Photocatalytic degradation of phenazopyridine contaminant in soil with direct solar light
Bibliographic record
Abstract
Photocatalytic degradation of waste pharmaceutics, with solar radiation, is described here as a feasible method to purify pre-contaminated soils. Phenazopyridine has been used as a model soil contaminant. Two different nano-size powders have been first examined as catalysts, namely commercial TiO2 (anatase) and commercial ZnO. As the ZnO showed higher catalytic efficiency, the study was then focused on it. The commercial ZnO powder was then compared with lab-prepared ZnO powder, and the latter shows relatively higher efficiency. The ZnO was used in two different ways. In one way, dry ZnO catalyst powder was spread onto the soil, while in the other way the ZnO was sprayed onto the soil surface by a wet spray method. The spray technique shows slightly higher efficiency, in addition to being easier to apply at future large scale. Depending on conditions and type of photocatalyst used, up to 90% contaminant removal can be achieved in 30 min. In case of photocatalysis experiments, the reacted contaminant molecules undergo complete degradation with no detectable side reaction organic products. Possible evaporation or escape of organic contaminant, or other possibly resulting organics, is ruled out by a series of control experiments. Photodegradation process takes place only at the catalytic sites on the soil surface, where contaminant molecules that diffuse from the soil bulk are completely degraded. Other useful organisms inside the soil are not affected as they are kept away from catalyst sites. A plausible mechanism is proposed for the degradation process.
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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) | 0.000 | 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".