Engineering the Conductive Network of Metal Oxide‐Based Sulfur Cathode toward Efficient and Longevous Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract The rational design of sulfur cathode structure to suppress shuttling behaviors and expedite the conversion kinetics of polysulfides plays an essential role for the practical implementation of lithium–sulfur (Li–S) batteries. In this work, a unique consecutive and oxygen‐deficient niobium oxide (Nb2O5−x) framework featured with 3D ordered macroporous (3DOM) architecture and carbon nanotubes (CNTs) embedding is developed, which serves as a high‐performance sulfur immobilizer and catalytic promoter for polysulfide conversion. The 3DOM architecture affords a robust porous and open framework that favors electrolyte infiltration for fast ion/mass transfer, as well as interface exposure for massive host–guest interactions. More importantly, CNTs are designed as “antennae” embedded within the Nb2O5−x skeleton, which not only contributes to a highly conductive framework but also intensifies the oxygen deficiency with enhanced sulfur immobilization and reaction catalyzation. Benefiting from these advanced features, Li–S cells based on S‐Nb2O5−x/CNTs cathode achieve excellent cyclability with a high capacity retention of 847 mAh g−1 after 500 cycles and remarkable rate capability with 741 mAh g−1 at 5 C. Moreover, a high areal capacity of 6.07 mAh cm−2 can also be achieved under a high sulfur loading of 6 mg cm−2, illustrating great potential in the development of practical 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) | 0.000 | 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".