Photocatalytic degradation of red dye: optimisation using RSM
Bibliographic record
Abstract
The aim of this research was to apply experimental design methodology to the optimisation of the photocatalytic degradation of red dye present in waste water. This paper reports the broad range of several photocatalyst composite efficiencies for photocatalytic degradation of red dye in waste water samples from textile industries. Three composites, which were graphitic carbon nitride (g-carbon nitride (C3N4))/zinc oxide (ZnO), g-carbon nitride/titanium dioxide (TiO2) and zinc oxide nanoparticles, were prepared using different precursors (zinc chloride, zinc nitrate etc.). The catalysts were characterised by Fourier transform infrared spectroscopy, scanning electron microscopy and transmission electron microscopy. The catalytic performance of different photocatalysts was tested using different variables such as dosage, stirring speed, composite structure and dye concentration. Two methods were used to optimise the operating conditions. The results indicate that the photocatalytic degradation of the red dye solution at the resulting optimum condition, which was found to be after 90 min of ultraviolet irradiation, can reach 68, 57 and 53% when using zinc oxide nanoparticles, titanium dioxide/carbon nitride and zinc oxide/carbon nitride, respectively, as catalysts.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".