Sodium (<sup>23</sup>Na) Solid-State NMR Reveals Reaction Products in the Sodium-Oxygen Battery
Notice bibliographique
Résumé
The Na-O2 battery is a promising energy storage device due to its high theoretical energy density and low cost of sodium metal, however this technology remains in the early stages of development, due to the challenges related to reversibility, and electrolyte stability.[1] Here we demonstrate the utility of 23Na nuclear magnetic resonance (NMR) as a diagnostic tool for screening sodium–oxygen (Na-O2) discharge products. When an ether-based electrolyte is employed, either one or both of the desirable products; sodium superoxide (NaO2) and sodium peroxide (Na2O2) is electrochemically formed.[2-5] In addition to the anticipated electrochemistry, electrolyte breakdown also occurs during the operation of the cell, where sodium carbonate (Na2CO3) is a main electrolyte breakdown product.[1]Currently the underlying battery chemistry is still unclear but can be revealed through the careful characterization of electrochemically-cycled electrodes, using advanced strategies including solid-state NMR. NaO2, Na2O2 and Na2CO3 are readily distinguishable in the 23Na NMR spectrum, as shown in the experimental data in Figure 1, and supported by our quantum chemical calculations of the quadrupole parameters for both 17O and 23Na. In a mixed sample, where the potential reaction products are ground together, the presence of paramagnetic NaO2 is observable based on its well-resolved chemical shift and rapid relaxation. This is contrasted by the spectral overlap and slow relaxation times of the diamagnetic Na2O2 and Na2CO3 species, which have both unique, and overlapping resonances in their 23Na MAS NMR spectra. Nevertheless, the two species can be quantified using 2D multiple quantum spectroscopy applied under magic angle spinning (MQ-MAS) at 20-40kHz. We have utilized this NMR strategy to develop calibration curves for the relative intensities of the peaks in 2D spectra, as a function of the mass of each possible reaction product. We have compared this data with the 2D MQMAS spectra acquired for electrodes extracted from sodium-oxygen cells, as shown in Figure 2. Three regions of peaks are clearly visible, with the broad, lower frequency peak assigned to sodium carbonate, and the narrow, highest frequency peak assigned to sodium peroxide. The challenging region to interpret is the overlapping middle region, where both sodium peroxide and sodium carbonate have resonances, as evident also in Figure 1. The 2D NMR strategy used here allows for quantitative extraction of the individual quadrupolar lineshapes, which compare very well to the calculated lineshapes for these constitutents. Thus, the calibration data can be used to systematically compare electrochemical reaction products for the sodium-oxygen electrochemistry. This investigation begins with a comparison of electrochemically-cycled electrodes where the electrolyte was diethylene glycol diethyl ether. With 23Na NMR we can determine which reaction product is electrochemically formed in Na-O2cells as a function of both electrolyte composition and cycling conditions. [1] Q. Sun, Y. Yang, Z.-W. Fu, Electrochemistry Communications 2012, 16, 22. [2] P. Hartmann, M. Heinemann, C. L. Bender, K. Graf, R.-P. Baumann, P. Adelhelm, C. Heiliger, J. r. Janek, The Journal of Physical Chemistry C 2015, 119, 22778. [3] W. Liu, Q. Sun, Y. Yang, J.-Y. Xie, Z.-W. Fu, Chemical Communications 2013, 49, 1951. [4] C. L. Bender, W. Bartuli, M. G. Schwab, P. Adelhelm, J. Janek, Energy Technology 2015, 3, 242. [5] H. Yadegari, Y. Li, M. N. Banis, X. Li, B. Wang, Q. Sun, R. Li, T.-K. Sham, X. Cui, X. Sun, Energy & Environmental Science 2014, 7, 3747. 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) | 0,002 | 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 ».