Sepiolite-Supported WS2 Nanosheets for Synergistically Promoting Photocatalytic Rhodamine B Degradation
Bibliographic record
Abstract
Pristine tungsten disulfide (WS2) nanosheets are extremely prone to agglomeration, leading to blocked active sites and the decrease of catalytic activity. In this work, highly dispersed WS2 nanosheets were fabricated via a one-step in situ solvothermal method, using sepiolite nanofibers as a functional carrier. The ammonium tetrathiotungstate was adopted as W and S precursors, and N,N-dimethylformamide could provide a neutral reaction environment. The electron microscope analysis revealed that the WS2 nanosheets were stacked compactly in the shape of irregular plates, while they were uniformly grown on the surface of sepiolite nanofibers. Meanwhile, the BET measurement confirmed that the as-prepared composite has a larger specific surface area and is more mesoporous than the pure WS2. Due to the improved dispersion of WS2 and the synergistic effect between WS2 and the mesoporous sepiolite mineral which significantly facilitated the mass transport, the WS2/sepiolite composite exhibited ca. 2.6 times the photocatalytic efficiency of the pure WS2 for rhodamine B degradation. This work provides a potential method for low-cost batch preparation of high-quality 2D materials via assembling on natural materials.
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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".