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Dynamics of Droplet Pinch-Off at Emulsified Oil-Water Interfaces: Interplay between Interfacial Viscoelasticity and Capillary Forces

2023· article· en· W4317214462 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysical Review Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationUniversity of Calgary
KeywordsViscoelasticityMarangoni effectMaterials scienceSurface tensionCapillary actionRheologyNewtonian fluidMechanicsRelaxation (psychology)Composite materialThermodynamicsPhysics

Abstract

fetched live from OpenAlex

The presence of submicrometer structures at liquid-fluid interfaces modifies the properties of many science and technological systems by lowering the interfacial tension, creating tangential Marangoni stresses, and/or inducing surface viscoelasticity. Here we experimentally study the break-up of a liquid filament of a silica nanoparticle dispersion in a background oil phase that contains surfactant assemblies. Although self-similar power-law pinch-off is well documented for threads of Newtonian fluids, we report that when a viscoelastic layer is formed in situ at the interface, the pinch-off dynamics follows an exponential decay. Recently, such exponential neck thinning was found theoretically when surface viscous effects were taken into account. We introduce a simple approach to calculate the effective relaxation time of viscoelastic interfaces and estimate the thickness of the interfacial layer and the viscoelastic properties of liquid-fluid interfaces, where the direct measurement of interfacial rheology is not possible.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.262
Teacher spread0.251 · 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