The Effect of LiFePO<sub>4</sub> Particle Size and Surface Area on the Performance of LiFePO<sub>4</sub>/Graphite Cells
Bibliographic record
Abstract
In an effort to better understand capacity loss mechanisms in LiFePO 4 (LFP)/graphite cells, this work considers carbon-coated LFP materials with different surface area and particle size. Cycling tests at room temperature (20 °C) and elevated temperatures show more severe capacity fade in cells with lower surface area LFP material. Measurements of Fe deposition on the negative electrode using micro X-ray fluorescence ( μ XRF) spectroscopy reveal more Fe on the graphite electrode from cells with low surface area. Measurements of parasitic heat flow using isothermal microcalorimetry show marginally higher parasitic heat flow in cells with low surface area. Cross-sectional SEM images of aged LFP electrodes show micro-fracture generation in large LFP particles, which are more prevalent in the low surface area material. Further, studies on the impact of vacuum drying procedures show that while Fe deposition can be inhibited by removing excess water contamination, the direct impact of Fe deposition on capacity fade is small. Despite the observed particle cracking, differential voltage analysis on aged cells suggested active material loss was not significant, leading to the conclusion that LFP particle fracture instead increases parasitic reaction rates leading to Li inventory loss.
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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 | 0.001 | 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".