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Record W2084013707 · doi:10.1115/icnmm2011-58064

Flow Patterns and Electrokinetic Mixing Performance in Heterogeneous Microchannels

2011· article· en· W2084013707 on OpenAlexafffund
Jafar Jamaati, Hamid Niazmand, Metin Renksizbulut

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaFerdowsi University of Mashhad
KeywordsMicrochannelMechanicsMixing (physics)VortexElectrokinetic phenomenaMicromixerElectro-osmosisMaterials scienceFlow (mathematics)MicrofluidicsPhysicsChemistryNanotechnology

Abstract

fetched live from OpenAlex

Due to the recent advances in microfabrication techniques, it is possible to produce microchannels with positive, negative, or even neutral surface charges. According to several numerical and experimental investigations, such a combination of charge patterns on the microchannel walls results in complex flow fields with circulation zones that are highly desirable for fluid mixing requirements as in lab-on-a-chip devices. In this paper, the mixing efficiency associated with electro-osmotic flows in heterogeneous microchannels is investigated. The Navier-Stokes equations are solved for the flow field along with species transport equations to obtain the concentration field. The effects of the Electric Double Layer (EDL) on the flow field are considered using the Helmholtz-Smoluchowski model in which the EDL effects on the fluid adjacent to the walls are replaced by velocity slip at walls. Different configurations and profiles for the wall charges can be applied to the microchannel walls. In the present study, heterogeneous patterns consisting of different patches with constant zeta-potentials are considered. The flow pattern of a single patch consists of a single vortex attached to the channel wall, which significantly increases the mixing performance. It is expected that a combination of several patches would increase the mixing performance considerably. Therefore, the effects of the size, number, and locations of multiple patches on the mixing performance are investigated in detail. The results for a single patch indicate that the mixing efficiency increases with the size of the patch and its proximity to the microchannel inlet. It is expected that with a suitable combination of patches, an optimized configuration can be found in which the mixing efficiency is maximized and the length of the mixing section is minimized. The results can be applied to the design of micro-mixers to minimize their size while achieving the desired mixing requirements.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.008
GPT teacher head0.169
Teacher spread0.161 · 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".

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Citations0
Published2011
Admission routes2
Has abstractyes

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