Facile Hydrothermal Synthesis of VS<sub>2</sub>/Graphene Nanocomposites with Superior High-Rate Capability as Lithium-Ion Battery Cathodes
Bibliographic record
Abstract
In this study, a facile one-pot process for the synthesis of hierarchical VS2/graphene nanosheets (VS2/GNS) composites based on the coincident interaction of VS2 and reduced graphene oxide (rGO) sheets in the presence of cetyltrimethylammonium bromide is developed for the first time. The nanocomposites possess a hierarchical structure of 50 nm VS2 sheets in thickness homogeneously anchored on graphene. The VS2/GNS nanocomposites exhibit an impressive high-rate capability and good cyclic stability as a cathode material for Li-ion batteries, which retain 89.3% of the initial capacity 180.1 mAh g(-1) after 200 cycles at 0.2 C. Even at 20 C, the composites still deliver a high capacity of 114.2 mAh g(-1) corresponding to 62% of the low-rate capacity. Expanded studies show that VS2/GNS, as an anode material, also has a good reversible performance with 528 mAh g(-1) capacity after 100 cycles at 200 mA g(-1). The excellent electrochemical performance of the composites for reversible Li+ storage should be attributed to the exceptional interaction between VS2 and GNS that enabled fast electron transport between graphene and VS2, facile Li-ion diffusion within the electrode. Moreover, GNS provides a topological and structural template for the nucleation and growth of two-dimensional VS2 nanosheets and acted as buffer matrix to relieve the volume expansion/contraction of VS2 during the electrochemical charge/discharge, facilitating improved cycling stability. The VS2/GNS composites may be promising electrode materials for the next generation of rechargeable lithium ion batteries.
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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".