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 S 8 →Li 2 S 4 , while the missing cluster defects can catalyze the reaction of Li 2 S 4 →Li 2 S, 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".