Synthesis, characterization, and photocatalytic activity of silver and zinc co-doped TiO<sub>2</sub> nanoparticle for photodegradation of methyl orange dye in aqueous solution
Bibliographic record
Abstract
In this study, TiO2 nanocrystals, 1 mol% Ag-doped TiO2, and 1 mol% Ag and 0.6 mol% Zn co-doped TiO2 powders were synthesized by the sol–gel route. Their photocatalytic activities on methyl orange dye under visible irradiation were investigated. The powders were characterized by X-ray diffraction (XRD), UV–visible spectroscopy (UV–vis), Brunauer–Emmett–Teller (BET), and Fourier transform infrared spectroscopy (FTIR). The XRD results revealed the presence of a rutile phase with an average crystallite size of 9 and 11 nm. The UV–vis spectra showed a red-shift towards a longer wavelength with the corresponding decrease in band gap from 2.9 to 2.5 eV. The BET surface areas of the nanoparticles ranged from 4.7 to 11.8 m2 g−1 with an average pore size between 18.9 and 56.6 nm. The Ag-doped TiO2 has the largest surface area of 11.8 m2 g−1, whereas the Ag–Zn co-doped TiO2 was found to have the highest pore size and volume. The absorption bands at 750–500 cm−1 were attributed to the –O–Ti–O– bond in the TiO2 lattice. The photocatalytic efficiency was highest at an optimum pH of 4.1 for Ag–Zn co-doped TiO2. The results confirmed that Ag-doped and Ag–Zn co-doped TiO2 were more effective than pure TiO2. The kinetic data were fitted into a pseudo first-order equation using a Langmuir–Hinshelwood kinetic model.
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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".