Solar degradation of diclofenac using Eosin-Y-activated TiO<sub>2</sub>: cost estimation, process optimization and parameter interaction study
Bibliographic record
Abstract
Diclofenac (DCF), a widely used non-steroidal anti-inflammatory drug, is a commonly detected substance that readily accumulates in tissues of aquatic fish and poses a threat to wildlife and freshwater quality. Advanced Oxidation Processes have been employed as an alternative due to the inadequacy of conventional treatment methods of trace contaminants. This study utilized an innovative method of solar-activation of TiO2 using Eosin-Y dye for the degradation of DCF. Furthermore, the study incorporated a central composite design (CCD) to optimize the dye concentration and estimated the cost for the present process. Optimized parameters for light intensity (750 mW/cm2), Eosin-Y dye concentration (2 mg/L), TiO2 loading (37.5 mg/cm2) and DCF concentration (25 mg/L) were determined through a CCD. The optimized parameters convey a DCF degradation rate of 40% and 49% for 2 ppm (low range) and 4 ppm (high range) dye concentrations, respectively, for a 5-minute reaction time. Cost estimation for the materials used was for the current process was also performed. It was determined that the additional cost of using 4 ppm instead of 2 ppm to achieve only 10% more DCF degradation is not warranted and would require additional treatment to remove subsequently formed halogenated compounds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".