Investigating the Fate of an Electrolyte Additive: A Combined Theoretical and Experimental Study of Prop-1-Ene-1,3-Sultone (PES) in Li-Ion Cells
Bibliographic record
Abstract
INTRODUCTION One way to improve the cycling performance and stability of Li-ion cells is the use of electrolyte additives. In recent years, prop-1-ene-1,3-sultone (PES) has shown great promise for improving cell lifetime and decreasing gas formation. 1–5 Voltage cycling experiments and surface analysis studies have provided important clues for understanding the fate of PES in cells. However, the details of this additive’s mechanism of action remains unknown. This presentation will discuss computational chemistry methods, including the accurate representation of solvation for ethylene carbonate (EC)/ethylmethyl carbonate (EMC) mixtures, and the application of these methods to gain insight into the role and ultimate fate of PES in Li-ion cells. EXPERIMENTAL Calculations were performed with the Gaussian 09 (G09.D01) software package using the B3LYP/6-311++G(d,p) method. The IEFPCM-UFF solvation model and its parameterization will be discussed. A cylindrical, stainless steel capacitance cell, based on the design of Greer and Jacobs, was used for dielectric constant measurements. 6 Machine-made 220 mAh graphite/Li[Ni 1/3 Mn 1/3 Co 1/3 ]O 2 (NMC) pouch cells were filled with 3:7 EC/EMC, 1 M LiPF 6 , and 0 – 2 % PES, and galvanostatically cycled. 4 Select cells were disassembled in an argon-filled glove box for XPS surface analysis as described by Madec et al . 7 Volumetric and GC-MS analysis of gas formation was performed as described by Self et al . 5 RESULTS AND DISCUSSION Computational chemistry can be used to determine standard electrode potentials, free energies of reactions and transition state energies. It is, however, imperative that solvation is properly modeled to obtain meaningful results. The polarizable continuum model (PCM) is a simple yet robust approach that requires only the dielectric constant (static permittivity) of the reaction medium. Therefore, dielectric constants of EC/EMC solvent blends were measured at various compositions and temperatures. It was found that measured values do not exactly match those predicted by a simple linear combination of EC and EMC. The reactions of PES at the electrodes were then investigated. PES reduction has a calculated reduction potential of 1.0 V vs. Li/Li + , which closely matches experimental dQ/dV plots. 4 The subsequent reduction is predicted to occur very rapidly and results in the reactive Li 2 PES compound shown in Figure 1. The decomposition of this compound and its various reactions with the solvent (EC and EMC) and with other PES molecules will be discussed. These reactions are spontaneous and result in the formation of Li 2 SO 3 and organic sulfate species (RSO 3 Li) at the anode. This is a good match to the S 2p peaks observed in the XPS spectrum of the anode after formation. The predicted gas-phase products, including several hydrocarbons at the anode and the formation of O=C=S at the cathode, are also consistent with those observed by GC-MS. In summary, carefully developed theoretical methods coupled with experimental data reveal several spontaneous pathways for the reductive decomposition of PES. It is hoped that these results will also prove useful for developing new and improved electrolyte additives. REFERENCES 1. B. Li et al., J. Mater. Chem. A , 1 , 12954–12961 (2013). 2. B. Li et al., Electrochimica Acta , 105 , 1–6 (2013). 3. B. Li, M. Xu, T. Li, W. Li, and S. Hu, Electrochem. Commun. , 17 , 92–95 (2012). 4. J. Xia et al., J. Electrochem. Soc. , 161 , A1634–A1641 (2014). 5. J. Self, C. P. Aiken, R. Petibon, and J. R. Dahn, J. Electrochem. Soc. , 162 , A796–A802 (2015). 6. D. T. Jacobs and S. C. Greer, Rev. Sci. Instrum. , 51 , 994–995 (1980). 7. L. Madec et al., J. Phys. Chem. C , 118 , 29608–29622 (2014). 8. K. Xu, Chem. Rev. , 114 , 11503–11618 (2014). Figure 1 – The reduced Li 2 PES compound predicted to form at the anode during cell formation. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".