Photocatalytic Degradation of Phenol and Phenol Derivatives Using a Nano-TiO2 Catalyst: Integrating Quantitative and Qualitative Factors Using Response Surface Methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the toxicity effects and endocrine disrupting properties of phenolic compounds, their removal from water and wastewater has gained widespread global attention. In this study, the photocatalytic degradation of phenolic compounds in the presence of titanium dioxide (TiO2) nano-particles and UV light was investigated. A full factorial design consisting of three factors at three levels was used to examine the effect of particle size, temperature and reactant type on the apparent degradation rate constant. The individual effect of TiO2 particle size (5, 10 and 32 nm), temperature (23, 30 and 37 °C) and reactant type (phenol, o-cresol and m-cresol) on the apparent degradation rate constant was determined. A regression model was developed to relate the apparent degradation constant to the various factors. The largest photocatalytic activity was observed at an optimum TiO2 particle size of 10 nm for all reactants. The apparent degradation rate constant trend was as follows: o-cresol > m-cresol > phenol. The ANOVA data indicated no significant interaction between the experimental factors. The lowest activation energy was observed for o-cresol degradation using 5-nm TiO2 particles. A maximum degradation rate constant of 0.0138 min−1 was recorded for o-cresol at 37 °C and a TiO2 particle size of 13 nm at a D-optimality value of approximately 0.98. The response model adequately related the apparent degradation rate constant to the factors within the range of factors under consideration.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it