Nontrivial Effects of “Trivial” Parameters on the Performance of Lithium–Sulfur Batteries
Bibliographic record
Abstract
A robust lithium-sulfur (Li–S) battery is constituted by a wide range of optimized fundamental parameters (e.g., amount of electrolyte, electrolyte additive, sulfur loading density, and the size of sulfur particles). In this paper, some other often-neglected “trivial” parameters (including assembly pressure of the coil cells, thickness of spring/lithium foil in coin cells, sheet number of separator, and cut-off voltage) of Li–S batteries have been demonstrated to show pronounced effects on the battery performance. Our results indicate that the coin cell assembly pressure and sheet number of the separator play the important roles in suppressing polysulfide shuttling over battery cycling, which improves significantly the cycling life of Li–S batteries. The thickness of springs/lithium foils also affects the battery performance greatly. When switching the cut-off voltage of 1.5–3.0 V to narrower ones (1.7–2.5 V or 1.8–2.6 V), the cycling life of batteries at 0.2 C can be further enhanced to >300 cycles while with no drastic polysulfide shuttling. Adjusting these trivial parameters can thus synergistically improve the cycling performance of Li–S batteries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".