Enhanced dye degradation using plasma bubbles for the sustainable environmental remediation
Bibliographic record
Abstract
This study proposes a novel and eco-friendly approach for wastewater treatment using plasma jet technology under bubble condition. This method allows for the controlled production of highly reactive hydroxyl radicals ( O H • ) while minimizing unwanted interactions with nitrogen in the air. The presence of bubbles in liquid significantly boosts the diffusion of O H • within the wastewater, leading to a two-fold increase in degradation rate compared to normal condition. The effectiveness of the treatment was confirmed through ultraviolet-visible spectroscopy, which showed a significant decrease in rhodamine B and methyl orange absorbance peaks. Raman spectroscopy further revealed structural changes in both pollutants, indicating successful degradation. Additionally, plasma characteristics like power, electron temperature , and density were monitored to gain deeper insights into the underlying mechanism. Importantly, the process minimizes the formation of harmful secondary pollutants such as ozone and nitrogen oxides . These pollutants were found under concentration of 0.14 mg m −3 which is below established safety thresholds, adhering to World Health Organization guidelines. This research demonstrates that plasma jet treatment in bubble condition not only enhances the degradation efficiency of pollutants in wastewater but also minimizes the formation of harmful byproducts. This represents a significant breakthrough in developing sustainable wastewater treatment technologies.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".