NMR Studies of Sulfonamide Based Deep Eutectic Lithium Electrolytes
Bibliographic record
Abstract
LiPF6 solutions in organic carbonates, the present day battery industry state of the art lithium ion battery (LIBs) electrolytes, while providing adequate performance in LIBs for consumer applications, have some well-known and considerable drawbacks. LiPF6 hydrolyzes with HF formation and decomposes thermally at near-ambient temperatures, linear carbonates have high flammability and also generate flammable gases through their decomposition. Their replacement or materials-level mitigation measures for their deficiencies should target improved battery performance at both low and high temperatures, as well as increased battery durability. Ionic liquids (ILs) have been at the forefront of the search for improved LIB electrolytes. However; many ILs are very expensive and often exhibit specific conductivities lower by more than one order of magnitude compared to LiPF6in organic carbonates even at room temperature. Deep eutectic electrolytes1 (characterized by a significant depression in the freezing point for a mixture of two or more compounds at some composition) provide a much lower cost alternative to IL’s, while matching their low flammability. Our work focuses on the sulfonamide - lithium class binary deep eutectic electrolytes.2Besides discussing some of the physical properties which determine their suitability for LIBs (electrochemical window, conductivity, viscosity), we hereby present their detailed characterization by NMR. Such a study is particularly important for an in-depth understanding of their structure and properties, which should guide ongoing efforts towards improving their properties. Specialized NMR techniques can provide insight into both the transport properties and the local environment of these deep eutectic electrolytes. In particular, diffusion ordered spectroscopy (DOSY)3 can yield information on the diffusion coefficients and transference numbers of relevant nuclei in the sulfonamide component, cation and anion, i.e., 1H, 7Li, and 19F. We will show that NMR can provide diffusion coefficients over several orders of magnitude (Figure 1). Lithium transport numbers greater than 0.5 have been observed and are hereby reported. Changes in these parameters under applied electric potential are investigated through in-situ NMR imaging techniques, whose efficacy has been demonstrated previously.4 In particular, in situ 7Li imaging can map out the both spatial and temporal dependences of concentration gradients that develop in an electrochemical cells under an applied potential. Such data can then be used to obtain diffusion coefficients and transference numbers from a mathematical solution of the inverse transport problem for the system under study.5 To further probe the solvent structure surrounding the lithium cations, we utilized 1H-7Li and 19F-7Li heteronuclear Overhauser effect spectroscopy (HOESY).6 Our NMR results will be compared with those from molecular dynamics simulations. References [1] Q. Zhang, K. D. O. Vigier, S. Royer, and F. Jérôme, Chem. Soc. Rev. 41, 7108 (2012) [2] I. C. Halalay, D. R. Frisch, O.E. Geiculescu, D. D. Desmarteau, S. E. Creager, and C. Lu, disclosure US 2011/0111308 [3] E. O. Stejskal & J. E. Tanner, J. Chem. Phys. 42, 288 (1965). [4] S. A. Krachkovskiy, A. D. Pauric, I. C. Halalay, and G. R. Goward, J. Phys. Chem. Lett. 4, 3940 (2013). [5] Nyman, A.; Behm, M.; Lindbergh, G. Electrochim. Acta 2008, 53 (22), 6356-6365. [6] W. Bauer, Magn. Reson. Chem. 34, 532 (1996). Acknowledgements The authors acknowledge funding through the NSERC APC program and GM of Canada.
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How this classification was reachedexpand
Full frame machine prediction
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) | 0.001 | 0.000 |
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
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".