Visible-Solar-Light-Driven Photocatalytic Degradation of Phenol with Dye-Sensitized TiO<sub>2</sub>: Parametric and Kinetic Study
Bibliographic record
Abstract
Phenol degradation with TiO 2 photocatalyst under UV light is known to be an effective method. Under solar radiation, however, this approach does not receive adequate photons for catalyst activation, as the solar spectrum comprises mostly visible light (46%). In this study, we applied the dye-sensitization technique to prepare visible-light-active catalyst and used it under visible solar light generated from a solar simulator with a UV cutoff filter (λ > 420 nm) for phenol degradation. Eosin Y dye was used as a sensitizer for the TiO 2 catalyst with a very low level of platinum as a cocatalyst. Triethanolamine was used as a sacrificial electron donor. Parametric studies were performed for the catalyst loading, initial triethnolamine concentration, initial phenol concentration, platinum content on TiO 2, solution pH, and visible light intensity. About 93% degradation of 40 ppm phenol solution was achieved within 90 min using Eosin Y–TiO 2 /Pt photocatalyst under optimum conditions (pH 7.0, catalyst loading of 0.8 g/L, triethnolamine concentration of 0.2 M, 0.5% Pt loading on TiO 2, visible solar light intensity of 100 mW/cm 2 ). The kinetic rate constant and adsorption equilibrium constant were determined, and a Langmuir–Hinshelwood-type equation was proposed to describe phenol degradation on TiO 2 at different visible light intensities. The model equation was found to predict the experimental results quite well.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".