Enhanced Reversible Sodium‐Ion Intercalation by Synergistic Coupling of Few‐Layered MoS<sub>2</sub> and S‐Doped Graphene
Bibliographic record
Abstract
Abstract Sodium‐ion batteries (SIBs) are regarded as the best alternative to lithium‐ion batteries due to their low cost and similar Na+ insertion chemistry. It is still challenging but greatly desired to design and develop novel electrode materials with high reversible capacity, long cycling life, and good rate capability toward high‐performance SIBs. This work demonstrates an innovative design strategy and a development of few‐layered molybdenum disulfide/sulfur‐doped graphene nanosheets (MoS2/SG) composites as the SIB anode material providing a high specific capacity of 587 mA h g−1 calculated based on the total composite mass and an extremely long cycling stability over 1000 cycles at a current density of 1.0 A g−1 with a high capacity retention of ≈85%. Systematic characterizations reveal that the outstanding performance is mainly attributed to the unique and robust composite architecture where few‐layered MoS2 and S‐doped graphene are intimately bridged at the hetero‐interface through a synergistic coupling effect via the covalently doped S atoms. The design strategy and mechanism understanding at the molecular level outlined here can be readily applied to other layered transition metal oxides for SIBs anode and play a key role in contributing to the development of high‐performance SIBs.
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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".