A Guide to Ethylene Carbonate-Free Electrolyte Making for Li-Ion Cells
Bibliographic record
Abstract
Li[Ni 0.42 Mn 0.42 Co 0.16 ]O 2 (NMC442)/graphite pouch cells demonstrate superb performance at high voltage when ethylene carbonate (EC)-free electrolytes, using a solvent mixture that is >95% ethyl methyl carbonate (EMC) and between 2 and 5% of an "enabler", are used. The "enablers", required to passivate graphite during formation, can be vinylene carbonate (VC), methylene-ethylene carbonate (MEC), fluoroethylene carbonate (FEC) or difluoro ethylene carbonate (DiFEC), among others. In order to optimize the amount of "enabler" added to EMC, gas chromatography coupled with mass spectrometry (GC-MS) was used to track the consumption of "enabler" during the formation step. Storage tests, electrochemical impedance spectroscopy (EIS), ultrahigh precision coulometry (UHPC), long-term cycling, differential voltage analysis and isothermal microcalorimetry were used to determine the optimum amount of enabler to add to the cells. It was found that the graphite negative electrode cannot be fully passivated when the amount of "enabler" is too low resulting in gas production and capacity fade. Using excess "enabler" can cause large impedance and gas production in most cases. The choice of "enabler" also impacts cell performance. A solvent blend of 5% FEC with 95% EMC (by weight) provides the best combination of properties in NMC442/graphite cells operated to 4.4 V. It is our opinion that the experiments and their interpretation presented here represent a primer for the design of EC-free electrolytes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.056 |
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".