Notice bibliographique
Résumé
Introduction Si-based alloys are potential candidates as high energy density negative electrodes in Li-ion batteries. Such alloys have high theoretical capacity but also have high volume expansion, which can lead to cell fade. It has been shown previously that amorphous Si (a-Si) synthesized by chemical or physical deposition has improved cycling performance compared to crystalline Si (cr-Si) [1, 2]. Typically, a-Si is made by ball milling or atomic deposition techniques. Templating methods have also been employed to make nanostructured Si alloys that include void space in which the Si can expand [3, 4]. Such alloys can perform well as negative electrodes in Li cells. An inexpensive route for synthesizing bulk quantities of a-Si or nanostructured Si alloys is desirable. In this study, a new chemical delithiation method employing ethanol as an oxidizing agent was applied to prepare bulk quantities of a-Si from Li-Si compounds. The a-Si formed was found to have a unique exfoliated layered structure, which has lower volume expansion than cr-Si and improved cycling characteristics. C-Si and Fe-Si alloys were also synthesized by the delithiation of C-Li-Si and Fe-Li-Si alloys using this chemical delithiation method. When tested as negative electrodes in Li cells, the C-Si and Fe-Si alloys showed superior electrochemical characteristics and lower volume expansion than cr-Si. Experimental Li12Si7, Li7Si3, Li13Si4 or Li22Si5 compounds were first prepared in an arc furnace from the elements. Ethanol was then used to delithiate the Li-Si compounds under an Ar atmosphere. After the resulting slurry was washed with distilled water, a-Si was recovered with a 70% yield. C-Li-Si and Fe-Li-Si precursors with serial C: Si or Fe: Si stoichiometric ratios were prepared by ball milling. Ethanol was then used to delithiate the C-Li-Si or Fe-Li-Si alloys under an Ar atmosphere. After the resulting products were washed with distilled water, C-Si and Fe-Si alloys were recovered with 79% and 70% yields, respectively. Electrode slurries were prepared by mixing active materials (a-Si, C-Si or Fe-Si), carbon black and polyimide in a volume ratio of 62.5/18/19.5 in N-methyl pyrrolidinone. Electrode disks were punched from the coating foil and heated in a tube furnace for 3h at 300 °C under an Ar flow. 2325 coin-type cells were assembled in an Ar-filled glovebox with a Li counter/reference electrode. All cells were cycled between 5~900 mV with a Maccor Series 4000 Automated Test System. Results a-Si prepared from ethanol delithiation of Li12Si7, Li7Si3, Li13Si4, and Li22Si5 resulted in layered products, except in the case of Li22Si5, which was composed of dense particles. Figure 1 shows an SEM image of a-Si prepared from ethanol delithiation of Li12Si7. a-Si prepared from ethanol delithiation of Li12Si7 had the most orderly layered structure, with the layers being highly exfoliated. All a-Si samples had superior cycling performance and lower volume expansion compared to cr-Si. Figure 2 shows the cycling performance of an a-Si electrode prepared from ethanol delithiation of Li12Si7 and a cr-Si electrode. It is thought that the porous layered structure of these delithiated materials can accommodate the Si volume expansion during lithiation, resulting in low overall particle expansion and improved cycling performance. Remarkably, C-Si and Fe-Si alloys made by the delithiation of ball milled C-Li-Si or Fe-Li-Si alloys contained very low silicide or carbide content. Instead the alloys were primarily composed of a nano-composite of elemental C and Si or Fe metal and Si. This was confirmed by X-ray diffraction and Mössbauer spectroscopy. These alloys have a completely different nanostructure than conventional ball milled Si-based alloys, in which carbon and Fe are typically completely reacted to form carbides and silicides. Conclusions The alcohol delithiation method represents an effective means of producing Si-based alloys as negative electrode materials for Li cells. The unique nanostructures of these alloys and their electrochemical performance will be discussed. References [1] L. B. Chen, J. Y. Xie, H. C. Yu, and T. H. Wang, J. Appl. Electrochem., 39, 1157 (2009). [2] R. Epur, M. Ramanathan, F. R. Beck, A. Manivannan, and P. N. Kumta, Mat. Sci. Eng. B-Solid, 177, 1157 (2012). [3] H. Y. Lee and S. M. Lee, J. Power Sources, 112, 649 (2002). [4] M. Yoshio, T. Tsumura, and N. Dimov, J. Power Sources, 146, 10 (2005). 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 | 0,000 |
| 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 | 0,001 |
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 ».