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 7Li 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
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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
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".