Structural Change of the Discharge Products in Lithium Sulfur Battery during Storage
Bibliographic record
Abstract
Lithium-Sulfur (Li-S) battery has been regarded as one of the most promising candidates to commercialize electric vehcles (EVs). Compared to Ni-Cd or commercialized Li ion battery, Li-S battery possesses many advantages, including high theoretical capacity (1,675 mAh/g) and low price of active materials. Energy density, rate capability, and cycling stabilities are regarded as key performances of any next generation secondary batteries, however, memory effect, which is history-dependent variation in battery state, is also a highly important battery chacteristic to be considered. As exampled with Ni-Cd battery, memory effect often leads to an accelerated cell deterioration [1]. In recent years, it was also reported that LiFePO4,which is a promising cathode material for EV applications, has memory effect [2] and it could result in a severe error of battery managment system (BMS). In spite of its importance, such memory effect has not been issued for Li-S battery. In this work, the structural change of discharge products during storage and its influence on the subsequent cycle was studied. Structural changes with different storage time were clearly observed with ex-situ X-ray diffraction (XRD) and X-ray photoeletron spectroscopy (XPS) analysis. The XRD results demonstrated that the discharge products with a low crystallinity is converted to a more crystalline structure during a storage at room temperature. In addition, the XPS spectra collected at different storage time suggest that the structural change observed in XRD originates from a compositional change of the solid discharge products. The structural evolution of the discharge products significantly influence the overpotential of the subsequent charging step. This small perturbation in voltage profile can induce severe error in battery management system in EVs because it can lead to mis-estimate state of charge. The mechanism for the behavior and the impact on the Li-S performances will be presented and discussed.
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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".