A Study of Esters As Co-Solvents in Lithium-Ion Batteries
Bibliographic record
Abstract
Abstract: Higher rate capability in Li-ion batteries is always better. Using esters as a co-solvent can significantly improve the rate capability of Li-ion batteries because of their low viscosity which leads to higher electrolyte conductivity [1, 2]. However, which ester is the best to use from a cell lifetime perspective? In this work, the four esters, methyl acetate (MA), methyl propionate (MP), ethyl acetate (EA) and methyl butyrate (MB) were compared as co-solvents in the electrolytes of Li-ion pouch cells at levels from 0% to 60 % by weight. Experiments included high temperature storage, ultra high precision coulometry, high rate charge to determine the onset of lithium plating, long term cycling and isothermal microcalorimetry. In addition, Gering’s Advanced Electrolyte Model was used to compare the conductivity and viscosity benefits associated with the use of each ester. The baseline electrolytes to which the esters were added were 1.2 M LiPF6 in EC:EMC 30:70 or 1.2 M LiPF6 in EC:EMC:DMC (25:5:70 by vol%). Figure 1 shows the results of 60oC storage tests for Li-ion pouch cells containing various amounts of each of the four esters. Cells were stored at either 4.2 V, to examine the stability of the positive electrode/electrolyte interface and at 2.5 V to examine the stability of the negative electrode SEI. Figure 1 shows that of the four esters, methyl acetate is greatly preferred based on the storage testing for this Li-ion cell chemistry. Experiments involving many Li-ion cells chemistries and the four esters will be reported. Based on this work, tradeoffs involving the use of esters have been identified and will be discussed here. [1] M. C. Smart, B. V. Ratnakumar, K. B. Chin, L. D. Whitcanack, J. Electrochem. Soc., 157, A1361 (2010). [2] H.-C. Shiao, D. Chua, Hsiu-ping. Lin, S. Slane, M. Salomon, J. Power Sources, 87, 167 (2000). Figure 1. Open circuit potential versus time of Li-ion pouch cells at 60±0.1oC with different ester contents: (a-d) cells containing MA, MP, EA and MB were precharged to 4.2V; (e-h) cells containing MA, MP, EA and MB were precharged to 2.5V. Figure 1
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".