Photocatalytic degradation of dimethyl sulphoxide by CdS/TiO<sub>2</sub> core/shell catalyst: A novel measurement method
Bibliographic record
Abstract
Abstract In the present research, the photocatalytic degradation of dimethyl sulphoxide (DMSO) by means of CdS/TiO2 core/shell nanocomposite was investigated and modelled for the direct measurement of degradation rate. Instead of common measurement methods, liquid freezing point measurement was used in order to determine the rate of DMSO decomposition. To model the photocatalytic behaviour, two empirical‐statistical equations based on photocatalytic retention time, amount of catalyst and pollutant were introduced. The effect of parameters such as retention time, amount of catalyst and pollutant were studied by statistical methods. The design of experiments, acquisition and optimization of statistical models was performed by response surface methodology (RSM) through central composite design (CCD). The rates of disappearance fitted the Langmuir‐Hinshelwood kinetics model and the parameter k was determined to be up to 0.0105 per minute for a low concentration and 0.0073 per minute for a high concentration of DMSO. In addition, more than 85% degradation of 1% DMSO was attained by 8% catalyst in 150 minutes. Finally, the accuracy and consistency of the statistical models was verified by the HPLC method and ~ ± 2.7% difference was observed. The other results indicate that the CdS/TiO2 nano photocatalyst can efficiently remove dimethyl sulphoxide from wastewater under visible light irradiation.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".