Synergistic Effect of Dual‐Functional Groups in MOF‐Modified Separators for Efficient Lithium‐Ion Transport and Polysulfide Management of Lithium‐Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium‐sulfur batteries (LSBs) have been regarded as an attractive candidate for future energy storage systems owing to their exceptionally high energy density. However, the further application of LSBs is faced with critical challenges such as the intrinsic insulation of sulfur and the shuttle effect of soluble lithium polysulfides (LiPS). To overcome these problems, a dual functional metal‐organic framework (UIO‐66‐NH 2 ‐HSO 3 ) modified separator is proposed, strategically implemented to examine the dual functionality in anchoring LiPS and facilitating Li + transport. Theoretical calculations indicated that the Li + diffusion kinetics and LiPS adsorption ability are synergistically boosted by the dual‐functional groups in the framework. The ‐HSO 3 demonstrates high affinity for capturing LiPS species while simultaneously repulsing polysulfide anions. Conversely, ‐NH 2 effectively immobilizes these anionic species. Additionally, the lower LUMO energy level of NH 2 ‐H 2 BDC and the higher HOMO energy level of HSO 3 ‐H 2 BDC significantly accelerate the reaction kinetics of LSBs. Electrochemical assessments revealed that the UIO‐66‐NH 2 ‐HSO 3 @PP composite delivers ultra‐high rate capability and long‐term cycling durability, surpassing most of the reported results. In situ spectroscopic analysis established that the UIO‐66‐NH 2 ‐HSO 3 @PP facilitates homogeneous lithium‐ion migration while mitigating polysulfide shuttling. This study provides a theoretical foundation for the rational design of multifunctional MOFs membranes as advanced separators for high‐performance LSBs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".