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Record W1949661266 · doi:10.1002/aic.15026

Effect of channel size on liquid‐liquid plug flow in small channels

2015· article· en· W1949661266 on OpenAlexfundno aff
Dimitrios Tsaoulidis, Panagiota Angeli

Bibliographic record

VenueAIChE Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilQueen's University
KeywordsSpark plugPlug flowPressure dropLaminar flowMechanicsParticle image velocimetryReynolds numberParticle sizeFlow velocityDrop (telecommunication)Materials scienceChemistryVelocimetryFlow (mathematics)Hydraulic diameterAnalytical Chemistry (journal)ThermodynamicsChromatographyPhysicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The hydrodynamic properties of plug flow were investigated in small channels with 0.5‐, 1‐, and 2‐mm internal diameter, for an ionic liquid/aqueous two‐phase system with the aqueous phase forming the dispersed plugs. Bright field Particle Image Velocimetry combined with high‐speed imaging were used to obtain plug length, velocity, and film thickness, and to acquire velocity profiles within the plugs. Plug length decreased with mixture velocity, while for constant mixture velocity it increased with channel size. Plug velocity increased with increasing mixture velocity and channel size. The film thickness was predicted reasonably well for Ca > 0.08 by Taylor's (Taylor, J Fluid Mech. 1961;10(2):161–165) model. A fully developed laminar profile was established in the central region of the plugs. Circulation times in the plugs decreased with increasing channel size. Pressure drop was predicted reasonably well by a modified literature model, using a new correlation for the film thickness derived from experimental values. © 2015 American Institute of Chemical Engineers AIChE J, 62: 315–324, 2016

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
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 head0.018
GPT teacher head0.250
Teacher spread0.233 · 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

Citations96
Published2015
Admission routes1
Has abstractyes

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