Sodium (<sup>23</sup>Na) Solid-State NMR Reveals Reaction Products in the Sodium-Oxygen Battery
Bibliographic record
Abstract
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
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Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
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