Integrating multiple cold plasma generators and Bernoulli-driven microbubble formation for large-volume water treatment
Bibliographic record
Abstract
Cold plasma-bubble is a promising clean technology for wastewater treatment using air and electricity. However, scalability continues to pose a significant challenge to industrial applications. In this study, we integrate portable, low-power cold plasma generators with spontaneous microbubble formation in engineered venturi tubes for rapid water treatment. These tubes provide water flow channels with multiple plasma ports. The design expanded the flow rate range for stable microbubble formation from earlier reports, enabling 16 L/min and scaling up the volume of treated water to 40 L with same energy efficiency. Importantly, we identified a universal linear correlation between the total surface area of microbubbles and activation efficiency, represented by removal of a model dye, methyl orange. Significant disinfection against Gram-(−/+) bacteria with 6.68-log was confirmed in increasing water volume. Time required for effective disinfection of 4-log CFU/mL removal increases approximately linearly with volume of water, suggesting that disinfection can be achieved even at large-scale without losing the effectiveness. Increasing the plasma generator numbers (four-needle), the treatment capacity can be further improved to 120 L. Our work demonstrates that the cold plasma-bubble technology for flowing water is rapid and scalable, providing a sustainable solution for diverse industrial and environmental challenges. This study integrates portable, low-power cold plasma generators for microbubble enhanced activation using engineered venturi tubes, demonstrating scalable, efficient and sustainable wastewater treatment and disinfection. • Engineered Venturi coupled with cold plasma generators designed for water treatment. • Dual-needle & water channel expanded liquid flow-rate, enhancing RONS mass-transfer. • Rapid disinfection renders log 6.68 CFU/mL ( E. coli ) removal even from large-scale. • Linear degradation-rate relationship with volume offers scalable operation.
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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".