Hierarchical Nanocomposite of Hollow N-Doped Carbon Spheres Decorated with Ultrathin WS<sub>2</sub> Nanosheets for High-Performance Lithium-Ion Battery Anode
Bibliographic record
Abstract
Hierarchical nanocomposite of ultrathin WS2 nanosheets uniformly attached on the surface of hollow nitrogen-doped carbon spheres (WS2@HNCSs) were successfully fabricated via a facile synthesis strategy. When evaluated as an anode material for LIBs, the hierarchical WS2@HNCSs exhibit a high specific capacity of 801.4 mA h g(-1) at 0.1 A g(-1), excellent rate capability (545.6 mA h g(-1) at a high current density of 2 A g(-1)), and great cycling stability with a capacity retention of 95.8% after 150 cycles at 0.5 A g(-1). The Li-ion storage properties of our WS2@HNCSs nanocomposite are much better than those of the previously most reported WS2-based anode materials. The impressive electrochemical performance is attributed to the robust nanostructure and the favorable synergistic effect between the ultrathin (3-5 layers) WS2 nanosheets and the highly conductive hollow N-doped carbon spheres. The hierarchical hybrid can simultaneously facilitate fast electron/ion transfer, effectively accommodate mechanical stress from cycling, restrain agglomeration, and enable full utilization of the active materials. These characteristics make WS2@HNCSs a promising anode material for high-performance LIBs.
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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".