MétaCan
Menu
← Back to cohort
Record W2297670518 · doi:10.1149/ma2016-03/2/299

Optimization of Electrolytes for Si-Containing Full Cell

2016· article· en· W2297670518 on OpenAlexaff
Vincent Chevrier, Rémi Petibon, C. P. Aiken, L. J. Krause, L. D. Jensen, Ang Xiao, Dinh Ba Le, K. W. Eberman, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteMaterials scienceElectrodeAlloyChemical engineeringDielectric spectroscopyElectrochemistryComposite materialChemistry

Abstract

fetched live from OpenAlex

Introduction The Si/electrolyte interface is increasingly recognized as being a key factor in the successful implementation of Si-based materials in Li-ion batteries. Si-based negative electrode materials undergo massive volume changes during cycling. Pure Si expands by approximately 280% while 3M’s active/inactive Si alloys expand on the order of 135%. The expansion and contraction of Si has important consequences on the surface stability of these materials. This work will focus on the combined impact of material design and electrolyte choices on cell performance. Results and Discussions The 3M Si alloy is compared to pure Si in EC/EMC 3/7 + 10% FEC. Cross section SEM images are taken after 100 cycles in a half cell. While the 3M alloy shows a discernible SEI on distinct particles, the pure Si has dramatically expanded in size and completely lost its particle morphology. The impact of electrolyte choice on the surface morphology of the 3M Si alloy is then explored across a range of electrolytes compositions using cross section SEM images after 100 cycles. Further testing is performed in 200 mAh pouch cells (Lifun), with a LiCoO2 positive electrode, a Si-alloy/graphite negative electrode, and approximately 3 mAh/cm2 of reversible capacity. In order to understand the impact of electrolyte composition as well as failure mechanisms, cells were studied by long term cycling, ultrahigh precision cycling, post-cycling gas chromatography/mass spectroscopy (GC/MS) of the electrolyte, in-situ volume measurements, and electrochemical impedance spectroscopy. Fluorethylene carbonate (FEC) has long been recognized as an important electrolyte component for Si-containing full cells. GC/MS studies are used to study the consumption of FEC with cycle number as well as changes in electrolyte composition. FEC is found to be preferentially consumed relative to other electrolyte solvents and its depletion is found to generally cause sudden failure. A hierarchy of reactivity of electrolyte solvents is established. An electrolyte additive in the new product introduction process at 3M will be presented. This additive is found to help delay sudden failure as well as suppress FEC gassing. Conclusion When implementing well-designed Si-based materials in commercially-relevant full cells, the electrolyte is a key parameter to be optimized. In this work, electrolyte optimizations lead to delayed sudden failure, suppressed gassing and reduced impedance growth. These findings suggest considerable gains are still attainable through further electrolyte optimizations, which will enable deeper penetration of Si-based materials into the marketplace.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.227
Teacher spread0.216 · 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting Abstracts→Same topicAdvancements in Battery Materials→French-language works237,207→