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Enregistrement W4391640107 · doi:10.1149/ma2023-025871mtgabs

Multimodal Characterization of Nucleation and Progression of Interfacial Degradation in All Solid-State Batteries

2023· article· en· W4391640107 sur OpenAlexaff
Partha P. Paul, Ji Hu, R. S. Young, Ludovic Broche, Alexander J. E. Rettie, Marco DiMichiel, Philip J. Withers

Notice bibliographique

RevueECS Meeting Abstracts · 2023
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvancements in Battery Materials
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésNucleationDegradation (telecommunications)Characterization (materials science)Materials scienceSolid-stateChemical engineeringNanotechnologyEngineering physicsComputer scienceEngineeringThermodynamicsPhysicsTelecommunications

Résumé

récupéré en direct d'OpenAlex

We face an immediate need for more energy-dense batteries that are stable over long-term cycling, to address the increased electrification of the transportation sector. All-solid state batteries (ASSBs) that combine a solid-state electrolyte with a Li metal anode offer the potential to achieve this objective by replacing intercalation anodes such as graphite with Li metal. However, interfacial degradation at the Li | solid electrolyte interface currently compromises the safety and cycling stability of ASSBs [1]. This interfacial degradation is usually a combination of instabilities of mechanical, chemical or electrochemical origin. This in turn compromises the structural and morphological stability of ASSBs over cycling, causing them to fail in fewer cycles than required for implementation as next-generation batteries in automobiles [2]. Thus, in order to improve their cycling stability, the understanding of the origin and nature of these interfacial instabilities needs to be multimodal, to understand the interplay between the different degradation mechanisms. Argyrodite (Li6PS5Cl) is a particularly attractive solid-state electrolyte (SSE) due to a high ionic conductivity (comparable to liquid electrolytes), as well as potential for batch processing [3]. However, its brittle nature and chemical composition makes it susceptible to cracking and deleterious side reactions, which hamper its stability. This work will focus on elucidating the effect of (1) current density and (2) processing conditions on the cycling stability of Li | LPSCl | Li ASSBs. We use 4-D XRD-CT (X-ray diffraction computed tomography) combined with phase contrast micro-computed tomography (μCT) to conduct the multimodal investigation of interfacial degradation, under pseudo operando conditions. The methodology is to obtain a 3-D XRDCT scan around the interface in a particular cycled condition, followed by a higher-resolution 3D μCT scan on the same region. This sequence is repeated after every stripping/plating cycle, starting from the pristine cell up to cell failure. These experiments are conducted at a synchrotron source, to enable the acquisition at high spatial resolution (~5 μm) over large volumes (~mm3), in a reasonable amount of time. A standard swagelok-style cell is used for repeatability of results and optimal geometry for conducting tomography. XRD-CT can furnish quantitative phase maps of all phases (argyrodite and reaction by-products), as well as the elastic strain in the sample. The μCT on the other hand gives information on the evolution of morphology around the interface with cycling (cracks/voids). Thus, by correlating the two datasets together over cycling, we are able to connect mechanical instabilities to the chemical/electrochemical instabilities. Here, we focus on varying two specific processing parameters for argyrodite: the sintering temperature and pressure, and studying their effect on nucleation and propagation of interfacial instabilities over repeated cycling. Through cycling, we track the evolution of cracks/voids, chemical by-products such as LiCl, Li3P and Li2S using XRD-CT and μCT. Finally, we track the filling of metallic Li into certain cracks in dendritic form, which leads to shorting and failure of the cell. We find that both of these parameters heavily influence the behavior of the interface, with cells failing between 4-20 cycles, and a marked difference in how the degradation initiates and propagates. Finally, for the processing condition that shows the best cycling behavior, we do a C-rate study, by increasing the current density for plating/stripping with each cycle up to failure. This discusses the utility of a parameter such as critical current density for a cell, where the local current densities are very heterogeneous and different from the global current reading while cycling. We envisage these results being used as inputs into modeling studies to optimize strategies for interfacial stabilization in ASSBs going forward. [1] Paul, P. P. et al. Interfaces in all solid state Li-metal batteries: a review on instabilities, stabilization strategies, and scalability. Energy Storage Materials 2022, 45, 969-1001. [2] Hao, S. et al. Tracking lithium penetration in solid electrolytes in 3D by in-situ synchrotron X-ray computed tomography. Nano Energy 2021, 82, 105744. [3] Chen, Y-T. et al., Investigating dry room compatibility of sulfide solid-state electrolytes for scalable manufacturing, Journal of Materials Chemistry A 2022, 10, 7155-7164.

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 enseignants0,001
Score d'incertitude au seuil0,004

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)0,0010,000

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 Opus0,020
Tête enseignante GPT0,282
Écart entre enseignants0,262 · 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

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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