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Record W4212768545 · doi:10.1039/d1sm01204e

On the estimation of the size of a droplet emerging from a pore opening into a crossflow field

2022· article· en· W4212768545 on OpenAlexaff
Amgad Salama

Bibliographic record

VenueSoft Matter · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSurface tensionBreakupDragMechanicsMembrane emulsificationWeber numberCapillary actionWork (physics)Surface forceContext (archaeology)Computational fluid dynamicsCapillary numberMaterials scienceChemistryNanotechnologyMembraneMechanical engineeringThermodynamicsComposite materialPhysicsEngineeringTurbulenceGeologyReynolds number

Abstract

fetched live from OpenAlex

, pressure and crossflow velocity). Three forces, including capillary pressure, interfacial tension, and drag forces, were identified that account for a developed torque balance, which was then used to determine the onset of breakup of an emerging droplet. A comprehensive computational fluid dynamics (CFD) analysis has been conducted to highlight the physics involved in the process and also to provide scenarios for comparison exercises. The effects of crossflow velocity, applied pressure, and viscosity contrasts have been studied. It has been determined that the emerging droplet experiences deformation along the crossflow field because of the hydrodynamic drag. The receding portion of the contact line at the surface of the membrane wraps around the pore opening, generating an interfacial tension force that produces an opposing torque due to the crossflow drag and capillary pressure. Using this phenomenon, a framework for estimating the size of the droplet upon breakup is established. Comparisons with the results obtained from the CFD analysis under different conditions show very good agreement, which builds confidence in the modeling approach.

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 categoriesInsufficient payload (model declined to judge)
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 score0.158
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.006
GPT teacher head0.220
Teacher spread0.214 · 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.

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

Citations14
Published2022
Admission routes1
Has abstractyes

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