Methods of Composite Electrode Imaging
Notice bibliographique
Résumé
Lithium intercalation electrodes are composite devices that consist of typically at least four different constituents that provide separately charge storage, electronic conduction, ionic conduction and mechanical integrity. Consequently, the microstructure of these composites has an important role to play in intercalation electrode performance. An inhomogeneous electrode microstructure affects battery performance by increasing peak local current densities and resistances during the operation. This reduces accessible capacity, compromises safety, as it facilitates localized overcharging, and impacts ion transport and consequently power performance.1 Origins for inhomogeneity in electrode microstructure can lie in the casting procedure, slow carbon agglomeration over cycling as well as in binder adhesive failure due to active material volume changes, which causes a connectivity loss between active material and the matrix. Given this impact of the electrode microstructure, an improved understanding of microstructure and microscopic performance variations is of great importance to optimizing overall battery performance. As such, we need tools that allow us to measure microscopic performance, and correlate to microstructure, interfaces and their development over cycling. Thus a variety of spatial imaging techniques can be used to characterize the processes ex situ, in situ, and operando. Each technique gives a unique and specific information about certain process.2 , 1 We are highlighting herein X-ray tomography and scanning electrochemical microscopy (SECM) as tools for the investigation of disconnection mechanisms. X-ray Tomography (XRT) is a 3D imaging technique used to visualize complex mechanical interactions in batteries during operation. This nondestructive technique can provide a spatial resolution down to the nanometer scale. X-ray tomography can provide information of the material porosity and tortuosity, chemical composition and crystals and the reactions inside the battery.4 , 2 We are using this technique in the imaging, analysis and tracking of electrode microstructure with cycling. Scanning electrochemical microscopy (SECM) is a scanning probe technique that uses a microscopic electrochemical electrode as probe. This allows the measurement of local electrochemical activity on a substrate surface. It has previously been used to investigate the properties of solid electrolyte interphase (SEI) of the negative electrode as well as exploring the lithium ion activity at micro and nano scales resolution.3 We are employing this method to correlate microstructure and performance inhomogeneity. This presentation summarizes first results from both techniques and contrasts structural with performance inhomogeneity. We will be using these tools as we are developing new binders and microstructures for intercalation electrodes to improve the synergy between electrode constituents and optimize overall electrode performance. References (1) Müller, S.; Eller, J.; Ebner, M.; Burns, C.; Dahn, J.; Wood, V. Quantifying Inhomogeneity of Lithium Ion Battery Electrodes and Its Influence on Electrochemical Performance. J. Electrochem. Soc. 2018, 165 (2), A339–A344. https://doi.org/10.1149/2.0311802jes. (2) Schröder, D.; Bender, C. L.; Arlt, T.; Osenberg, M.; Hilger, A.; Risse, S.; Ballauff, M.; Manke, I.; Janek, J. In Operando X-Ray Tomography for next-Generation Batteries: A Systematic Approach to Monitor Reaction Product Distribution and Transport Processes. J. Phys. D. Appl. Phys. 2016, 49 (40), 404001. https://doi.org/10.1088/0022-3727/49/40/404001. (3) Ventosa, E.; Schuhmann, W. Scanning Electrochemical Microscopy of Li-Ion Batteries. Phys. Chem. Chem. Phys. 2015, 17 (43), 28441–28450. https://doi.org/10.1039/c5cp02268a. (4) Pietsch, P.; Wood, V. X-Ray Tomography for Lithium Ion Battery Research: A Practical Guide. Annu. Rev. Mater. Res. 2017, 47 (1), 451–479. https://doi.org/10.1146/annurev-matsci-070616-123957.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,007 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».