One Sulfonate and Three Sulfate Electrolyte Additives Studied in Graphite/LiCoO<sub>2</sub>Pouch Cells
Bibliographic record
Abstract
The effectiveness of three cyclic sulfate additives, ethylene sulfate (or 1,3,2-dioxathiolane-2,2-dioxide (DTD)), trimethylene sulfate (or 1,3,2-dioxathiane 2,2-dioxide (TMS)) and propylene sulfate (or 4-methyl-1,3,2-dioxathiolane-2,2-dioxide (PLS)) and one sulfonate additive, methylene methane disulfonate (1,5,2,4-dioxadithiane-2,2,4,4-tetraoxide (MMDS)) were studied in graphite/LiCoO 2 (LCO) pouch cells. The additives were studied singly and in combination with 2% vinylene carbonate (VC) using high precision coulometry, AC impedance spectroscopy, volume change to infer gas evolution and open circuit storage experiments. During formation, TMS can significantly reduce the irreversible capacity and gas evolution while PLS and DTD cannot. When added alone, cells with DTD, TMS or PLS perform much worse than cells with 2% VC in all of coulombic efficiency, charge endpoint capacity slippage and voltage drop during storage. When combined with 2% VC, cells with TMS and VC provide similar performance to cells with 2% VC alone, cells with VC + MMDS are better and cells with VC + DTD and VC + PLS are worse. The trends found here are different from the results obtained using the same additives in graphite/Li[Ni 1/3 Mn 1/3 Co 1/3 ]O 2 (NMC) pouch cells where cells with VC + DTD and VC + MMDS were found to provide benefit compared to VC alone. It is hard to distinguish any difference between 2% VC and 2%VC + 1% TMS in both graphite/LCO cells and graphite/NMC cells, suggesting that TMS is nearly "inert" in the presence of VC in these 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".