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Record W2314751172 · doi:10.1149/ma2016-01/5/466

(Invited) Investigations into the Chemical Role of Additives in Li-Ion Cells

2016· article· en· W2314751172 on OpenAlexaff
David S. Hall, Rémi Petibon, L. D. Ellis, Stephen Glazier, Julian Self, Mengyun Nie, Lénaïc Madec, Ang Xiao, W. M. Lamanna, Kiah Smith, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteEthylene carbonateChemistryLithium (medication)X-ray photoelectron spectroscopyChemical engineeringSolvationElectrochemistrySolventIonElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

INTRODUCTION For over a decade, the most common electrolyte solution in commercially available lithium-ion cells has remained LiPF6 dissolved in some blend of organic carbonate solvents.1 Rather than change the salt or the solvent, many industrial production lines have adopted the use of electrolyte additives to improve cycling performance, extend calendar lifetime, decrease detrimental gas formation and improve lithium-ion cell safety. The practical advantage of this move to electrolyte additives is that performance improvements can be achieved with minimal changes to existing supply chains for electrolyte salts and solvents. However, the optimization of lithium-ion cells for various applications (automotive, grid storage, etc.) would be greatly enhanced by a more detailed understanding of the cell chemistry. In particular, it is desirable to characterize the chemical and electrochemical reactions that occur during solid-electrolyte interphase (SEI) formation for the various additives in use. This presentation will demonstrate how our group has used computational and experimental methods, together, to study SEI formation for two additives, prop-1-ene-1,3-sultone (PES)2 and pyridine boron trifluoride (PBF).3 EXPERIMENTAL Density functional theory (DFT) calculations were performed with the Gaussian 09 (G09.D01) software package using the B3LYP and M06-2X hybrid functionals. The IEFPCM-UFF solvation model and its parameterization will be discussed.4 Several experimental methods will be discussed, including coulometry, in situ volumetric measurements using the Measuring Archimedes’ Gas Evolution (MAGE) instrument, gas-chromatography coupled with mass spectrometry (GC-MS) and thermal conductivity detection (GC-TCD), X-ray photoelectron spectroscopy (XPS), and isothermal microcalorimetry. Experimental details have been described previously.5–9 RESULTS AND DISCUSSION During the initial formation cycle (i.e., the first charge step), PES forms a passive SEI at the negative electrode surface via a two-electron electrochemical reduction, which produces Li2PES (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 Li2SO3 and organic sulfate species (RSO3Li) at the anode. This is a good match to the S2ppeaks observed in the XPS spectrum of the anode after formation. The predicted gas-phase products, including several hydrocarbons at the anode, are also consistent with those observed by GC-MS. PBF similarly forms a passive SEI at the graphite surface by electrochemical reduction. The reduced species, LiPBF, forms a bipyridine boron trifluoride adduct, which is accompanied by the reduction of the solvent component, ethylene carbonate (EC). This reaction produces lithium ethyl carbonate, a soluble lithium semicarbonate. This reaction pathway does not produce an appreciable amount of any gas-phase species, as demonstrated by MAGE, GC-MS, and GC-TCD results. The predicted PBF-derived dimer is consistent with the C1s and N1s peaks observed in the XPS spectrum of the anode surface after formation. In summary, carefully developed theoretical methods coupled with experimental data reveal several spontaneous pathways for the reductive decomposition of two additives, PES and PBF. It is hoped that these results will prove useful for developing new and improved electrolyte additives. Moreover, these results provide new insight into the role of the solvent molecules during SEI formation that may have significance for research into new solvents and solvent blends. REFERENCES 1. K. Xu, Chem. Rev., 114, 11503–11618 (2014). 2. B. Li, M. Xu, T. Li, W. Li, and S. Hu, Electrochem. Commun., 17, 92–95 (2012). 3. M. Nie, J. Xia, and J. R. Dahn, J. Electrochem. Soc., 162, A1186–A1195 (2015). 4. D. S. Hall, J. Self, and J. R. Dahn, J. Phys. Chem. C, 119, 22322–22330 (2015). 5. C. P. Aiken et al., J. Electrochem. Soc., 161, A1548–A1554 (2014). 6. V. L. Chevrier et al., J. Electrochem. Soc., 161, A783–A791 (2014). 7. L. Madec et al., J. Phys. Chem. C, 118, 29608–29622 (2014). 8. R. Petibon, L. M. Rotermund, and J. R. Dahn, J. Power Sources, 287, 184–195 (2015). 9. J. Self, D. S. Hall, L. Madec, and J. R. Dahn, J. Power Sources, 298, 369–378 (2015). 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0410.022

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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".

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Published2016
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