Degradation of pharmaceutical contaminants in water by an advanced plasma treatment
Bibliographic record
Abstract
ABSTRACT Contamination of water bodies with pharmaceutical compounds and their adverse effects on human and wildlife has been a source of concern for many societies. The need for more effective water treatment processes has been felt to eliminate these contaminants from water. In this work, single electrode non-thermal plasma in a floating electrode streamer corona discharge (FESCD) system is utilized for effective degradation of antibiotic ampicillin and non-steroidal anti-inflammatory drug (NSAID) ibuprofen. It was found that, after 3 h of plasma treatment, 100% of ampicillin and 90% of ibuprofen was degraded in the solution. The energy yield (the amount of degraded contaminants by consuming 1 kWh of energy) was calculated to be 0.12–0.13 g/kWh. Total Organic Carbon (TOC) measurements showed 20% and 60% mineralization for ampicillin and ibuprofen, respectively. Hydroxyl radicals were found to play a major role in the degradation of both contaminants. Furthermore, in both cases, the formation of oxygenated by products implied a possible role of ozone molecules in the degradation mechanism. Finally, Fluorescence Excitation-Emission Matrix (FEEM) was utilized to track the degradation of the contaminants in the tap water through the change in fluorescence properties and the connections between FEEM signals and the identified degradation by products were outlined.
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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) | 0.001 | 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".