Ultrasound-assisted advanced oxidation processes for water decontamination
Bibliographic record
Abstract
The study reflects a part of my experience in sonochemistry and ultrasound-assisted advanced oxidation processes (AOPs) acquired during the last fifteen years with my research team. The data discussed were selected from studies with azo dyes, endocrine disrupting compounds and analgesic/anti-inflammatory pharmaceuticals, which are all classified as “hazardous” or “emerging” contaminants. The research focused on their treatability by ultrasound (US) and AOPs with emphasis on the mineralization of organic carbon. Some of the highlights as pointed out in the manuscript are: i) ultrasound is capable of partially or completely oxidizing the above contaminant groups if the operating conditions are properly selected and optimized, but incapable of mineralizing them; ii) the mechanism of degradation in homogeneous solutions is OH-mediated oxidation in the bulk solution or at the bubble-liquid interface, depending on the molecular properties of the contaminant, the applied frequency and pH; iii) US-assisted AOPs such as ozonation, UV/peroxide, Fenton and UV/Fenton are substantially more effective than ultrasound alone, particularly for the mineralization process; iv) catalytic processes involving TiO 2 , alumina and zero-valent iron and assisted by ultrasound are promising options not only for the destruction of the parent compounds, but also for the mineralization of their oxidation byproducts. The degradation reactions in heterogeneous solutions take place mostly at the catalyst surface despite the high-water solubility of the compounds; v) sonolytic modification of the above catalysts to reduce their particle size (to nano-levels) or to decorate the surface with metallic nanoparticles increases the catalytic activity under sonolysis, photolysis and both, and improves the stability of the catalyst.
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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) | 0.002 | 0.001 |
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".