Facile Hydrothermal Synthesis of VS<sub>2</sub>/Graphene Nanocomposites with Superior High-Rate Capability as Lithium-Ion Battery Cathodes
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it