Performance and Degradation of LiFePO<sub>4</sub>/Graphite Cells: The Impact of Water Contamination and an Evaluation of Common Electrolyte Additives
Bibliographic record
Abstract
LiFePO 4 (LFP) is an appealing cathode material for Li-ion batteries. Its superior safety and lack of expensive transition metals make LFP attractive even with the commercialization of higher specific capacity materials. In this work the performance of LFP/graphite cells is tested at various temperatures and cycling protocols. The amount of water contamination is controlled to study the impact of water on capacity fade in LFP. Further, several additive systems that have been effective in NMC/graphite chemistries are tested in LFP/graphite cells. The presence of excess water impacts cell performance severely when no electrolyte additives are used, or when the electrodes are poorly passivated. When effective additive systems are used, the existence of up to 500 ppm excess water in the cell is does not strongly affect cycle life and storage performance. Fe dissolution is studied in LFP with micro X-ray fluorescence spectroscopy ( μ XRF), and most electrolyte additives virtually eliminate Fe dissolution, even at high temperature and with water contamination. Removing excess water contamination suppresses Fe dissolution in cells without electrolyte additives. Finally, the capacity retention of LFP/graphite cells at high temperature is compared with long lifetime NMC/graphite cells, demonstrating challenges for LFP/graphite cells.
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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 | 0.000 | 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".