Effect of Sonication Pre-treatment of TiO2 Catalyst for Photo-Degradation of Acid Orange 7 Azo Dye
Bibliographic record
Abstract
In this study, Acid Orange 7 azo dye was degraded on TiO2 catalyst layer illuminated with ultra-violet light. The TiO2 suspension had prior been sonicated at 20 kHz before electrophoretic deposition on smooth stainless steel surfaces, with the aim of increasing the efficiency of azo dyes degradation. The effect of sonication on the TiO2 suspension of 10 g.L-1 and electrophoretic deposition loading to layers on surfaces was studied. Morphological properties of the electrophoretic layers from two different suspensions, Alpha and Sigma TiO2 were characterized by Scanning Electron Microscopy to establish the specific surface properties, particle loading and crystalline sizes. A four–position reactor was used for dye degradation experiments under Ultra Violet light at 355 nm wavelength. The degradation of the azo dye was monitored at 30minutes interval for a total of 2 hours using Ultra Violet-Visible Spectrophotometer at λ = 485 nm. It was found out that 60% of dye degradation was achieved after 120 minutes without sonication pretreatment. Sonication pre-treatment resulted in 71.42% increase on the rate of photo-degradation, at a loading of 0.32 mg/cm2. Particulate layers coated with Alpha TiO2 showed more photoactivity compared to Sigma TiO2. Scanning Electron Microscopy indicated that Alpha TiO2 had  50 m2/g surface area and  28 nm crystal size compared to Sigma TiO2 with 10 m2/g and 169 nm crystal size. This study shows that sonication pretreatment of Alpha TiO2 /UV light system is most effective in photo-degrading Acid Orange 7 dye.
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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 | 0.000 | 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 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".