A Comparative Study of Vinylene Carbonate and Fluoroethylene Carbonate Additives for LiCoO<sub>2</sub>/Graphite Pouch Cells
Bibliographic record
Abstract
Vinylene carbonate (VC) and fluoroethylene carbonate (FEC) are compared as electrolyte additives for LiCoO 2 /graphite pouch cells using the ultra high precision charger (UHPC) at Dalhousie University, an automated storage system, electrochemical impedance spectroscopy (EIS) and long term cycling. Both VC and FEC are useful additives that improve couloumbic efficiency (CE), reduce charge end point capacity slippage, improve long-term cycling and reduce self-discharge during storage compared to cells with control electrolyte. Increasing the concentration of VC over 2% causes a dramatic increase in charge transfer resistance at the negative electrode surface, while the same effect is not observed for FEC. Therefore larger concentrations of FEC can be added to the electrolyte without this problem. However, when 4 or 6% FEC is used, greater gas generation during extended cycling at 40°C is detected. When only a single additive of VC or FEC is used in these LCO/graphite pouch cells tested at 40°C, a concentration of between 2% and 4% VC appears to be optimum as that provides high CE, low charge end point capacity slippage, a small increase in charge-discharge polarization with cycling and a small self-discharge during storage. The VC content would be optimized between these limits to trade off lifetime for rate capability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".