Kinetic study of p-nitrophenol degradation with zinc oxide nanoparticles prepared by sol–gel methods
Bibliographic record
Abstract
Three zinc oxides (ZnO A, B, C) with similar spherical morphology but different sizes are synthesized by sol–gel methods. A kinetic study is carried out on the photocatalytic activity of these three ZnO, through the degradation of p -nitrophenol (PNP). A mathematical model is developed and the rate constants of the three catalysts are determined. To understand the parameters influencing the kinetics, the catalysts are reduced to the surface of an isolated particle (assuming perfect dispersion conditions where all catalytic active sites are available) whose size is determined by X-ray diffraction (XRD) and transmission electron microscopy (TEM). However, by considering the real case (not a perfect dispersion), it appears that the size of the aggregates induced by the synthesis methods play a more important role in the catalytic activity of the three ZnO samples than defects. A discussion on the formation of these aggregates highlights the importance of the synthesis parameters, like the solvent or the surfactant used to obtain a high dispersion. The dispersion plays a crucial role in photocatalytic efficiency, with kinetics three times higher for the catalyst with the best dispersion. Also, as shown by photoluminescence and X-ray photoelectron spectroscopy (XPS), the type and amount of defects play an important role in the photocatalytic performance. ZnO A and B show a defect peak at 620 nm whereas ZnO C shows a defect peak at 680 nm, suggesting a different type of defect on the surface of the catalyst that reduces the photocatalytic performance. Electron paramagnetic resonance (EPR) measurements are performed to identify the type of radicals involved in PNP degradation. The results show that the catalyst with the best dispersion produces the highest amount of hydroxyl radicals. Finally, photoluminescence and XPS analyses underline the type and the amount of defects for the three photocatalysts.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".