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Record W2060921132 · doi:10.1002/cjce.20319

Dynamical mass‐transfer process of a CO<sub>2</sub> bubble measured by using LIF/HPTS visualisation and photoelectric probing

2010· article· en· W2060921132 on OpenAlexvenueno aff
Kodai Hanyu, Takayuki Saito

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsWakeBubbleMass transferParticle image velocimetryAnalytical Chemistry (journal)BuoyancyChemistryAdvectionMechanicsPhysicsThermodynamicsTurbulenceChromatography

Abstract

fetched live from OpenAlex

Abstract We directly visualised the dynamical mass‐transfer process from a zigzagging rising CO 2 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 CO 2 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 CO 2 ‐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 CO 2 concentration profile in the bubble wake (the POFP). It was found that the CO 2 ‐rich regions were formed into horseshoe‐like vortices; the CO 2 concentration at the centre region of the wake was the highest, and the concentration decreased toward the outer edge of the wake; the CO 2 ‐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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.186
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2010
Admission routes1
Has abstractyes

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