The Use of LiFSI and LiTFSI in LiFePO<sub>4</sub>/Graphite Pouch Cells to Improve High-Temperature Lifetime
Bibliographic record
Abstract
The use of LiPF 6 in Li-ion battery electrolytes provides sufficient stability, conductivity, and cost in most applications. However, LiPF 6 has also been known to cause degradation in Li-ion cells, primarily from its thermal decomposition or hydrolysis to form acidic species. This work considers the use of imide salts lithium bis(fluorosulfonyl)imide (LiFSI) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) as an alternative to LiPF 6 in LiFePO 4 /Graphite cells. The use of LiFSI or LiTFSI over LiPF 6 improved cycling performance both in control electrolyte (no additives) and electrolyte containing 2% vinylene carbonate (VC). However, while metrics from ultra high precision coulometry, isothermal microcalorimetry, and storage experiments all agreed with long-term cycling results for cells with control electrolyte, the opposite was seen with 2VC electrolyte. Pouch bag experiments elucidated information about the origin of parasitic reactions in LFP/Graphite cells, showing that most parasitic reactions originate at the negative electrode. Additionally, pouch bag experiments reveal a more passivating graphite solid electrolyte interphase (SEI) for LiFSI + 2VC electrolyte, agreeing with long term cycling experiments. It is concluded that in control electrolyte, the use of LiFSI limits redox shuttles, Fe dissolution, and SEI decomposition, while in 2VC electrolyte, LiFSI introduces a minor self-discharge reaction that does not impact long-term cycling.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".