WS<sub>2</sub> Nanosheets Modified with Ag Nanoparticles as Visible-Light Photocatalysis for Hot-Electron-Mediated Plasmonic Charge Transfer in the Degradation of Textile and Pharmaceutical Waste
Bibliographic record
Abstract
Two-dimensional (2D)-layered nanostructures integrated with plasmonic nanostructures have drawn huge attention for their potential applications in environmental remediation and water detoxification. In this study, silver (Ag) nanoparticles attached with a few layers of tungsten disulfide (WS 2 ) nanosheets were synthesized using liquid-phase exfoliation followed by a hydrothermal method for water remediation applications. The attachment of Ag nanoparticles (∼42 nm) over the WS 2 layers enables the effective band gap narrowing and reduction in recombination rate in WS 2 due to the generation of a Schottky junction formation at the interface of Ag and WS 2 layer, which is beneficial in boosting the photodegradation performance. In addition, improved visible-light absorption owing to the surface plasmon resonance effect significantly contributes to improving the photocatalytic activity of Ag-WS 2 nanohybrids. The optimized Ag-WS 2 nanohybrid (0.5 mg/mL) exhibited boosted photodegradation capability, effectively decomposing 10 μM of methylene blue (MB), 10 μM of methyl orange (MO), and 1 mg/mL of oxytetracycline hydrochloride (OTC-HCl) within 60, 40, and 60 min sequentially, which correspond to 3.2, 4.0, and 3.4 times higher photocatalytic activity than pristine WS 2 nanosheets. First-principles density function theory ensures that Ag nanoparticle attachment has a significant shift in the electronic structures and improves the effective charge transfer, which are in good agreement with the experimental results. Although few studies have examined plasmonic-WS 2 nanohybrids for photocatalysis, this study demonstrates the optimization in plasmonic content loading, in-depth exploration of charge transfer mechanisms, and their direct correlation with photodecomposition performance, which is essential to achieving advancement in this field.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".