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Record W2054166107 · doi:10.1115/icmm2003-1040

Interfacial Electrokinetic Effects on Fluid Flow in Microchannel by a Generalized Lattice Boltzmann Model

2003· article· en· W2054166107 on OpenAlexaff
Baoming Li, Steven K. Chai, Fuzhi Tian, Daniel Y. Kwok

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicrochannelLattice Boltzmann methodsMicroscale chemistryElectrokinetic phenomenaPoisson–Boltzmann equationDebye lengthNanofluidicsMechanicsSurface chargeElectrohydrodynamicsMaterials scienceDebye–Hückel equationFluid dynamicsBoltzmann equationPoisson's equationElectric potentialElectrolyteIonChemistryThermodynamicsPhysicsNanotechnologyElectrodeMathematicsPhysical chemistry

Abstract

fetched live from OpenAlex

A diffuse electric double layer (EDL) in microchannel flow created by the charged surface in contact with an electrolyte solution is characterized by the so-called Debye-Hu¨ckel screening length, which depends on the ionic strength of the solution. Usually, the electric double layer thickness, which is from several nanometers to a few hundreds nanometers, is small in comparison with the microchannel height of a few tens microns. Traditional computational fluid dynamics (CFD) methods for macroscopic hydrodynamic equations have difficulties in such complex fluid dynamics problems involving microscale surface interactions. In this paper, we employ a two-dimensional generalized lattice Boltzmann model in the presence of external forces on a rectangular grid with an arbitrary aspect ratio and nonuniform mesh grids. A modified Poisson-Boltzmann equation is applied to examine the adsorption of ions from solution to a charged surface and obtain the electrostatic potential and ion distribution. An example with electroviscous flow in microchannel is used to validate the prediction ability of the model proposed here. Excellent agreement with experimental results was found.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
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)0.0000.000

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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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 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

Citations2
Published2003
Admission routes1
Has abstractyes

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