Embedded Nanoparticle Composites as Li-Ion Battery Anode Materials
Notice bibliographique
Résumé
Graphite is used as an anode material in nearly all commercial Li-ion Batteries (LIBs). To increase energy density, Si and Sn-based materials (including SiO x ) are intensively being investigated as anode materials. If used to their full theoretical extent, they could increase LIB energy density by as much as 20%. 1 However, the widespread application of these elements is impeded due to their large volume change and unstable solid electrolyte interphase (SEI) formation that leads to capacity fade. A variety of design approaches have been taken to make alloy anodes compatible with commercial cells. 2-4 However, in most cases, the preparation procedures are complex, give low yields, and require expensive chemicals, making their large-scale production prohibitively expensive. Recently, we have shown that mechanofusion can be used to embed natural graphite (NG) with Si-nanoparticles (Si-NP). 5 In this process, the Si-NPs filled voids in the NG that had connections to the NG particle surface. The resulting Si-NP/NG composite particles had increased capacity and capacity retention even when no electrolyte additives were used. These results were highly promising. However, many unknowns remain, including: how the composite morphology and microstructure depend on the mechanofusion conditions, how many NPs can be inserted into NG, and how this process depends on the NP size. In this study, TiO 2 -NPs (rutile phase) were used as model guest particles to make TiO 2 -NP/NG composites and study the embedding process of NP into graphite. TiO 2 was selected for this study, since it is available in NPs of many different sizes and it has low electrochemical activity, allowing the electrochemistry of the NG to be studied after the embedding process. Fig. 1(a) and 1(b) show cross-section SEM images of NG embedded with 100 nm and 300 nm TiO 2 -NPs, respectively. It was found that mechanofusion conditions and NP size have a profound effect on the final composite morphology and microstructure. Importantly, the host graphite was found to retain high crystallinity under mechanofusion conditions that induce TiO 2 guest particle embedding, resulting also in good electrochemical performance of the host graphite. Moreover, the NP size was found to determine the porosity in the loading of the composites, with smaller NP size leading to increased NP loading and reduced internal porosity. These results demonstrate design strategies to NP/NG composite particles that can lead to new high energy density anode materials. Moreover, the dry processing method is cheap, scalable, and does not produce waste. Acknowledgements The authors acknowledge funding from NSERC and NOVONIX Battery Technology Solutions under the auspices of the NSERC Alliance grants program. References: N. Obrovac and V. Chevrier, Chemical Reviews , 114 , 11444-11502 (2014). Liu, Z. Lu, J. Zhao, M. T. McDowell, H. Lee, W. Zhao and Y. Cui, Nature Nanotechnology , 9 , 187-192 (2014). Wang, J.H. Ahn, J. Yao, S. Bewlay and H. Liu, Electrochemistry Communications , 6 , 689-692 (2004). Liu, H. Wu, M. T. McDowell, Y. Yao, C. Wang and Y. Cui, Nano Letters , 12 , 3315-3321 (2012). He, M. Salehabadi, S. Yasmin and M. N. Obrovac, Journal of the Electrochemical Society , 170 , 120511 (2023). 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 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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 ».