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Record W2045363318 · doi:10.1103/physreve.81.056318

Magnetic resonance imaging of two-component liquid-liquid flow in a circular capillary tube

2010· article· en· W2045363318 on OpenAlexafffund
Jing Zhang, Bruce J. Balcom

Bibliographic record

VenuePhysical Review E · 2010
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHagen–Poiseuille equationCapillary actionRADIUSMicrofluidicsFlow (mathematics)Nuclear magnetic resonanceTube (container)Materials sciencePhysicsResolution (logic)Resonance (particle physics)MechanicsAnalytical Chemistry (journal)OpticsChemistryAtomic physicsThermodynamicsChromatography

Abstract

fetched live from OpenAlex

Two-component liquid-liquid Poiseuille flow through a circular pipe with a "Y junction" has been investigated theoretically, but few experimental studies exist. Here we report a simple microfluidic two-component flow measurement in a capillary tube with magnetic-resonance imaging (MRI). Velocity mapping of both liquids has been achieved using spin-echo pure phase encoding techniques combined with chemical shift imaging. Images of 32 × 32 μm2 resolution were obtained for a channel of circular cross section (internal radius R=400 μm). The results are in agreement with the theoretical prediction of liquid-liquid flow. This report is to validate theoretical liquid-liquid Poiseuille velocity distribution predictions with a high-resolution MRI method. This MRI method is shown to be a versatile tool for the study of multicomponent flow in microfluidic systems.

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 score0.130
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.260
Teacher spread0.252 · 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

Citations11
Published2010
Admission routes2
Has abstractyes

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