Photocatalytic Degradation of Paraquat Herbicide Using a Fixed Bed Reactor Containing TiO<sub>2</sub> Nanoparticles Coated onto <i>β-SiC</i> Alveolar Foams
Bibliographic record
Abstract
Photocatalytic degradation of paraquat (PQ) aqueous solutions was studied in a fixed bed photoreactor under UV irradiation at 368 nm. This contained β-SiC alveolar foams coated with TiO2 P25 by dip-coating method. SEM analyses revealed that the surface of the film did not exhibit cracks in the presence of TTIP as a binder in the TiO2 P25 suspension. The following parameters were studied in continuous mode operation: the flow rate in the reactor, the initial concentration of the paraquat, the pH of the solution, the weight of photocatalytic material with the number of foams in the reactor and the weight of the catalyst deposited onto the support. The results showed that by working under optimal operating conditions at natural pH (pH = 6.7), low paraquat (Co = 10 ppm), and flow (26 mL/min), we recorded approximately (43.16 ± 1.00)% oxidation of paraquat and a decrease in total organic carbon (TOC) of (27.13 ± 1.00)% after about 70 minutes. The apparent rate constant is in the order of (0.0656 ± 0.0010) min-1. In addition, by increasing the amount of β-SiC foams coated with TiO2, we improve the degradation of paraquat in the same order. The study of aging of the material showed its stability over time. However, photocatalytic activity was limited after 20 minutes of UV irradiation due to the limitation of the diffusion of the paraquat molecules towards the surface of the photocatalyst. As an outcome, we obtained an efficient TiO2/β-SiC material for photocatalytic degradation of organic compounds in water.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".