Determining Parasitic Reaction Enthalpies in Lithium-Ion Cells Using Isothermal Microcalorimetry
Bibliographic record
Abstract
Typical measurements of parasitic heat flow in lithium-ion cells can determine the magnitude of heat flow but cannot differentiate between different types of reactions. This causes difficulty when comparing cells that contain different coatings and solvent systems, which may affect parasitic reaction pathways. In this work, a new technique to measure the rate and net enthalpy change (Δ H ) of parasitic reactions in lithium ion cells is introduced. Isothermal microcalorimetry and precision current measurements are used to make in-situ, non-destructive measurements of heat flow and parasitic current during high-voltage holds. The results are used to determine Δ H of parasitic processes in NMC532/graphite pouch cells. Effects of positive electrode coating, solvent, and upper cutoff voltage are explored. Solvent systems of organic carbonates with and without methyl acetate, and fluorinated carbonates are also investigated. Each solvent system yielded a unique trend of Δ H with increased voltage, and results were consistent between pair cells. Cells with organic carbonates showed consistent Δ H between voltages, while cells containing methyl acetate showed decreasing Δ H . Cells containing fluorinated carbonates showed increasing Δ H with increased voltage. The observed trends in Δ H and solvent systems as well as future developments of this method are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".