Design of Quasi‐MOF Nanospheres as a Dynamic Electrocatalyst toward Accelerated Sulfur Reduction Reaction for High‐Performance Lithium–Sulfur Batteries
Bibliographic record
Abstract
Lithium-sulfur (Li-S) batteries are considered as one of the most promising next-generation rechargeable batteries owing to their high energy density and cost-effectiveness. However, the sluggish kinetics of the sulfur reduction reaction process, which is so far insufficiently explored, still impedes its practical application. Metal-organic frameworks (MOFs) are widely investigated as a sulfur immobilizer, but the interactions and catalytic activity of lithium polysulfides (LiPs) on metal nodes are weak due to the presence of organic ligands. Herein, a strategy to design quasi-MOF nanospheres, which contain a transition-state structure between the MOF and the metal oxide via controlled ligand exchange strategy, to serve as sulfur electrocatalyst, is presented. The quasi-MOF not only inherits the porous structure of the MOF, but also exposes abundant metal nodes to act as active sites, rendering strong LiPs absorbability. The reversible deligandation/ligandation of the quasi-MOF and its impact on the durability of the catalyst over the course of the electrochemical process is acknowledged, which confers a remarkable catalytic activity. Attributed to these structural advantages, the quasi-MOF delivers a decent discharge capacity and low capacity-fading rate over long-term cycling. This work not only offers insight into the rational design of quasi-MOF-based composites but also provides guidance for application in Li-S batteries.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".