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Enregistrement 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 sur OpenAlexaffabout
Rémi Petibon, Vincent Chevrier, C. P. Aiken, J. R. Dahn

Notice bibliographique

RevueECS Meeting Abstracts · 2016
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvancements in Battery Materials
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésElectrolyteElectrodeMaterials scienceGraphiteAlloyPolarization (electrochemistry)Capacity lossIonChemical engineeringComposite materialChemistryOrganic chemistry

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,032
Score d'incertitude au seuil0,284

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,258
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission2
Résumé présentoui

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