Design Zwitterionic Amorphous Conjugated Micro‐/Mesoporous Polymer Assembled Nanotentacle as Highly Efficient Sulfur Electrocatalyst for Lithium‐Sulfur Batteries
Bibliographic record
Abstract
Abstract The commercial application of lithium‐sulfur (LiS) batteries is seriously hampered by the sluggish redox kinetics and the shuttle effect of lithium polysulfides (LiPSs). In this study, a novel conjugated micro/mesoporous polymer (ST‐CMP) is developed by rational design of the organic condensation reaction of squaric acid and 1,3,5‐Tris(4‐aminophenyl)benzene, which can uniformly wrap onto carbon nanotube (CNT) as assembled nanotentacle, serving as the highly efficient sulfur electrocatalyst for LiS batteries. In this composite, the cross‐linked CNT conductive network significantly improves the electron transfer and reaction kinetics of active materials. In addition, the ST‐CMP assembled nanotentacle prevents the restacking or aggregation of ST‐CMP, favoring the exposure of active sites on ST‐CMP for efficient LiPSs chemical interaction. The abundant polymeric zwitterions in ST‐CMP with uniformly distributed positively polarized N atoms and negatively polarized O atoms can not only empower comprehensive regulation of LiPSs adsorption, but also simultaneously allow accelerated LiPSs catalytic conversion, rendering admirable redox reaction kinetics. With these merits, the obtained S/ST‐CMP@CNT composites exhibit a high initial specific capacity of 1307 mAh g−1 at 0.2 C with an ultralow capacity fading rate of 0.047% per cycle after 500 cycles at 1 C, exhibiting great potential in developing LiS batteries with decent performance.
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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".