On the Key Role of the Carbon Conductive Additive on the Performance of Si-Based Electrodes with High Areal Capacities
Notice bibliographique
Résumé
Silicon-based electrode is a promising candidate in lithium-ion batteries (LIB) due to its significantly higher gravimetric capacity (3579 mAh g-1) in comparison to that of graphite (372 mAh g-1). However, during the process of lithiation/delithiation, the silicon material suffers from a huge volume change which has a negative repercussion on the electrode cycle life through the fracturing of the silicon particles and of the solid electrolyte interphase layer (SEI) and the disconnection of inter-particle contacts. Our group has recently shown that high performance silicon-based anodes can be achieved by combining (i) the use of high-energy ball-milling as a cheap and easy process to produce nanostructured silicon powder, (ii) the processing of the electrode with carboxymethylcellulose (CMC) binder at pH 3 condition, which has been proved to be able to promote the covalent grafting of the CMC to the Si particles; (iii)the use of fluoroethylene and vinylene carbonates (FEC/VC) electrolyte additives resulting in a more stable SEI. (1) One of the biggest challenges of commercializing silicon anodes is to reach a high areal capacity of more than 4 mAh cm-2, in order to achieve a volumetric energy density improvement over the use of conventional graphite-based anodes. Electrodes with such high areal capacity require careful design of their formulation at different scales, and in particular a special attention must be paid to the tailoring of durable intimate contacts between the active material particles and the conductive additive network so that sufficient electron transfer could be achieved throughout the electrode from the copper current collector.(2) Here, silicon-based electrodes of various areal capacities were prepared by using either carbon black (Super P, Timcal), vapor grown carbon nanofibers (VGCFs, Showa Denko), or graphite nanoplatelets (GM15, XGSciences) as conductive additive. These electrodes were examined by using SEM, XRD, Raman, electrical four-probe method and galvanostatic charge/discharge tests. The objective was to establish the relationships between the characteristics of the carbon additive and the electrochemical performance of the electrode. It was observed that the electrical conductivity, capacity retention, and coulombic efficiency of the silicon electrode are significantly affected by the shape, surface area, particle size and crystallinity of the used carbon additives. Spherical-shaped carbon black particles tend to agglomerate and fail in creating a conductive network resilient to the silicon particles’ volume variation. In contrast, vapor grown carbon nanofibers maintain more durable contacts with silicon particles by forming a more resilient conductive network due to their wire-like structure compared to carbon black.(3) Graphite nanoplatelets also create a continuous conductive network and seem to limit the mechanical degradation of the electrode coating, likely by playing the role of electrically conducting lubricant. (4) These results demonstrate that the choice of the conductive additive is of crucial importance for the optimization of silicon negative electrodes with commercially relevant areal capacities. References (1) Gauthier, M.; Mazouzi, D.; Reyter, D.; Lestriez, B.; Moreau, P.; Guyomard, D.; Roué, L. Energy Environ. Sci. 2013, 6(7), 2145. (2) Mazouzi, D.; Karkar, Z.; Reale Hernandez, C.; Jimenez Manero, P.; Guyomard, D.; Roué, L.; Lestriez, B. J. Power Sources 2015, 280, 533–549. (3) Lestriez, B.; Desaever, S.; Danet, J.; Moreau, P.; Plée, D.; Guyomard, D. Electrochem. Solid-State Lett. 2009, 12(4), A76. (4) Nguyen, B. P. N.; Gaubicher, J.; Lestriez, B. Electrochimica Acta 2014, 120, 319–326. Figure 1
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
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 ».