Degradation of methylene blue by pulsed nanosecond discharge in Ar, O2, and N2 gaseous bubbles in water: Evaluation of direct and postprocessing modes
Bibliographic record
Abstract
Dye-based water pollution is a subject of great concern as it has been linked to many health hazards. Considering their stable structures, some dyes, such as methylene blue (MB), cannot be easily removed from water. However, recent studies show that plasma-assisted processing has great potential for eliminating these dyes from water bodies. Among the different configurations of plasmas, discharges in gaseous bubbles in water are particularly promising in terms of water processing. The bubble-liquid discontinuity in such plasmas significantly facilitates the occurrence of discharge, and the modification of gas composition allows for controlling induced chemical reactions. In this study, we investigate the degradation of MB using pulsed discharges (amplitude of 20 kV, pulse duration of 500 ns, and repetition rate of 1 kHz) in Ar, O2, and N2 gaseous bubbles dispersed in water. The degradation of MB is evaluated in the direct mode, i.e., MB is present in the water during discharge as well as in the postprocessing mode, i.e., MB is added to water after plasma processing. Based on the obtained results, the most and least efficient degradation rates measured in the direct mode are achieved with O2 and N2 bubbles, respectively. Meanwhile, in the case of the postprocessing mode, degradation with N2 bubbles is initially more efficient than that with O2 or Ar. However, after hundreds of hours, higher degradation efficiency (up to 100%) is observed with O2 and Ar gas bubbles and not with N2. The findings reported herein are of great importance, particularly considering that plasma processing is complementary to the conventional techniques used in water depollution applications.
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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".