Dynamical mass‐transfer process of a CO<sub>2</sub> bubble measured by using LIF/HPTS visualisation and photoelectric probing
Bibliographic record
Abstract
Abstract We directly visualised the dynamical mass‐transfer process from a zigzagging rising CO2 bubble (2.9 mm in equivalent diameter) to its surrounding liquid by using laser‐induced fluorescence/8‐hydroxypyrene‐1, 3, 6‐trisulfonic acid (LIF/HPTS). We measured the surrounding liquid motion induced by bubble buoyancy using particle image velocimetry (PIV). Further, the CO2 concentration profile inside the bubble wake was measured directly by using a newly developed photoelectric optical fibre probe (POFP). Making the best and mutually complementary use of these three measurement techniques, we discuss the relationship between the mass‐transfer process and the flow structure. We succeeded in clearly visualising CO2‐rich regions corresponding with the dynamical mass‐transfer process from the bubble to the wake and the surrounding liquid (LIF/HPTS). We also obtained a CO2 concentration profile in the bubble wake (the POFP). It was found that the CO2‐rich regions were formed into horseshoe‐like vortices; the CO2 concentration at the centre region of the wake was the highest, and the concentration decreased toward the outer edge of the wake; the CO2‐rich regions were transported widely into the surrounding liquid by the advective liquid‐phase flows (PIV). In addition, we discuss the performance and characteristics of the newly developed POFP.
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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) | 0.000 | 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".