A Mixed Ether Electrolyte for Lithium Metal Anode Protection in Working Lithium–Sulfur Batteries
Bibliographic record
Abstract
Lithium‒sulfur (Li–S) battery is considered as a promising energy storage system to realize high energy density. Nevertheless, unstable lithium metal anode emerges as the bottleneck toward practical applications, especially with limited anode excess required in a working full cell. In this contribution, a mixed diisopropyl ether‐based (mixed‐DIPE) electrolyte was proposed to effectively protect lithium metal anode in Li–S batteries with sulfurized polyacrylonitrile (SPAN) cathodes. The mixed‐DIPE electrolyte improves the compatibility to lithium metal and suppresses the dissolution of lithium polysulfides, rendering significantly improved cycling stability. Concretely, Li | Cu half‐cells with the mixed‐DIPE electrolyte cycled stably for 120 cycles, which is nearly five times longer than that with routine carbonate‐based electrolyte. Moreover, the mixed‐DIPE electrolyte contributed to a doubled life span of 156 cycles at 0.5 C in Li | SPAN full cells with ultrathin 50 μm Li metal anodes compared with the routine electrolyte. This contribution affords an effective electrolyte formula for Li metal anode protection and is expected to propel the practical applications of high‐energy‐density 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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".