-discharge cycling.Several useful additives to use in combination with LiPO 2 F 2 , were found, especially vinylene carbonate (VC), difluoroethylene carbonate (DiFEC) and fluoroethylene carbonate (FEC).These electrolyte additive combinations (e.g.LiPO 2 F 2 + FEC, LiPO 2 F 2 + VC, LiPO 2 F 2 + DiFEC) are effective in NMC532/graphite pouch cells since they were found to improve coulombic efficiency, extend charge-discharge cycle lifetime, decrease parasitic heat flow and control impedance growth.The compositions of the solid electrolyte interphases (SEI) on both positive and negative electrodes were enhanced in fluorine and phosphorous when LiPO 2 F 2 was used and the fluorine content was further enhanced when FEC or VC were included along with LiPO 2 F 2 .The oxygen content of the negative electrode SEI diminished when LiPO 2 F 2 was used and further reduced when FEC or VC were included.These changes to the SEI layers may explain why the combination of LiPO 2 F 2 with other additives can improve the lifetime and performance of Li-ion cells.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".