Modeling of Degradation of Diazo Dye in Swirl-Flow Photocatalytic Reactor: Response Surface Approach
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
Photocatalytic degradation of Direct Blue 15 (DB15), an azo dye, was studied using a swirl-flow monolithic reactor under UV irradiation. The degradation reactions were carried out to investigate effects of initial dye concentration, catalyst loading, and light intensity at an optimal pH. The experiments were designed and mathematically modelled by CCD-RSM (central composite design-response surface methodology) approach. It was found that the selected parameters significantly affect DB15 degradation. In terms of the linear term, catalyst loading and light intensity had a synergistic effect, while dye concentration registered the opposite effect. Strong interaction was observed between catalyst loading and both light intensity and initial dye concentration compared with the interaction of light intensity and initial dye concentration. Based on the experimental results, a quadratic model was developed to predict the percentage removal of DB15. The predicted values of the model were in good agreement with the experimental values (R2 = 0.987), indicating the model fits well for the parameter space for which experiments were performed. According to diagnostic plots, the model credibility was valid because its residuals were distributed normally and exhibited a random pattern based on their examination versus the predicted values. The results revealed that the initial dye concentration and catalyst concentration have a significant effect on the mineralization time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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