Structural Design of Lithium–Sulfur Batteries: From Fundamental Research to Practical Application
Bibliographic record
Abstract
Abstract Lithium–sulfur (Li–S) batteries have been considered as one of the most promising energy storage devices that have the potential to deliver energy densities that supersede that of state-of-the-art lithium ion batteries. Due to their high theoretical energy density and cost-effectiveness, Li–S batteries have received great attention and have made great progress in the last few years. However, the insurmountable gap between fundamental research and practical application is still a major stumbling block that has hindered the commercialization of Li–S batteries. This review provides insight from an engineering point of view to discuss the reasonable structural design and parameters for the application of Li–S batteries. Firstly, a systematic analysis of various parameters (sulfur loading, electrolyte/sulfur ( E / S ) ratio, discharge capacity, discharge voltage, Li excess percentage, sulfur content, etc.) that influence the gravimetric energy density, volumetric energy density and cost is investigated. Through comparing and analyzing the statistical information collected from recent Li–S publications to find the shortcomings of Li–S technology, we supply potential strategies aimed at addressing the major issues that are still needed to be overcome. Finally, potential future directions and prospects in the engineering of Li–S batteries are discussed. Graphical Abstract
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".