Elemental Sulfur Nanoparticles Chemically Boost the Sodium Storage Performance of MoS<sub>2</sub>/rGO Anodes
Bibliographic record
Abstract
Abstract The critical role of sulfur nanoparticles in stabilizing MoS2 supported on reduced graphene oxide as anode material for sodium‐ion batteries is discovered. The MoS2 supported on reduced graphene oxide decorated with sulfur particles (∼50 nm) is in‐situ synthesized using an ammonium molybdate/graphene oxide preform and sublimed sulfur through a facile chemical vapor deposition process in a tube furnace with 2 temperature‐controlled zones. Although the sulfur particles show no positive effect when the material is tested as anode for Li‐ion batteries, they significantly improve the Na storage performance in terms of both, total specific capacity and cycle life. A stable high capacity of 580 mAh g−1 and an extremely low capacity fade of 94 μAh g−1 cycle−1 make the designed assembly one of the best‐performing MoS2‐based anode materials for sodium‐ion batteries so far. The post‐cycling analysis reveals that the elemental sulfur nanoparticles play two roles: during the intercalation of Na in‐between the layers of MoS2 (above 1.0 V), they function as blockers and inhibit the aggregation of MoS2; in the conversion reaction stage, the sulfur nanoparticles chemically participate in the Na storage process by forming Na2S5‐rich compounds, which eventually improve the reversibility of the conversion reaction and thereafter the cycling 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".