Nmr's Perspective of Speciation Process in Lithium Sulfur Batteries
Bibliographic record
Abstract
Lithium sulfur (Li-S) battery is a promising alternative technology of lithium ion batteries. However, due to the complicity of the chemistry in Li-S batteries, the mechanism is not well understood. In addition, the co-existence of soluble species and insoluble species limits the application of other characterization techniques. The sensitivity and element selectivity make NMR spectroscopy a powerful tool to study the changes in local chemical environment and speciation process in Li-S batteries. In this study, in situ 7 Li NMR spectroscopy was employed where plastic pouch cells were assembled and cycled in the magnet while NMR spectra were acquired simultaneously. Method to quantitatively study entire lithium inventory in a Li-S battery is developed and implemented, and the cell design is optimized for electrochemical performance and spectroscopic resolution. This methodology can be readily extended to Li-S batteries with other electrolytes where the speciation process can be tracked and analyzed. The development of electrolyte will also benefit greatly from the detailed understanding of the Li-S battery speciation process as well as the additive development. [1] See, et. al., J. Am. Chem. Soc., 2014, 136 (46), pp 16368–16377 [2] Xiao, et. al., Nano Lett., 2015, 15, 3309-3316
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".