An ultrasound‐conductivity method for measuring gas holdup in a microbubble‐based gas‐liquid system
Bibliographic record
Abstract
Due to the high specific surface area of microbubble‐based systems, the concept of gas‐liquid separation has successful applications in many fields, such as oil‐water separation, algal harvesting, micro‐extraction, membrane pretreatment, and water treatment. Gas holdup is an important parameter in such systems. However, the conventional measurement methods for the macrobubble system may not be directly applicable to the microbubble system due to small bubble size and low gas holdup. In this study, an ultrasound‐conductivity method under non‐isokinetic sampling conditions was developed to measure gas holdup in the microbubble‐based gas‐liquid system. The measurement setup consists of a sampling probe, a bubble coalescence unit, and a conductivity measurement unit. A key feature of the setup is a bubble coalescence unit to convert microbubbles to macrobubbles for conductivity measurement. The results showed that the bubble coalescence unit, made of an ultrasonic bath and a bubble‐coalescence cell, was successful in forming macrobubbles so that accurate conductivity measurements could be made. Under non‐isokinetic sampling conditions, the relationship between the extraction parameter (the gas volume fraction in the sampling probe) and the true gas holdup was established in flotation columns. The results indicate that the relationship does not depend on other factors, such as sampling probe orientation and flotation column diameter. Therefore, the developed ultrasound‐conductivity method has great potential in microbubble‐based gas‐liquid system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".