Amorphous MoS<sub>3</sub> Infiltrated with Carbon Nanotubes as an Advanced Anode Material of Sodium‐Ion Batteries with Large Gravimetric, Areal, and Volumetric Capacities
Bibliographic record
Abstract
The search for earth‐abundant and high‐performance electrode materials for sodium‐ion batteries represents an important challenge to current battery research. 2D transition metal dichalcogenides, particularly MoS2, have attracted increasing attention recently, but few of them so far have been able to meet expectations. In this study, it is demonstrated that another phase of molybdenum sulfide—amorphous chain‐like MoS3—can be a better choice as the anode material of sodium‐ion batteries. Highly compact MoS3 particles infiltrated with carbon nanotubes are prepared via the facile acid precipitation method in ethylene glycol. Compared to crystalline MoS2, the resultant amorphous MoS3 not only exhibits impressive gravimetric performance—featuring excellent specific capacity (≈615 mA h g−1), rate capability (235 mA h g−1 at 20 A g−1), and cycling stability but also shows exceptional volumetric capacity of ≈1000 mA h cm−3 and an areal capacity of >6.0 mA h cm−2 at very high areal loadings of active materials (up to 12 mg cm−2). The experimental results are supported by density functional theory simulations showing that the 1D chains of MoS3 can facilitate the adsorption and diffusion of Na+ ions. At last, it is demonstrated that the MoS3 anode can be paired with an Na3V2(PO4)3 cathode to afford full cells with great capacity and 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) | 0.000 | 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".