Microbubble-enhanced cold plasma (MB-CAP) for pathogen disinfection in water: a sustainable alternative to traditional methods
Bibliographic record
Abstract
, without the need for added chemical reagents. It utilizes reactive oxygen and nitrogen species (RONS), ultraviolet (UV) radiation, and transient electric fields to effectively inactivate a wide range of waterborne pathogens. CAP disrupts microbial membranes, damages nucleic acids, and induces oxidative stress, rapidly inactivating bacteria, viruses, and fungi. A notable advancement in plasma-based water disinfection is microbubble-enhanced cold atmospheric plasma (MB-CAP), which significantly improves plasma-liquid interactions. Microbubbles (MBs) act as efficient carriers for RONS, greatly increasing the gas-liquid interfacial area and enhancing the mass transfer of RONS. This results in faster removal of pathogens compared to conventional CAP systems. Furthermore, MB-CAP offers localized and targeted treatment capabilities, making it particularly suitable for decentralized water systems, hospital wastewater, and high-load industrial effluents. This review thoroughly examines the mechanisms of microorganism inactivation by MB-CAP, reactor configurations, MB generation techniques, and disinfection performance. This review also discusses key challenges such as energy efficiency, scalability, and regulatory compliance. Future research should focus on developing hybrid CAP systems, integrating renewable energy sources, and implementing real-time monitoring tools to optimize treatment efficacy. Overall, the review highlights the transformative potential of MB-CAP as a next-generation sustainable water disinfection technology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".