Dual-Functional Metal–Organic Framework Freestanding Aerogel Boosts Sulfur Reduction Reaction for Lithium–Sulfur Batteries
Bibliographic record
Abstract
Lithium–sulfur (Li–S) technology stands out as a promising energy storage system. However, its journey toward practical implementation is hindered by sluggish sulfur reduction reaction (SRR) kinetics. A free-standing graphene aerogel (GA) combined with a copper-based metal–organic framework (MOF–GA) is fabricated as the sulfur host material for Li–S battery cathodes. The presence of MOF particles assumes a dual role, demonstrating its efficacy not only as a catalyst for the reduction reaction of graphene oxide (GO) but also as an electrochemical catalyst to promote sluggish SRR kinetics. The former amplifies electron transfer kinetics within the electrode, and the latter elevates the overall cell performance. Experimental results and theoretical calculations have proven the catalytic activity of MOF–GA electrodes, leading to a higher sulfur utilization of over 80% and a lower capacity decay of 0.082% per cycle. Under extreme conditions, the Li–S cells show a high initial specific capacity of 1113.0 mAh·g –1 under an elevated loading of 4.25 mg·cm –2 and a high sulfur fraction >70%. This study shows the effectiveness of the synergist effects of MOF particles within the GA framework in promoting the sulfur redox reaction in Li–S batteries.
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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".