The Dual‐Play of 3D Conductive Scaffold Embedded with Co, N Codoped Hollow Polyhedra toward High‐Performance Li–S Full Cell
Bibliographic record
Abstract
Abstract Lithium–sulfur (Li–S) batteries are one of the most promising battery technologies to support the fast‐expanding electrical vehicle and large‐scale energy storage market. However, several intrinsic and intractable obstacles are still impeding the practical implementation of Li–S batteries, which calls for advances in both sulfur and lithium electrodes. Herein, a 3D conductive scaffold is developed with hollow carbon polyhedra embedded on tubular carbon fabric (HPTCF) as self‐standing matrix for both improved sulfur and lithium electrodes. Attributed to the high conductivity, abundant active interfaces, and favorable surface functionalization of HPTCF, reliable sulfur and lithium electrochemistry are simultaneously achieved. The results show an outstanding cyclability with a minimum capacity decay of 0.018% per cycle over 600 cycles in half cell, while the combined cathodic and anodic improvements further contribute to an excellent Li–S full cell performance with high capacity retention of 3.1 mAh cm−2 at 200th cycle and superb rate performance of 2.5 mAh cm−2 at 4 C rate under a reasonably high sulfur loading of 4 mg cm−2. This work offers exemplary material engineering that concurrently and effectively tackles the problems in sulfur and lithium electrodes. This approach has great potential to promote the practical application of 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) | 0.001 | 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".