Notice bibliographique
Résumé
Li-ion batteries have long been used as the energy source for portable electronics. It is also the battery of choice for the burgeoning electric vehicle industry. The first commercial Li-ion cell used a LiCoO2 cathode and a carbonaceous anode. Only recently have cell makers introduced new materials, such as NMC type cathodes used in many cells today. In most cases the anode is still graphite, despite decades of research on new materials with higher energy density. Much of the research in new anode materials has focused on alloys, usually of Si due to its ability to alloy with large amounts of Li. However, problems persist that keep alloy anodes from widespread use in commercial cells. The biggest issue for the implementation of alloy anodes is their huge volume expansion during lithiation that can lead to particle fracture, pulverization and electrical isolation of portions of the anode, disruption of the SEI and ultimately capacity loss and cell failure [1]. It has been shown that using amorphous or nanostructured alloys can alleviate the pulverization experienced by crystalline materials that suffer from inhomogeneous expansion during phase changes [2]. Also, the inclusion of inactive phases in alloys can help to lessen the expansion of alloy anodes during lithiation, thus easing the stress on the electrode and reducing capacity fade [3]. Mechanical milling has long been used to both reduce grain size and create metal alloys. Ferguson et al. [4,5] used mechanical attrition to make nanostructured Sn-Co-C alloys. These alloys had improved electrochemical performance versus crystalline counterparts of similar composition. However, the process of mechanical attrition can be very time consuming, with some experiments taking days or weeks to produce the desired structure. Typically alloys are made on the lab scale by SPEX milling. Despite decades of research, to our knowledge this method has not been optimized. In fact the SPEX milling conditions commonly used to make alloy anodes are far from optimal and furthermore do not allow the use of soft metals (such as Sn) as starting materials [4]. The purpose of this work was to develop a lab-scale milling method to rapidly produce amorphous or nano-structured materials, and to produce in hours alloys that can take days or weeks to produce with other milling techniques. To accomplish this, 325 mesh Si (Aldrich) was SPEX milled under many different conditions, varying ball size/amount, sample size/amount, temperature, and time. Optimized conditions were then used to synthesize Si and Sn-based alloys. Conditions vastly superior to those previously reported for producing amorphous phases by SPEX milling were found. Most importantly, it was discovered that a reduction in ball size caused the grain size of Si to reduce dramatically during milling. Figure 1 shows a comparison of the Si (111) XRD peak of 325 mesh Si (Aldrich) after being SPEX milled with different conditions. The optimized method leads to a tremendous reduction in the grain size of the Si. Given the success of the optimized SPEX milling method in Si grain size reduction, the question remains if the technique can rapidly produce nano-structured alloys. Figure 2 shows the XRD pattern for a Sn30Co30C40 alloy produced by attrition, reported by Ferguson et al. [4]. Elemental Sn powder could not be used by this method, as soft elements, like Sn, would weld together in the attritor [4]. Instead, the Sn and Co were pre-alloyed to form CoSn2 by arc melting and annealing which added extra processing steps. In contrast, Sn-Co-C alloys could be successfully prepared simply from elemental Sn, Co and graphite powders using the optimized SPEX milling method. An XRD pattern of the Sn30Co30C40 alloy prepared by this method is also shown in Figure 2. Both methods produce an amorphous/nanocrystalline alloy. However, the attritor method took 16 hours to produce the alloy with the XRD pattern shown, while the SPEX mill sample was produced in 3.5 hours. This technique can greatly speed the screening of alloy candidates. A description of the rapid SPEX milling method, microstructure and electrochemistry of some of the alloys produced will be presented and compared to results for alloys produced with more time consuming methods. References. [1] M.N. Obrovac and V.L. Chevrier, Chem. Rev. 114, 11444 (2014). [2] L.Y. Beaulieu et. al., J. Electrochem. Soc., 150(11), A1457 (2003). [3] Ou Mao and J.R. Dahn, J. Electrochem. Soc., 146 (2), 423 (1999). [4] P.P. Ferguson et al., Journal of Power Sources, 194, 794 (2009). [5] P.P. Ferguson, Ph.D. Thesis, Dalhousie University, Halifax, NS, Canada (2009). [6] Ou Mao et al., J. Electrochem. Soc., 146 (2), 405 (1999). 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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,005 |
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 ».