Simultaneous degradation of multiple micropollutants in flowing water by mild and strong microbubble-enhanced cold plasma activation
Bibliographic record
Abstract
Elimination of stubborn organic micropollutants from water is crucial for bioaccumulation prevention and ecosystem protection. Cold plasma activation technology is a clean, sustainable, and highly effective approach to the degradation of micropollutants and pathogens in contaminated water. In this study, we focus on understanding the processes of simultaneous degradation of multiple micropollutants (8 types at maximum) in flowing water by recently developed microbubble-enhanced cold plasma activation (MB-CPA) technology. The degradation of the micropollutants with the treatment time was analyzed by using ultrahigh performance liquid chromatography coupled to a triple quadrupole mass spectrometer (UHPLC-QQQ-MS). We found that the degradation efficiencies of all compounds increase rapidly under strong activation conditions that can lead to above 98 % removal of a model compound. After long treatment duration or at a fast flow rate, the removal efficiency was sufficiently high for all compounds that were either easy or hard to degrade. The large variation in degradation efficiencies was present under mild activation conditions. The electron spinning resonance measurements reveal a greater abundance of hydroxyl radicals in treated synthetic river water and pure water, highlighting the effects of water matrix on the degradation efficiency. The understanding from this work may help to design the activation process and minimize the energy consumption for the simultaneous elimination of pollutants in diverse and complex water bodies by cold plasma technology. • Microbubble enhanced cold plasma activation with tunable activation level. • Portable and solar-driven cold plasma activation set up (< 80 W). • Simultaneous removal of 8 micropollutants within 35 min in complex water matrices. • Investigating kinetics and mechanisms of degradation/disinfection with interference. • Establishing model compound-based degradation benchmark for versatile micropollutants.
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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 | 0.000 | 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 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".