Air-nanobubbles ineffective to reduce pathogenic bacteria in fresh and brackish waters
Bibliographic record
Abstract
ABSTRACT Nanobubble (NB) technology has been hailed as a novel way to disinfect water. Previous studies suggested that when NBs collapse, they create shock waves that result in OH - free radicals, which can damage cells, including bacteria. In this study, we investigated, through a series of 11 experiments, the potential use of air nanobubbles (128 ± 44 nm, mean ± SD) to reduce the concentration of various pathogenic bacteria including Aeromonas hydrophila, A. veronii, Vibrio parahaemolyticus , and Streptococcus agalactiae under controlled, tank-based laboratory conditions. Despite the high number of nanobubbles continuously added to a relatively small volume of water in experimental tanks (50-100 L), we did not observe a consistent or significant decrease in bacteria that would control disease outbreaks. Although most of the experiments were conducted in fresh water on A. hydrophila , results were consistent across fresh and brackish water experiments, Gram-negative and Gram-positive bacteria, and a range of nanobubble concentrations. This study suggests air nanobubbles on their own are inadequate to significantly reduce high levels of pathogenic bacteria in water. We propose to explore other gases for improving the disinfection properties of this technology. SIGNIFICANCE STATEMENT Air nanobubbles did not sufficiently reduce the level of bacteria in laboratory experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".