Measuring Parasitic Heat Flow in LiFePO<sub>4</sub>/Graphite Cells Using Isothermal Microcalorimetry
Bibliographic record
Abstract
Isothermal microcalorimetry has previously been used to probe parasitic reactions in Li-ion batteries, primarily studying Li[NixMnyCo1-x-y]O2 (NMC) positive electrode materials. Here, isothermal microcalorimetry techniques are adopted to study parasitic reactions in LiFePO4 (LFP)/graphite cells. Features in the heat flow from graphite staging transitions were identified, and the associated heat flow was calculated using simple lattice-gas mean-field theory arguments, finding good agreement with experimentally measured values. Parasitic heat flow was measured in LFP/graphite pouch cells with different electrolyte additives. In an electrolyte without additives, a massive parasitic heat flow was measured suggesting a shuttle reaction unique to the LFP/graphite system. In cells containing electrolyte additives, parasitic heat flow agreed well with long-term cycling results, confirming the value of this technique to rank the lifetime of LFP/graphite cells with different electrolyte additives. Finally, comparing cells with and without unwanted water contamination, it was found that the parasitic heat flow was similar or slightly higher in cells where water was intentionally removed before cycling, seemingly contradicting long-term cycling results. It is concluded that the presence of water (at the 500 ppm level) may slightly reduce parasitic reactions, but at the expense of a more resistive SEI layer.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".