Effects of Fluorinated Carbonate Solvent Blends on High Voltage Parasitic Reactions in Lithium Ion Cells Using OCV Isothermal Microcalorimetry
Bibliographic record
Abstract
Fluorinated carbonates such as fluoroethylene carbonate (FEC) and di-2,2,2-trifluoroethyl carbonate (TFEC) improve cycling performance and decrease parasitic reactions at high potentials. However they also decrease cell life at lower voltage ranges compared to typical organic carbonate solvents. A method of quantifying the parasitic heat flow during open circuit and voltage hold conditions was implemented using isothermal microcalorimetry on Li-ion pouch cells to investigate the effect of additives in FEC:TFEC (3:7) and the effects of blending ethylene carbonate (EC):ethylmethyl carbonate (EMC) 3:7 with FEC:TFEC (3:7) in order to reduce parasitic reactions during operation at 4.2 V, 4.4 V and 4.6 V. Additives were found to lower parasitic heat flow in FEC:TFEC at all operating potentials, however to less of an effect at 4.6 V. Blending EC:EMC and FEC:TFEC resulted in decreased rates of parasitic reactions at all operating potentials compared to EC:EMC alone. Cells with unblended FEC:TFEC exhibited the lowest parasitic heat flow at 4.4 V and 4.6 V, although they produced more gas than EC:EMC containing blends. This work demonstrates the advantage of blending fluorinated carbonates with traditional solvents, particularly during use at low operating potentials, as well as demonstrating some advantages of fluorinated carbonates for high voltage lithium ion battery applications.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".