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Enregistrement W4285399375 · doi:10.1149/ma2022-01184mtgabs

Examining Sulfur Nucleation and Growth on Carbon Nanomaterials from Aqueous, Elemental Sulfur Sols for Lithium−Sulfur Batteries

2022· article· en· W4285399375 sur OpenAlexaff
Gillian F. Hawes, Christian Punckt, Michael A. Pope

Notice bibliographique

RevueECS Meeting Abstracts · 2022
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Battery Materials and Technologies
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésPolysulfideSulfurCarbon fibersLithium (medication)Materials scienceCathodeLithium–sulfur batteryElectrolyteChemistryChemical engineeringInorganic chemistryMetallurgyElectrodeComposite material

Résumé

récupéré en direct d'OpenAlex

With decades of engineering, lithium-ion batteries are beginning to reach their fundamental energy density limits. As such, next generation chemistries that can store significantly more energy are essential for electrification of vehicles, improved portable electronics, and increased adoption of renewable energy sources. Lithium-sulfur batteries have attracted significant research attention due to their theoretical full cell capacity (1167 mAh/g) and gravimetric energy density (2500 Wh/kg) which are both an order of magnitude higher than conventional lithium-ion batteries. Furthermore, lithium-sulfur batteries employ elemental sulfur as the cathode material, which is both widely abundant and inexpensive, and also a common waste product of the petroleum industry, making the projected cost of lithium-sulfur cells per kWh much lower than conventional lithium-ion cells that rely on costly lithium transition metal oxide-based cathodes. However, a number of challenges plague the lithium-sulfur chemistry, one of the most critical being that elemental sulfur and its final discharge product, Li2S, are insulating in nature, requiring sulfur to be intimately mixed with a conductive, lightweight carbon material in order to facilitate electron transport. Because sulfur is typically redistributed throughout the cathode during cycling due to dissolution of intermediate polysulfide species in the electrolyte, the uniform coating of sulfur onto carbon materials has not garnered significant research attention. However, uniform distribution of the sulfur within the cathode is critical for several reasons. Large, insulating sulfur particles are not electrochemically converted during cycling, limiting cell capacity, and as sulfur undergoes an 80% volume change during cycling, non-uniform sulfur distribution can result in mechanical failure of the cathode. Moreover, as the field moves toward all-solid-state batteries, in which sulfur redistribution does not occur, the initial sulfur distribution within the cathode is critical for high performance cells. Nanoscale mixing of sulfur and carbon additives is typically achieved via melt imbibition, a lengthy, high-temperature process in which sulfur is melted and then used to coat solid carbon powders. This process typically employs carbon nanomaterials as aggregated solid powders, thereby limiting the accessible surface area that can be coated by sulfur. To access the exceptional surface area of carbon nanomaterials such as graphene (~2630 m2/g), the sulfur coating process needs to occur while the carbon nanomaterials are dispersed in solvent. While this has been demonstrated previously, it is typically done with highly toxic solvents such as CS2 due to sulfur’s limited solubility, or by using more costly and inefficient sulfur precursors, known as hydrophilic sulfur sols, to form elemental sulfur in situ. We demonstrate, for the first time, the use of aqueous, hydrophobic sulfur sols to coat carbon nanomaterials in solution at room temperature. Hydrophobic sulfur sols are sub-micron, metastable sulfur particles which are formed via the rapid dilution of an organic solution of sulfur into large quantities of water. We demonstrate that due to the metastable nature of these hydrophobic sulfur sols in the aqueous system, sulfur dissolves out of the sols and can uniformly coat the surface of dispersed carbon nanomaterials such as reduced graphene oxide (rGO) via a heterogeneous nucleation and growth process. This process is simple, scalable, employs inexpensive elemental sulfur directly, and can be performed at room temperature with an aqueous system, making it an attractive method to prepare sulfur cathodes. We study how the sulfur deposition process is affected by the introduction of a surfactant into the aqueous phase and the use of different organic solvents to prepare the sol, and further demonstrate that heterogeneous sulfur nucleation occurs preferentially with rGO, while a competing, undesirable homogeneous nucleation pathway that forms large insulating sulfur crystals is observed for Ketjen black and graphene oxide. We demonstrate that via this approach, high loading (3-4 mgsulfur/cm2) rGO/sulfur cathodes can be prepared that achieve capacities of 1300 mAh/g (~4.8 mAh/cm2) at 0.1C, and capacities 7-fold higher than cells prepared via traditional melt imbibition approaches at higher C rates of 0.8C and 1C. Moreover, we demonstrate that these cells can be prepared without additional conductive additives or binder and can achieve projected energy densities of ~468 Wh/kg at 0.1C when considering all inactive components and no lithium degradation, indicating the promise of this simple, novel approach for high energy density sulfur cathodes.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

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)0,0020,000

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 Opus0,017
Tête enseignante GPT0,215
Écart entre enseignants0,198 · 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 source (Gemma direct ou Codex distillé), 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

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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