Defect Engineering for Expediting Li–S Chemistry: Strategies, Mechanisms, and Perspectives
Bibliographic record
Abstract
Abstract Lithium–sulfur (Li–S) batteries have stimulated a burgeoning scientific and industrial interest owing to high energy density and low materials costs. The favorable reaction kinetics of sulfur species is a key prerequisite for pursuing their commercialization. Recent years have witnessed a wealth of investigations in terms of boosting sulfur redox via rationalizing redox mediators. Defect engineering, which allows for the effective exposure of active sites and optimization of electronic structure, has emerged expeditiously as an essential strategy to enhance polysulfide modulation, and hence expedite Li–S chemistry. Nevertheless, a comprehensive overview of defect engineering in Li–S realm is still lacking. This review emphasizes the recent advances in the rational design and polysulfide modulation strategies of different types of defective mediators. Their unique morphological configuration, superb electrochemical activity, and underlying catalytic mechanism are comprehensively summarized, aiming to deepen the understanding of defect‐mediated Li–S chemistry. Moreover, in situ evolution of defective mediators is discussed to identify the true active sites under aprotic reaction conditions. Opportunities and an outlook of this fast‐developing frontier that may lead to practical implementations of Li–S batteries are proposed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".