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 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) | 0.000 | 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 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".