Sono-Photocatalytic Activity of Cloisite 30B/ZnO/Ag2O Nanocomposite for the Simultaneous Degradation of Crystal Violet and Methylene Blue Dyes in Aqueous Media
Bibliographic record
Abstract
A new nanocomposite based on Cloisite 30B clay modified with ZnO and Ag2O nanoparticles (Cloisite 30B/ZnO/Ag2O) was synthesized as an effective catalyst in the sono-photocatalytic process of crystal violet (CV) and methylene blue (MB) dyes simultaneously. The characteristics and catalytic activity of Cloisite 30B/ZnO/Ag2O nanocomposite were investigated under different conditions. The specific active surface for Cloisite 30B/ZnO/Ag2O nanocomposite was 18.29 m2/g. Additionally, the catalytic activity showed that Cloisite 30B/ZnO/Ag2O nanocomposite (CV: 99.21%, MB: 98.43%) compared to Cloisite 30B/Ag2O (CV: 85.38%, MB: 83.62%) and Ag2O (CV: 68.21%, MB: 66.41%) has more catalytic activity. The catalytic activity of Cloisite 30B/ZnO/Ag2O using the sono-photocatalytic process had the maximum efficiency (CV: 99.21%, MB: 98.43%) at pH 8, time of 50 min, amount of 40 mM H2O2, catalyst dose of 0.5 g/L, and the concentration of ‘CV + MB’ of 5 mg/L. The catalyst can be reused in the sono-photocatalytic process for up to six steps. According to the results, •OH and h+ were effective in the degradation of the desired dyes using the desired method. Data followed the pseudo-first-order kinetic model. The method used in this research is an efficient and promising method to remove dyes from wastewater.
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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".