Green Synthesis of Ag Nanoparticles for Plasmon-Assisted Photocatalytic Degradation of Methylene Blue
Bibliographic record
Abstract
Green synthesis of metal nanoparticles has been gaining great interest due to the increasing need for eco-friendly manufacturing and application of nanomaterials. Metal nanoparticles, especially silver nanoparticles, are widely used in water treatments and as environmental remedy. Here we present a method to synthesize silver nanoparticles at room temperature using green tea extract under visible light irradiation, along with an application for enhanced photocatalytic degradation on methylene blue (MB) dye. Ag nanoparticles were synthesized under different photoreduction times and then further characterized. The photocatalytic rate of synthesized nanoparticles was also investigated and compared with TiO2 nanowires under UV and visible light irradiations. The results showed that Ag nanoparticles can directly degrade MB dye through plasmonic excitation and electron transferring under visible light, and Ag nanoparticles can further enhance TiO2 photonic degradation by enhanced e-h separation with UV and/or a wide band light, including UV light. Ag nanoparticles under visible light photoreduction for 0.5 h presented better behavior for two kinds of plasmonic enhanced photodegradation; the average size of the nanoparticles is about 30 nm. Therefore, the green synthesized Ag nanoparticles exhibit promising prospects in chemical and biological pollutant treatment.
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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".