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How Fast do Microdroplets Generated During Liquid–Liquid Phase Separation Move in a Confined 2D Space?

2021· article· en· W3173238619 on OpenAlexafffund
Gilmar F. Arends, John M. Shaw, Xuehua Zhang

Bibliographic record

VenueEnergy & Fuels · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsTernary operationPhase (matter)Oil dropletSolventChemistryWork (physics)DissolutionDisplacement (psychology)Confined spaceAnalytical Chemistry (journal)ChromatographyMaterials scienceThermodynamics

Abstract

fetched live from OpenAlex

How liquid transport occurs in confined spaces is relevant to many industrial and lab-scale processes, ranging from enhanced oil recovery to drug delivery systems. In this work, we investigate propelling microdroplets that form from liquid–liquid phase separation in a quasi-2D chamber, focusing on the direction and speed of microdroplets in response to local composition gradients. The confined ternary solution in our experiments comprises a model oil (the main one being octanol), a good solvent (ethanol), and a poor solvent (water). It is displaced by water. Depending on the initial solution composition, water-rich or oil-rich microdroplets, ∼1/4–1/3 of the height of a narrow and wide microchamber form and move spontaneously as the ternary solution mixes with and is displaced by water diffusing from a deep side channel. Microdroplet movement is followed in situ using high-speed bright-field imaging or fluorescence imaging when the solution is doped with a dye. Local ethanol composition gradients are estimated from the variation of fluorescence intensity in the local continuous liquid surrounding of the mobile microdroplets. From phase separation of the ternary solution with high oil concentration, mobile oil-rich microdroplets form in a water-rich zone, accompanying the formation of water-rich microdroplets in an oil-rich zone. The fast movement of oil-rich microdroplets induces directional flow transport that mobilizes water-rich microdroplets close to the water-rich zone. Regardless of the initial composition of the solution, displacement of oil-rich microdroplets extends linearly with time. The average microdroplet speed increases with the initial oil concentration in the ternary solution. The fastest speed of oil-rich microdroplets observed in our experiments is ∼150 μm/s along the surface of a hydrophobic wall. The presence of a sharp ethanol composition gradient is thought to be the primary driving force for the fast movement of oil-rich microdroplets in confinement. Our results demonstrate the potential of enhancing liquid transport in confinement through composition gradients arising from phase separation.

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 categoriesMeta-epidemiology (narrow)
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 score0.016
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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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