Implications of Metal-Site Vacancies on Li-Ni-Mn-Co Based Positive Electrode Materials
Notice bibliographique
Résumé
One of the key issues with Li-excess positive electrode materials is their high irreversible capacity loss (IRC), which is usually ~ 20 % of their first charge capacity. Li-excess materials with IRC as low as 4% have been recently reported by us1. Those materials were intentionally synthesized with less Li than the stoichiometric amount, based on oxidation state rules, and as a result, metal-site vacancies were found in their single-phase, layered, pristine structures. With metal-site vacancies, the structure can be written as Li[ΔqM(1-q)]O2, where Δ is a metal site vacancy and the transition metal layer has no Li atoms. An elemental analysis of metals only on such materials would conclude they were Li rich because when q > 0, the number of moles of Li is greater than the number of moles of transition metal atoms, even though there are no Li atoms in the transition metal layer. The small IRC was found to be related to the presence of metal site vacancies. Following that work, a comprehensive search for materials that contain metal-site vacancies was performed in the Li-Ni-Mn-Co pseudo-ternary system. An array of materials with deliberate Li-deficiency and a wide-range of Ni, Mn, and Co compositions was synthesized and their properties were investigated. It was found that, in the Li-Ni-Mn-Co pseudo-ternary system, materials with metal-site vacancies can be synthesized at many Ni-Mn-Co combinations by forcing Li deficiency. Most of the materials were layered single-phase materials but increasing Li deficiency eventually caused the evolution of a spinel phase. The presence of metal-site vacancies were verified by density measurements made with a He-pycnometer. Figure 1 shows the XRD patterns of several single-phase materials (a to f), which have considerable amount of metal-site vacancies, in the range of 20° to 34° representing superlattice ordering between TM ions and vacancies in the TM layer similar to that reported by McCalla et al2. The relative intensity of superlattice peaks varied with overall metal composition (not shown here) suggesting different degrees of ordering. For example, the absence of superlattice peaks in samples e and f suggests that only a negligible amount of vacancies reside in the TM layer whereas the prominent superlattice peaks in samples a and b suggests a significant amount of vacancies in the TM layer. Thus the nature and the relative intensity of the superlattice peaks can be used as a first approximation to predict the location and distribution of vacancies between TM and Li layers. Detailed results on the implications of metal-site vacancies on the properties of Li-Ni-Mn-Co based positive electrode materials will be presented. References: Shunmugasundaram, R.; Senthil Arumugam, R.; Dahn, J. R. Chem. Mater. 2015,27, 757–767 McCalla, E.; Rowe, A. W.; Camardese, J.; Dahn, J. R. Chem. Mater. 2013, 25, 2716–2721 Figure 1
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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) | 0,000 | 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 | 0,001 | 0,001 |
| Science ouverte | 0,001 | 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 ».