Melamine-based nanoscale porous organic frameworks as multifunctional separator modifiers to mitigate the polysulfide shuttle effect in lithium–Sulfur batteries
Bibliographic record
Abstract
The shuttling of polysulfides between electrodes in lithium-sulfur (Li-S) batteries significantly impairs cycle stability. This study explores the use of melamine-based nanoscale porous organic frameworks (POFs) as polysulfide reservoirs to modify glass fiber (GF) separators. Melamine was reacted with dibromoalkanes of varying carbon chain lengths (n = 4, 8, and 12) to produce a series of POF materials, POF-C n , with different nanoscale pore sizes and solubilities. The POF composites, POF-C n /SP/PVP, which include conductive carbon Super P (SP) and a polyvinylpyrrolidone (PVP) binder, were coated onto GF membranes to create modified separators for Li-S batteries. Batteries with these modified GF separators exhibited higher initial capacities, improved rate performance, and better long-term cycle stability compared to those with non-modified separators. Among the POF composites, POF-C 8 /SP/PVP exhibited the best performance, with an initial specific capacity of 1392 mAh g -1 at 0.1C and a high capacity retention of 90% after 300 cycles at 0.5C. The enhanced capacity, stability, and rate performance are attributed to the nanoporous structure of POF-C 8 and its high nitrogen content, which effectively traps soluble LiPSs and reduces their diffusion toward the Li anode. The good solubility of POF-C 8 facilitates uniform dispersion in the modifying layer, promoting efficient polysulfide trapping and maximizing their utilization in electrochemical reactions, aided by the conductive SP in the composite.
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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) | 0.000 | 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 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".