Microporous Cyclodextrin Film with Funnel‐type Channel Polymerized on Electrospun Cellulose Acetate Membrane as Separators for Strong Trapping Polysulfides and Boosting Charging in Lithium–Sulfur Batteries
Bibliographic record
Abstract
The “shuttle effect” of polysulfides hampers the commercialization of lithium–sulfur (Li‐S) batteries. Here, a thin molecular sieve film was decorated on the surface of an electrospun cellulose acetate (CA) membrane derived from recycled cigarette filters, where the truncated cone structure β‐cyclodextrin (β‐CD) was selected as the building block to physically block and chemically trap polysulfides while simultaneously dramatically speeding up ion transport. Furthermore, on the β‐CD free side of the separator facing the cathode, graphite carbon (C) was sputtered as an upper current collector, which barely increases the thickness. These benefits result in an initial discharge performance of 1378.24 mAh g−1 and long‐term cycling stability of 863.78 mAh g−1 after 1000 cycles at 0.2 C for the battery with the β‐CD/CA/C separator, which is more than three times that of the PP separator after 500 cycles. Surprisingly, the funnel‐type channel of β‐CD generates a differential ionic fluid pressure on both sides, speeding up ion transport by up to 69%, and a 65.3% faster charging rate of 9484 mA g−1 was achieved. The “funnel effect” of a separator is regarded as a novel and high‐efficiency solution for fast charging of Li‐S and other lithium secondary 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".