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Record W2347127210 · doi:10.1149/ma2016-03/2/419

Sodium (<sup>23</sup>Na) Solid-State NMR Reveals Reaction Products in the Sodium-Oxygen Battery

2016· article· en· W2347127210 on OpenAlex
Zoë E. M. Reeve, Christopher J. Franko, Kris J. Harris, Hossein Yadegari, Andy Xueliang Sun, Gillian R. Goward

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Synthesis and Characterization
Canadian institutionsWestern UniversityMcMaster University
Fundersnot available
KeywordsElectrolyteChemistryElectrochemistrySodiumBattery (electricity)Relaxation (psychology)Electrochemical cellInorganic chemistryAnalytical Chemistry (journal)Physical chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

The Na-O 2 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 23 Na nuclear magnetic resonance (NMR) as a diagnostic tool for screening sodium–oxygen (Na-O 2 ) discharge products. When an ether-based electrolyte is employed, either one or both of the desirable products; sodium superoxide (NaO 2 ) and sodium peroxide (Na 2 O 2­ ) is electrochemically formed. [2-5] In addition to the anticipated electrochemistry, electrolyte breakdown also occurs during the operation of the cell, where sodium carbonate (Na 2 CO 3 ) 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. NaO 2 , Na 2 O 2 and Na 2 CO 3 are readily distinguishable in the 23 Na NMR spectrum, as shown in the experimental data in Figure 1 , and supported by our quantum chemical calculations of the quadrupole parameters for both 17 O and 23 Na. In a mixed sample, where the potential reaction products are ground together, the presence of paramagnetic NaO 2 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 Na 2 O 2 and Na 2 CO 3 species, which have both unique, and overlapping resonances in their 23 Na 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 23 Na NMR we can determine which reaction product is electrochemically formed in Na-O 2 cells 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 &amp; Environmental Science 2014 , 7 , 3747. Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.014
GPT teacher head0.220
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it