A Study of the Physical Properties of Li-Ion Battery Electrolytes Containing Esters
Bibliographic record
Abstract
Adding esters as co-solvents to Li-ion battery electrolytes can improve low-temperature performance and rate capability of cells. This work uses viscosity and electrolytic conductivity measurements to evaluate electrolytes containing various ester co-solvents, and their suitability for use in high-rate applications is probed. Among the esters studied, methyl acetate (MA) outperforms other esters in its impact on the conductivity and viscosity of the electrolyte. Therefore, viscosity and conductivity were measured as a function of temperature and LiPF 6 concentration for electrolytes ethylene carbonate (EC): linear carbonate: MA in the ratio 30:(70-x):x, where linear carbonate = {ethyl methyl carbonate (EMC), dimethyl carbonate (DMC)}, and x = {0, 10, 20, 30}. Adding MA leads to an increase in conductivity and decrease in viscosity over all conditions. Calculations of electrolyte properties from a model based on a statistical-mechanical framework, the Advanced Electrolyte Model (AEM), are compared to all measurements and excellent agreement is found. All electrolytes studied roughly agree with a Stokes' Law model of conductivity. A Walden analysis shows that the ionicity of the electrolyte is not significantly impacted by either MA content or LiPF 6 concentration. Li[Ni 0.5 Mn 0.3 Co 0.2 ]O 2 /graphite cells containing MA were cycled at charging rates up to 2C and showed improved cycling performance.
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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 | 0.000 | 0.001 |
| 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".