Individual and interaction effects of operating parameters on the photocatalytic degradation under visible light illumination: Response surface methodological approach
Bibliographic record
Abstract
ABSTRACT In this study, for the first time a statistical analysis based on the response surface methodology (RSM) was employed to investigate individual and interaction effects of key operating parameters of the photocatalytic degradation under visible‐light irradiation using Ag‐S/PEG/TiO2. Ag‐S/PEG/TiO2 is a visible‐light‐driven photocatalyst and was synthesized (based on earlier research) by co‐doping of TiO2 with silver and sulphur and addition of polyethylene glycol (as a reagent template). In addition, the model pollutant was methylene orange (MO) and the studied operating parameters included the photocatalyst loading, initial concentration of the pollutant, and pH of the solution. The statistics‐based experimental design and RSM was utilized to find a quadratic model as a functional relationship between the degradation efficiency and the three operating parameters. The regression analysis with R2 value of 0.9678 showed a close fit between the model prediction and experimental data of the degradation efficiency. The analysis of variance based on the model indicated that pH of the solution was the most influential factor, while the two other operating parameters were also significant. The efficiency of MO degradation reached 94.0 % under the optimum conditions (i.e. photocatalyst loading of 1.20 g/L, MO concentration of 5 mg/L, and pH of 2).
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".