MétaCan
Menu
Back to cohort
Record W2282052266 · doi:10.1149/ma2015-02/5/389

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

2015· article· en· W2282052266 on OpenAlexaff
David S. Hall, Julian Self, Lénaïc Madec, Rémi Petibon, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldChemistry
TopicOrganic and Inorganic Chemical Reactions
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteSolvationEthylene carbonateChemistryIonX-ray photoelectron spectroscopyEne reactionChemical engineeringPhysical chemistryAnalytical Chemistry (journal)ElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.256
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicOrganic and Inorganic Chemical ReactionsFrench-language works237,207