Modeling RSM of photocatalytic treatment of Acid Red 18 pollutant using ZnO–Cr nano-photocatalyst, kinetic studies, and energy management
Bibliographic record
Abstract
The ZnO–Cr nano-photocatalyst was synthesized using a microwave-assisted solution combustion method and applied for the photodegradation of the organic pollutant Acid Red 18 (AR18). The synthesized nano-photocatalyst was characterized by XRD, FESEM, EDX, and FTIR methods. To reach the optimal condition of the treatment, the response surface methodology was used in the central composite design model. The amount of nano-photocatalyst, pH of the solution, and initial concentration of the pollutant were optimized. The polynomial 3-degree model was fitted to the photodegradation data, and the correlation coefficients of the model showed an interaction between the parameters. Optimization of the polynomial model for pollutant treatment was investigated under the same conditions, and the comparison of the observed and predicted treatment models showed a low difference in decolorization. The intermediates were identified by liquid chromatography/mass spectrometry. A kinetic study showed that the first-order kinetic constant for the degradation of pollutant concentrations from 10 to 30 mg L−1 changed from 0.0178 to 0.0058 min–1. Finally, economic evaluation and energy management of the process showed that the decolorization process was more economical at low pollutant concentrations.
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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 | 0.000 |
| 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 | 0.001 | 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".