Accelerated Multi‐step Sulfur Redox Reactions in Lithium‐Sulfur Batteries Enabled by Dual Defects in Metal‐Organic Framework‐based Catalysts
Bibliographic record
Abstract
Abstract The sluggish sulfur redox kinetics and shuttle effect of lithium polysulfides (LiPSs) are recognized as the main obstacles to the practical applications of the lithium‐sulfur (Li−S) batteries. Accelerated conversion by catalysis can mitigate these issues, leading to enhanced Li−S performance. However, a catalyst with single active site cannot simultaneously accelerate multiple LiPSs conversion. Herein, we developed a novel dual‐defect (missing linker and missing cluster defects) metal–organic framework (MOF) as a new type of catalyst to achieve synergistic catalysis for the multi‐step conversion reaction of LiPSs. Electrochemical tests and first‐principle density functional theory (DFT) calculations revealed that different defects can realize targeted acceleration of stepwise reaction kinetics for LiPSs. Specifically, the missing linker defects can selectively accelerate the conversion of S8→Li2S4, while the missing cluster defects can catalyze the reaction of Li2S4→Li2S, so as to effectively inhibit the shuttle effect. Hence, the Li−S battery with an electrolyte to sulfur (E/S) ratio of 8.9 mL g−1 delivers a capacity of 1087 mAh g−1 at 0.2 C after 100 cycles. Even at high sulfur loading of 12.9 mg cm−2 and E/S=3.9 mL g−1, an areal capacity of 10.4 mAh cm−2 for 45 cycles can still be obtained.
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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".