The Effect of Methyl Acetate, Ethylene Sulfate, and Carbonate Blends on the Parasitic Heat Flow of NMC532/Graphite Lithium Ion Pouch Cells
Bibliographic record
Abstract
The formulation of solvent systems can have a severe impact on the lifetime, rate performance, and temperature performance of lithium ion cells. Methyl acetate (MA) has been found to increase rate and temperature performance of carbonate solvents, but decreased cell lifetime. This work used ultra-high precision coulometry, in-situ gas analysis, and isothermal microcalorimetry to investigate a recently reported promising high rate additive blend of fluoroethylene carbonate (FEC) and 1,3,2-dioxathiolane-2,2-dioxide (DTD) in LiNi 0.5 Mn 0.3 Co 0.2 O 2 /graphite pouch cells. Solvent systems composed of blends of ethylene carbonate (EC), ethyl methyl carbonate (EMC), and dimethyl carbonate (DMC) were investigated, with additions of 0%, 20% and 40% wt MA. MA was found to decrease the coulombic efficiency and increase slippage, parasitic heat flow, and gas volume. The addition of just 1% wt of DTD to 2% FEC improved the performance of cells containing 20% and 40% MA to that of cells containing no MA and 20% MA, respectively, under 4.3 V. Results suggest the negative impact of MA originates from increased electrolyte oxidation at the positive electrode. Additionally, blends of MA in EC:EMC and EC:DMC (3:7 wt) were tested head-to-head, yielding a small improvement to parasitic heat flow and UHPC performance when cells used EC:EMC.
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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 | 0.000 |
| 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".