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Record W2314253324 · doi:10.1103/physreve.86.026308

Droplet formation in microfluidic T-junction generators operating in the transitional regime. III. Dynamic surfactant effects

2012· article· en· W2314253324 on OpenAlexaff
Tomasz Glawdel, Carolyn L. Ren

Bibliographic record

VenuePhysical Review E · 2012
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSurface tensionPulmonary surfactantMicrofluidicsWork (physics)Materials scienceDynamic TensionGenerator (circuit theory)MechanicsTension (geology)Flow (mathematics)Chemical physicsThermodynamicsNanotechnologyPhysicsComposite materialPower (physics)

Abstract

fetched live from OpenAlex

This study extends our previous work on droplet generation in microfluidic T-junction generators to include dynamic interfacial tension effects created by the presence of surfactants. In Paper I [T. Glawdel, C. Elbuken, and C. L. Ren, Phys. Rev. E 85, 016322 (2012)], we presented experimental findings regarding the formation process in the squeezing-to-transition regime, and in Paper II [T. Glawdel, C. Elbuken, and C. L. Ren, Phys. Rev. E 85, 016323 (2012)] we developed a theoretical model that describes the performance of T-junction generators without surfactants. Here we study dynamic interfacial tension effects for two surfactants, one with a small molecular weight that adsorbs quickly, and the other with a large molecular weight that adsorbs slowly. Using the force balance developed in Paper II we extract the dynamic interfacial tension from high speed videos obtained during experiments. We then develop a theoretical model to predict the dynamic interfacial tension in microfluidic T-junction generators as a function of the surfactant properties, flow conditions, and generator design. This model is then incorporated into the overall model for generator performance to effectively predict the size of droplets produced when surfactants are present.

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)0.001
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 score0.106
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.263
Teacher spread0.253 · 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

Citations85
Published2012
Admission routes1
Has abstractyes

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