Effects of Gas Type, Oil, Salts and Detergent on Formation and Stability of Air and Carbon Dioxide Bubbles Produced by Using a Nanobubble Generator
Bibliographic record
Abstract
Recent developments in ultrafine bubble generation have opened up new possibilities for applications in various fields. Herein, we investigated how substances in water affect the size distribution and stability of microbubbles generated by a common nanobubble generator. By combining light scattering techniques with optical microscopy and high-speed imaging, we were able to track the evolution of microbubbles over time during and after bubble generation. Our results showed that air injection generated a higher number of microbubbles (<10 μm) than CO2 injection. Increasing detergent concentration led to a rapid increase in the number of microbubbles generated by both air and CO2 injection and the intensity signal detected by dynamic light scattering (DLS) slightly increased. This suggested that surface-active molecules may inhibit the growth and coalescence of bubbles. In contrast, we found that salts (NaCl and Na2CO3) in water did not significantly affect the number or size distribution of bubbles. Interestingly, the presence of oil in water increased the intensity signal and we observed that the bubbles were coated with an oil layer. This may contribute to the stability of bubbles. Overall, our study sheds light on the effects of common impurities on bubble generation and provides insights for analyzing dispersed bubbles in bulk.
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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 | 0.001 |
| 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".