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Record W4310185959 · doi:10.3390/fluids7120365

Bubble Growth in Supersaturated Liquids

2022· article· en· W4310185959 on OpenAlexaff
Raj Kumar Nayak Maloth, Roger E. Khayat, Christopher T. DeGroot

Bibliographic record

VenueFluids · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsBubbleSupersaturationSurface tensionThermodynamicsLiquid bubbleViscosityMaximum bubble pressure methodDiffusionMechanicsDissolutionMass transferWork (physics)InertiaSolubilityMaterials scienceAdvectionBubble pointMass transfer coefficientChemistryPhysicsClassical mechanicsPhysical chemistry

Abstract

fetched live from OpenAlex

Bubble formation and dissolution have a wide range of industrial applications, from the production of beverages to foam manufacturing processes. The rate at which the bubble expands or contracts has a significant effect on these processes. In the current work, the hydrodynamics of an isolated bubble expanding due to mass transfer in a pool of supersaturated gas–liquid solution is investigated. The complete scalar transportation equation (advection–diffusion) is solved numerically. It is observed that the present model accurately predicted bubble growth when compared with existing approximated models and experiments. The effect of gas–liquid solution parameters such as inertia, viscosity, surface tension, diffusion coefficient, system pressure, and solubility of the gas has been investigated. It is found that the surface tension and inertia have a very minimal effect during the bubble expansion. However, it is observed that the viscosity, system pressure, diffusion, and solubility have a considerable effect on bubble growth.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2022
Admission routes1
Has abstractyes

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