Towards high-performance solid-state Li–S batteries: from fundamental understanding to engineering design
Bibliographic record
Abstract
Solid-state lithium-sulfur batteries (SSLSBs) with high energy densities and high safety have been considered among the most promising energy storage devices to meet the demanding market requirements for electric vehicles. However, critical challenges such as lithium polysulfide shuttling effects, mismatched interfaces, Li dendrite growth, and the gap between fundamental research and practical applications still hinder the commercialization of SSLSBs. This review aims to combine the fundamental and engineering perspectives to seek rational design parameters for practical SSLSBs. The working principles, constituent components, and practical challenges of SSLSBs are reviewed. Recent progress and approaches to understand the interfacial challenges via advanced characterization techniques and density functional theory (DFT) calculations are summarized and discussed. A series of design parameters including sulfur loading, electrolyte thickness, discharge capacity, discharge voltage, and cathode sulfur content are systematically analyzed to study their influence on the gravimetric and volumetric energy densities of SSLSB pouch cells. The advantages and disadvantages of recently reported SSLSBs are discussed, and potential strategies are provided to address the shortcomings. Finally, potential future directions and prospects in SSLSB engineering are examined.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".