Heterogeneous WS<sub><i>x</i></sub>/WO<sub>3</sub> Thorn-Bush Nanofiber Electrodes for Sodium-Ion Batteries
Bibliographic record
Abstract
Heterogeneous electrode materials with hierarchical architectures promise to enable considerable improvement in future energy storage devices. In this study, we report on a tailored synthetic strategy used to create heterogeneous tungsten sulfide/oxide core-shell nanofiber materials with vertically and randomly aligned thorn-bush features, and we evaluate them as potential anode materials for high-performance Na-ion batteries. The WSx (2 ≤ x ≤ 3, amorphous WS3 and crystalline WS2) nanofiber is successfully prepared by electrospinning and subsequent calcination in a reducing atmosphere. To prevent capacity degradation of the WSx anodes originating from sulfur dissolution, a facile post-thermal treatment in air is applied to form an oxide passivation surface. Interestingly, WO3 thorn bundles are randomly grown on the nanofiber stem, resulting from the surface conversion. We elucidate the evolving morphological and structural features of the nanofibers during post-thermal treatment. The heterogeneous thorn-bush nanofiber electrodes deliver a high second discharge capacity of 791 mAh g(-1) and improved cycle performance for 100 cycles compared to the pristine WSx nanofiber. We show that this hierarchical design is effective in reducing sulfur dissolution, as shown by cycling analysis with counter Na electrodes.
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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".