Electrolyte Structure of Lithium Polysulfides with Anti‐Reductive Solvent Shells for Practical Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract The lithium–sulfur (Li–S) battery is regarded as a promising secondary battery. However, constant parasitic reactions between the Li anode and soluble polysulfide (PS) intermediates significantly deteriorate the working Li anode. The rational design to inhibit the parasitic reactions is plagued by the inability to understand and regulate the electrolyte structure of PSs. Herein, the electrolyte structure of PSs with anti‐reductive solvent shells was unveiled by molecular dynamics simulations and nuclear magnetic resonance. The reduction resistance of the solvent shell is proven to be a key reason for the decreased reactivity of PSs towards Li. With isopropyl ether (DIPE) as a cosolvent, DIPE molecules tend to distribute in the outer solvent shell due to poor solvating power. Furthermore, DIPE is more stable than conventional ether solvents against Li metal. The reactivity of PSs is suppressed by encapsulating PSs into anti‐reductive solvent shells. Consequently, the cycling performance of working Li–S batteries was significantly improved and a pouch cell of 300 Wh kg −1 was demonstrated. The fundamental understanding in this work provides an unprecedented ground to understand the electrolyte structure of PSs and the rational electrolyte design in 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 | 0.000 |
| 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".