Integrate bubble flotation and intermittent microbubble-enhanced cold plasma activation for scalable disinfection of food processing wastewater
Bibliographic record
Abstract
The food processing industry is one of the largest global consumers of potable water, generating vast volumes of pathogen-rich wastewater that pose significant treatment challenges. Cold plasma activation has emerged as a promising technology for water disinfection; however, scalability remains a critical limitation. In this study, we address this challenge by developing an integrated approach that combines flotation-based pre-treatment with intermittent microbubble-enhanced cold plasma activation (MB-CPA) for effective disinfection at varying scales. The pre-treatment step significantly reduces turbidity (∼66 %) and organic load (∼57 %) in wastewater, minimizing interference during disinfection. The novel intermittent MB-CPA technique then enables enhanced antibacterial action, achieving ∼6-log CFU/mL reduction of Gram-positive, Gram-negative, and antibiotic-resistant bacteria in both simulated and real wastewater, with a 1.3-fold increase in efficacy against E. coli compared to continuous CPA. Rapid inactivation was achieved in 10–15 min, with successful scale-up from small volumes (0.5–3.5 L, ∼ 5.7-log CFU/mL reduction) to larger volumes (10 L, ∼ 5-log CFU/mL reduction), and a strong linear relationship (R2 = 0.96) between volume and exposure time highlights the scalability and feasibility of this method. This integrated system provides a transformative strategy for scalable, efficient disinfection in the meat processing industry, offering a viable solution to the critical challenge of scaling cold plasma-based water treatment for industrial 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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".