Consumption of Fluoroethylene Carbonate (FEC) on Si-C Composite Electrodes for Li-Ion Batteries
Bibliographic record
Abstract
The electrolyte additive fluoroethylene carbonate (FEC) is known to significantly improve the lifetime of Li-ion batteries with silicon anodes. In this work, we show that FEC can indeed improve the lifetime of silicon-carbon composite anodes but is continuously consumed during electrochemical cycling. By the use of 19 F-NMR spectroscopy and charge/discharge cycling we demonstrate that FEC is only capable to stabilize the cell performance as long as FEC is still remaining in the cell. Its total consumption causes a significant increase of the cell polarization leading to a rapid capacity drop. We show with On-line Electrochemical Mass Spectrometry (OEMS) that the presence of FEC in the electrolyte prohibits the reduction of other electrolyte components almost entirely. Consequently, the cumulative irreversible capacity until the rapid capacity drop correlates linearly with the specific amount of FEC (in units of μmol FEC /mg electrode ) in the cell. The latter quantity therefore determines the lifetime of silicon anodes rather than the concentration of FEC in the electrolyte. By correlating the cumulative irreversible capacity and the specific amount of FEC in the cell, we present an easy tool to predict how much cumulative irreversible capacity can be tolerated until all FEC will be consumed in either half-cells or full-cells. We further demonstrate that four electrons are consumed for the reduction of one FEC molecule and that one carbon dioxide molecule is released for every FEC molecule that is reduced. Using all information from this study and combining it with previous reports in literature, a new reductive decomposition mechanism for FEC is proposed yielding CO 2 , LiF, Li 2 O, Li 2 CO 3 , H 2 and a partially cross-linked polymer.
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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) | 0.000 | 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".