A Study of Vinylene Carbonate and Prop-1-ene-1,3 Sultone Electrolyte Additives Using Polycrystalline Li[Ni<sub>0.6</sub>Mn<sub>0.2</sub>Co<sub>0.2</sub>]O<sub>2</sub> in Positive/Positive Symmetric Cells
Bibliographic record
Abstract
Vinylene carbonate (VC) and prop-1-ene-1,3 sultone (PES) have been reported to have beneficial effects on both negative and positive electrodes of Li-ion cells. To simplify evaluation of these two additives with LiNi0.6Mn0.2Co0.2O2 (NMC622), positive/positive symmetric cells were used to exclude the influence from any negative electrode. The compatibility of electrolytes containing 2%VC or 2%PES with NMC622 in 1M LiPF6 EC:DEC (v/v 1:2) electrolyte was evaluated by multiple voltage hold periods at 55 °C to accelerate the capacity fade. EIS spectra showed 2%PES is superior for impedance control compared to 2%VC. dV/dQ vs V fitting results showed that active mass loss is worse at high voltage than low voltage regardless of electrolyte used. Cross-sectional SEM images showed more microcracking of NMC622 particles at high voltage than at low voltage, which was more severe at the end of testing than at the beginning of testing. Symmetric cell storage at 0 V (ca. 3.8 V vs Li/Li+) and 0 °C showed a significant increase in cell impedance for cells stored after the end of testing and a moderate impedance increase for cells stored at the beginning of testing suggesting the accumulation of deleterious reaction products in the cells during testing.
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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 | 0.001 | 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".