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Enregistrement W4309836820 · doi:10.1149/ma2022-023278mtgabs

Optimizing Aqueous Binders for Next-Generation Lithium-Ion Batteries: A Practical Approach

2022· article· en· W4309836820 sur OpenAlexaff
James Sturman, Chae-Ho Yim, Mathieu Toupin, Zouina Karkar, Elena A. Baranova, Yaser Abu‐Lebdeh

Notice bibliographique

RevueECS Meeting Abstracts · 2022
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvancements in Battery Materials
Établissements canadiensNational Research Council CanadaUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésAnodeSiliconMaterials scienceLithium (medication)GraphiteGravimetric analysisChemical engineeringCarbon fibersNanotechnologyElectrodeComposite materialMetallurgyComposite numberChemistryOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

The demand for lithium-ion batteries continues to grow as the need for renewable energy increases. Over the past few decades, lithium-ion batteries have seen widespread adoption in portable electronics, and they are increasingly being used in electric vehicles. To satisfy the energy density needed for these next-generation applications, new anode materials with high-energy densities are required. Traditional graphite is a very stable intercalation-based anode, but its low capacity has driven many researchers to identify alternatives. Silicon (Si) has one of the highest gravimetric energy densities known when alloyed with lithium. Unlike graphite which is limited to 1 lithiumion per 6 carbon atoms (LiC 6 ), the lithium-silicon alloy Li 15 Si 4 has nearly 4 lithium ions per silicon atom. This gives silicon a theoretical capacity of 3579 mAh/g, compared to the 372 mAh/g for graphite. However, the challenge with silicon (and most alloy anodes) is its poor stability— as the alloying of lithium and silicon is accompanied with a 300% volume expansion. On a microscopic scale, this causes material/electrode swelling and pulverization, which in turn causes severe capacity fade in a few cycles. The binder is known to play an important role in the cycle stability of silicon-based anodes for lithium-ion batteries. Polyvinylidene fluoride (PVDF) has traditionally been the binder used in the preparation of both graphite anodes and lithium metal oxide cathodes. It is electrochemically stable; that is, it is not reduced at the low potential of the anode (~5 mV vs Li) and is not oxidized at the high potential of the cathode (5 V vs Li). However, PVDF has three main disadvantages. First, PVDF cannot accommodate the large volumetric expansion of silicon lithiation. As a result, batteries using PVDF as a binder with silicon will experience a very rapid drop in capacity. Second, PVDF is not water soluble and requires the use of organic solvents like N-Methyl-2-pyrrolidone (NMP) during electrode processing. Finally, the cost of PVDF is approximately 20 USD/kg, which is several times greater than competing binders like sodium carboxymethylcellulose (NaCMC) at 6 USD/kg. Next-generation binders should be inexpensive, able to accommodate the expansion of silicon, and use environmentally friendly processing. Natural biopolymers (such as NaCMC and xanthan gum XG) are a promising class of binders that offer several advantages over traditional polyvinylidene fluoride (PVDF). Biopolymers contain many carboxylate and hydroxyl functional groups which aid in the formation of noncovalent bonding with the oxide surface of silicon. In particular, xanthan gum is a high molecular weight polysaccharide produced extracellularly by the bacteria Xanthomonas campestris . It is water soluble and thus eliminates the need for organic solvents. In addition, its high molecular weight is known to provide better capacity retention, owing to the long molecular chains that can accommodate silicon expansion. While existing studies have explored the fundamental properties of these biopolymer binders and their interaction with silicon, there has been little research on the use of these binders under realistic processing conditions (≤ 10 wt% binder). Herein, we optimize the electrochemical performance of both NaCMC and XG-based silicon electrodes with a low binder content. In addition, we report results for both a high-silicon (≥80 wt%) and a practical low-silicon (20 wt%) composite, all while using nano silicon prepared by industrial-scale synthesis. Figure 1 below shows the capacity retention of different electrode preparations. A higher initial capacity is expected with 80% silicon. However, the overall stability is superior for the low silicon-high graphite (G) composites. Keywords: high-capacity anodes; binders; biopolymers; silicon; lithium-ion batteries Figure 1. Capacity vs cycle number for 1 st Generation Si-based electrodes. Cycled at C/10. References H. Yim, S. Niketic, N. Salem, O. Naboka, and Y. Abu-Lebdeh, J. Electrochem. Soc. , 164 , A6294–A6302 (2017). Zhao, S. Niketic, C. H. Yim, J. Zhou, J. Wang, and Y. Abu-Lebdeh, ACS Omega , 3 , 11684–11690 (2018). M. Courtel and Y. Abu-Lebdeh, “Use of Xanthan Gum as an Anode Binder,” Patent 10,483,546 B2, 2019. Li, Z. G. Wu, Y. M. Liu, Z. W. Yang, G. K. Wang, Y. X. Liu, Y. J. Zhong, Y. Song, B. H. Zhong, and X. D. Guo, Ionics (Kiel). , 27 , 1829–1836 (2021). 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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,358
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
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 Opus0,059
Tête enseignante GPT0,277
Écart entre enseignants0,218 · 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.

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

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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