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Record W2949138922 · doi:10.1039/c9sm00638a

Splitting droplets through coalescence of two different three-phase contact lines

2019· article· en· W2949138922 on OpenAlexafffund

Bibliographic record

VenueSoft Matter · 2019
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsCoalescence (physics)Drop (telecommunication)Contact angleSurface tensionViscosityInstability

Abstract

fetched live from OpenAlex

Moving contact lines of more than two phases dictate a large number of interfacial phenomena. Despite their significance in fundamental and applied processes, the contact lines at a junction of four-phases (two immiscible liquids, a solid and gas) have been addressed only in a few investigations. Here, we report an intriguing phenomenon that follows after the four phases oil, water, solid and gas make contact through the coalescence of two different three-phase contact lines. We combine experimental studies and theoretical analyses to reveal and rationalize the dynamics exhibited upon the coalescence between the contact line of a micron-sized oil droplet and the receding contact line of a millimeter-sized water drop that covers the oil droplet on the substrate. We find that after the coalescence a four-phase contact line is formed for a brief period. However this quadruple contact line is not stable, leading to a 'droplet splitting' effect and eventually expulsion of the oil droplet from the water drop. We then show that the interfacial tension between the different phases and the viscosity of the oil droplet dictate the splitting dynamics. More viscous oils display higher resistance to the extreme deformations of the droplet induced by the instability of the quadruple contact line and no droplet expulsion is observed in such cases.

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 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 score0.098
Threshold uncertainty score0.465

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)0.0000.000

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 head0.012
GPT teacher head0.250
Teacher spread0.238 · 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

Citations17
Published2019
Admission routes2
Has abstractyes

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