Photocatalytic degradation of aqueous methyl orange using a novel Ag/TiO2/Fe2O3 photocatalyst prepared by UV-assisted thermal synthesis
Bibliographic record
Abstract
ABSTRACT The aim of this study is to develop a novel photocatalyst doped by a noble metal onto two semiconductor oxides for the applications of photocatalytic wastewater treatment. An alternative preparation method of UV-assisted thermal synthesis is used to synthesize this photocatalyst. The two photocatalysts, Fe 2 O 3 and TiO 2 , were combined with different mass ratios of Fe:TiO 2 followed by doping the synthesized Fe 2 O 3 /TiO 2 with a silver ion with different mass ratios of Ag:TiO 2 to produce the novel photocatalyst Ag/TiO 2 /Fe 2 O 3 . Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy is employed to evaluate the formation, the morphology, and the elemental analysis of the composite photocatalyst. The phase and the crystal structure of the new composites are investigated by X-ray diffraction. The specific surface area of all photocatalysts is analyzed based on the Brunauer–Emmett–Teller method by adsorption of nitrogen gas at 77 K. Removal of methyl orange is used to examine the photocatalytic degradation efficiency of the photocatalysts. Results indicate that the specific surface area of the Ag/TiO 2 /Fe 2 O 3 photocatalyst was significantly increased and the photocatalytic activity of the new photocatalyst was enhanced for the degradation of aqueous methyl orange. The new composite has the potential to absorb light at a higher wavelength in the visible region where the redshift occurs. By applying the Ag/TiO 2 /Fe 2 O 3 photocatalyst with a mass ratio of 0.01 Ag:TiO 2 and 0.01 Fe:TiO 2 , the maximum degradation of methyl orange reaches to 89.5% and 95.5% under UV-A radiation and natural sunlight, respectively. Diffractograms confirm that the substitutions occur in the crystal lattice of the main photocatalyst.
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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".