Phenyl Carbonates As Low Cost and Competitive Additive for Li-Ion Batteries
Bibliographic record
Abstract
Introduction Li-ion batteries are used in a wide range of applications, ranging from consumer electronics to electric vehicles. However these batteries suffer from limited lifetime and high cost. The development of long-lived batteries often necessitates the use of elaborate additives that can further increase their manufacturing cost. In this report, we demonstrate that phenyl carbonates can be very competitive additives and can even perform as well as vinylene carbonate (VC). This class of additives can be very inexpensive and can bring many advantages. Experimental The effect of phenyl carbonates including methyl carbonate (MPC), ethyl carbonate (EPC), and diphenyl carbonate (DPC) as additives was studied in machine-made 220 mAh graphite/Li[Ni 1/3 Mn 1/3 Co 1/3 ]O 2 pouch cells using a wide range of techniques. These techniques included ultra-high precision coulometry, 1,2 open circuit voltage storage, 3 electrochemical impedance spectroscopy (EIS), EIS on symmetric cells, 4 gas chromatography coupled with mass spectrometry for the measurement of additive consumption 5,6 as well as gas composition and liquid reaction by-products. 7,8 Results and discussion Figure 1 shows the compounds detected in the gas formed after the first charge to 3.5 V of NMC(111)/graphite pouch cells filled with 1M LiPF 6 EC:EMC (3:7) base electrolyte containing no additive, containing 1% MPC or 1% DPC. The compounds detected for cells containing no additive can be rationalized with the multiple reduction pathways EC and EMC undertake. 7 Figure 1 shows that small loadings of phenyl carbonates yield very similar gas composition as cells without additives. Figure 1 also shows that cells containing MPC and DPC produced a small quantity of benzene. At the same time, cells containing MPC seemed to produce more CH 4 than cells filled with control electrolyte, while cells containing DPC produced less CH 4 than cells filled with control electrolyte. The presence of benzene, the variation of CH 4 and CO 2 indicate that the phenyl carbonates get reduced at the graphite surface to some extent. In addition, the transesterification of EMC, to DMC and DEC, that occurs during formation in cells with control electrolyte is completely eliminated when 1% MPC or 1%DPC is added to the electrolyte just as it is when 1% VC is added. Figure 2 shows the results of 40°C, 4.2 V open circuit voltage storage experiments of NMC(111)/graphite pouch cells containing no additive (control), 2% VC, different loadings of MPC or 2% VC + 2% MPC. Figure 2a shows that all cells containing either VC or MPC had a much lower voltage drop during storage than cells without any additive. This is strong evidence that both MPC and VC slow the parasitic reactions at the positive electrode. 3 The similarity in voltage drop between cells with VC or phenyl carbonates also indicates that phenyl carbonates are competitive with VC in terms of parasitic reaction reduction at the positive electrode. Figure 2b shows the impedance spectra, measured at 10°C and 3.8 V, of the same cells after 1000 h of storage at 4.2 V and 40°C. Figure 2c shows that small loadings of phenyl carbonates give rise to cells with very small impedance compared to VC. Conclusion Phenyl carbonates are very promising additives. While they slow down parasitic reactions at the negative electrode 7 and at the positive electrode (Figure 2a and 2b) as much as VC, they give rise to cells with very small impedance. Diphenyl carbonate is a very inexpensive chemical whose bulk price is less than half of that of VC. The use of phenyl carbonates as additives can then help yield Li-ion cells with long lifetime, good power performance and reduced manufacturing cost. The detailed reduction mechanism of this class of additive will be discussed as well as their effect on the impedance of the positive electrode and negative electrode. Reference 1. A. J. Smith, J. C. Burns, D. Xiong, and J. R. Dahn, J. Electrochem. Soc. , 158 , A1136–A1142 (2011). 2. A. J. Smith, J. C. Burns, S. Trussler, and J. R. Dahn, J. Electrochem. Soc. , 157 , A196–A202 (2010). 3. N. N. Sinha et al., J. Electrochem. Soc. , 158 , A1194–A1201 (2011). 4. R. Petibon et al., J. Electrochem. Soc. , 160 , A117–A124 (2013). 5. R. Petibon, J. Xia, J. C. Burns, and J. R. Dahn, J. Electrochem. Soc. , 161 , A1618–A1624 (2014). 6. R. Petibon et al., J. Electrochem. Soc. , 161 , A1167–A1172 (2014). 7. R. Petibon, L. M. Rotermund, and J. R. Dahn, J. Power Sources, 287 , 184-195 (2015) 8. J. Self, C. P. Aiken, R. Petibon, and J. R. Dahn, J. Electrochem. Soc. , 162 , A796–A802 (2015). Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".