Creating Edge Sites within the 2D Metal‐Organic Framework Boosts Redox Kinetics in Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium–sulfur batteries have received extensive interest owing to their exceptionally high energy density. Nonetheless, their practical implementation is still impeded by the shuttle effect of polysulfides and sluggish conversion kinetics. Considering that, a porous 2D defective zeolitic imidazolate framework‐7 (ZIF‐7) with abundant active edges is rationally designed as multifunctional sulfur carriers for Li–S batteries. The 2D ZIF‐7 enables uniform distribution of sulfur and rapid Li‐ion diffusion, while rich edges facilitate sufficient exposure to active sites capturing and catalyzing polysulfides. In addition, the nitrogen defects on edge sites can further accelerate the transformation of polysulfides and decrease the energy barrier of Li 2 S decomposition. Consequently, the Li–S batteries demonstrate surprisingly practical prospects with a stable capacity of 676.9 mAh g −1 over 500 cycles at 1 C (capacity retention rate = 72.3%). When assembled into a pouch cell at 2.3 mg cm −2 , it still exhibits a high capacity of 901.1 mAh g −1 after 100 cycles at 0.1 C. This work offers a rational structural design strategy to tackle the challenges of the sulfur cathode.
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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".