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Record W2300533301 · doi:10.1149/ma2016-03/2/717

Toward the Successful Utilization of Si in Negative Electrodes of Li-Ion Batteries

2016· article· en· W2300533301 on OpenAlexaffabout
Rémi Petibon, Vincent Chevrier, C. P. Aiken, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteElectrodeMaterials scienceGraphiteAlloyPolarization (electrochemistry)Capacity lossIonChemical engineeringComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Introduction In order to reduce cost and increase energy density of Li-ion batteries, electrode materials with higher energy densities need to be used. Si-based electrode materials are promising candidates. Si-based electrodes that are structurally stable over many cycles have now been reported [1]. However, in order to successfully implement Si-based electrodes in a full cell configuration, parasitic side reactions with electrolyte occurring due to expansion and contraction of the Si-based material need to be addressed and understood. In this talk, the capacity fade mechanism of Si-alloy-graphite/LiCoO 2 cells will be discussed. The effect of solvent blends as well as electrolyte additive combinations on the lifetime of Si-based full cells will also be discussed. Results and Discussion Figure 1 shows that 200 mAhLiCoO 2 /Si-alloy-graphite pouch cells filled with an electrolyte containing fluoroethylene carbonate (FEC) have a gradual capacity loss during the first 250 cycles followed by a sudden failure. This sudden failure is accompanied by a large cell polarization growth. Analysis of the composition of the electrolyte by gas chromatography revealed that this sudden cell failure is associated with the depletion of FEC. Figure 2 shows that the capacity loss and FEC consumption in LiCoO 2 /Si-alloy-graphite pouch cells have both a time dependence and a cycle number dependence. The time dependence is likely to come from the ever-growing SEI at the negative electrode particle surface (graphite and Si-alloy) while the cycle number dependence is likely to come from the SEI repair at the Si-alloy particle surfaces caused by volume changes during repeated cycling. Figures 1 and 2 strongly indicate that design of new electrolytes is necessary. The new electrolytes should minimize the time dependent and cycle number dependent capacity loss (better passivation) and also minimize the consumption of FEC, if FEC is to be used. Figure 3a shows the normalized discharge capacity of the same Si-alloy-based full cells with an electrolyte containing 10% FEC and various co-solvents. Figure 3a shows that the choice of carbonates has a noticeable impact on the cycle-number-to-failure. For instance, carbonate A and B lead to longer-lived cells. Figure 3a, also shows that propylene carbonate (PC) seems to be superior to ethylene carbonate (EC). Figure 3b shows an example of the impact of additive choice. Figure 3a shows that adding 5% of additive A (proprietary compound) allows the FEC content to be halved while keeping the cycle-number-to-failure relatively unchanged. Additive A also minimizes the polarization growth of the cell dramatically (not shown in abstract). Finally, using only additive A and no FEC prevents the sudden failure event. Conclusion Pouch cells using Si-based negative electrodes and FEC-based electrolytes present a distinctive failure mode. This failure is a result of the depletion of FEC. The capacity loss and FEC consumption has been shown to be the result of SEI growth as well as SEI repair during Si-alloy particle expansion/contraction. Electrolyte design has also been shown to help extend the cell lifetime dramatically. A detailed analysis of the failure of Si-based pouch cells will be presented. The effect of solvent substitution as well as additive choice on the cycling performance of Si-based pouch cells will be presented. This will be highly useful to the Li-ion battery community and will serve as a guide for future developments. Acknowledgments The authors would like to thank 3M Canada and NSERC for the partial funding of this work. The authors thank Dr. Jing Li of BASF for providing some of the solvents, salts and additives used in his work. The authors thanks Xiaodong Cao of HSC Corporation for providing some of the mateirals used in the work. Remi Petibon thanks NSERC and the Walter C. Sumner Foundation for Scholarship support. References [1] V.L. Chevrier, L. Liu, D.B. Le, J. Lund, B. Molla, K. Reimer, L.J. Krause, L.D. Jensen, E. Figgemeier, K.W. Eberman, J. Electrochem. Soc. 161 (2014) A783–A791. Figure 1

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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)0.000
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 score0.032
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.258
Teacher spread0.234 · 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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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