Special Synergy between Electrolyte Additives and Positive Electrode Surface Coating to Enhance the Performance of Li[Ni<sub>0.6</sub>Mn<sub>0.2</sub>Co<sub>0.2</sub>]O<sub>2</sub>/Graphite Cells
Bibliographic record
Abstract
Li[Ni 0.6 Mn 0.2 Co 0.2 ]O 2 (NMC622) is one of the most attractive positive electrode materials for Li-ion cells. Ultra high precision coulometry, impedance spectroscopy, gas evolution and long-term cycling were used to explore the combined impact of several electrolyte additives and an Al 2 O 3 surface coating on NMC622/graphite cells tested to 4.4 V at 40°C. An excellent correlation between the short-term coulombic efficiency measurements and the long-term cycling results was noted. An electrolyte containing 1 M LiPF 6 in ethylene carbonate: ethyl methyl carbonate (3:7 by weight) with additives 2 wt% prop-1-ene-1,3 sultone (PES) + 1 wt% methylene methane disulfonate (MMDS) + 1 wt% tris (trimethylsilyl) phosphite (TTSPi) (this electrolyte is called PES 211) was found to give the best performance in both coated and uncoated NMC622/graphite cells. The surface coating was found to improve coulombic efficiency, reduce impedance and improve capacity retention in all cases studied. However, uncoated NMC622/graphite cells with PES211 were found to perform better than coated NMC622/graphite cells which used any other electrolyte. The special synergy between Al 2 O 3 -coated NMC622 and PES211 needs to be understood so that even better coatings and electrolyte combinations can be developed.
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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) | 0.001 | 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".