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Record W2055905410 · doi:10.1088/0960-1317/13/1/316

Analytical treatment of flow in infinitely extended circular microchannels and the effect of slippage to increase flow efficiency

2002· article· en· W2055905410 on OpenAlexafffund
Jun Yang, Daniel Y. Kwok

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2002
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSlippageMicrochannelElectrokinetic phenomenaSlip (aerodynamics)MicrofluidicsElectrolyteMechanicsVolumetric flow rateMaterials scienceSlip ratioFlow (mathematics)Electro-osmosisChemistryThermodynamicsNanotechnologyComposite materialChromatographyShear stressPhysicsElectrodeElectrophoresis

Abstract

fetched live from OpenAlex

Slippage of liquids at hydrophobic surfaces in microchannels has frequently been observed. We present here an analytical solution for oscillating flow in circular microchannels by combining the electrokinetic transport phenomena with Navier's slip condition. For pressure-driven flow, our results suggest that slippage of a typical electrolyte solution at a channel wall with a 10% slip length can improve fluid flow rate by about 20%. With respect to electro-osmotic pumping at a given flow rate, we showed that the voltage requirement for a typical electrolyte solution can be greatly reduced, by as much as 90% for a 1% slip length and 99% for a 10% slip length. Our results are useful to precisely control time-dependent microflow in microfluidic microelectromechanical system devices. They also provide design guidelines to improve the efficiency of lab-on-a-chip devices and miniature mechanical pumping/cooling systems by inducing slippage of liquids at the channel wall.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
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
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.185
Teacher spread0.181 · 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 designSimulation or modeling
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

Citations25
Published2002
Admission routes2
Has abstractyes

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