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Record W2101232495 · doi:10.1017/s0022112009006533

Breakup of surfactant-laden jets above the critical micelle concentration

2009· article· en· W2101232495 on OpenAlexaff
Richard V. Craster, Omar K. Matar, Demetrios T. Papageorgiou

Bibliographic record

VenueJournal of Fluid Mechanics · 2009
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Alberta
FundersEngineering and Physical Sciences Research Council
KeywordsBreakupPulmonary surfactantCritical micelle concentrationSurface tensionMicelleMarangoni effectThermodynamics of micellizationPhysicsMechanicsNonlinear systemThermodynamicsEquation of stateMaterials scienceChemistryAqueous solutionPhysical chemistry

Abstract

fetched live from OpenAlex

The breakup of viscous liquid jets that contain surfactant, that is potentially above the critical micelle concentration (CMC) is considered here within the long-wave approximation. The soluble surfactant is assumed to be present in three phases: as an interfacial species, bulk monomers and micelles. A model is developed for the interaction between these phases and the surface tension which obeys a nonlinear equation of state. The effects of the equation of state and the reservoir of surfactant created by micelles on breakup are investigated. The long-wave approximation naturally leads to a system of coupled one-dimensional equations that are solved numerically. It is demonstrated that jet breakup and satellite formation are critically affected by the presence of high surfactant concentrations above the CMC. This manifests itself by the formation of unusually large satellites. We present extensive numerical evidence that the mechanism for this phenomenon centres on the interplay between Marangoni stresses and the nonlinear surfactant equation of state; the latter exhibits a plateau at high interfacial concentrations.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations56
Published2009
Admission routes1
Has abstractyes

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