Triple-Site Integrated Redox-Active Metal–Organic Cages Enable Complementary Acceleration Mechanisms for Serially Enhancing Sulfur Redox Kinetics
Bibliographic record
Abstract
The development of lithium–sulfur batteries (LSBs) is hindered by the shuttle effect of lithium polysulfides (LiPSs) and sluggish sulfur redox reaction (SRR) kinetics. Herein, we integrate multiple functional units (−SH, −NH 2, Zr) into metal–organic cages (MOCs) to construct a triple-site integrated MOC (TSI-MOC), synergistically suppress the shuttle effect, and promote the SRR. The −SH-decorated ligand forms a sulfur oligomer with LiPSs, promoting faster reaction pathways. The exposed Zr-based clusters catalyze the conversion of LiPSs, while the −NH 2 -functionalized ligand adjacent to the metal clusters aids in aggregating LiPSs, further enhancing the catalytic and confinement effects. LSBs with TSI-MOCs deliver a higher discharge capacity (949.7 mAh g –1 ) and a lower capacity decay rate (only 0.018% at 1 C) compared to those with single-site MOCs (S-MOCs) and dual-site integrated MOCs (DSI-MOCs). The TSI-MOC also enables LSBs with a high areal capacity of 9.08 mAh cm –2 under a high sulfur loading of 9.1 mg cm –2, as well as the stable operation of Li–S pouch cells with a high energy density of 307 Wh kg –1 . This work demonstrated the importance of integrating multiple functional sites to improve the chemical interactions between hosts and redox-active intermediates, facilitating the thoughtful design of MOCs for high-performance LSBs.
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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".