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Record W2028378340 · doi:10.1021/la025942r

Microchannel Flow with Patchwise and Periodic Surface Heterogeneity

2002· article· en· W2028378340 on OpenAlexaff
David Erickson, Dongqing Li

Bibliographic record

VenueLangmuir · 2002
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrokinetic phenomenaReynolds numberStreaming currentMicrochannelMechanicsFlow (mathematics)Double layer (biology)Streamlines, streaklines, and pathlinesChemistryPhysicsMaterials scienceLayer (electronics)NanotechnologyTurbulence

Abstract

fetched live from OpenAlex

Surface heterogeneity is present in a variety of electrokinetic transport phenomena. It is desirable to understand the synergetic effects of the electrical double layer field and the surface heterogeneity on electrokinetic flow in microchannels. In this paper, a 3D, finite element based, numerical model for pressure-driven flow through microchannels with an arbitrary but periodic patchwise heterogeneous surface pattern has been developed. The model is based on a simultaneous solution to the Nernst−Planck, Poisson, and Navier−Stokes equations to determine the local ionic concentration, the double layer distribution, and the flow field. The presence of a heterogeneous patch is shown to induce flow in all three coordinate directions, including a circulation pattern perpendicular to the main flow axis. The strength of this circulation region is found to be proportional to Reynolds number and double layer thickness. While at low Reynolds number (i.e., Re < 1) the double layer distribution is diffusion dominated, significant convective effects are observed at higher Reynolds number leading to a deviation from the classical Poisson−Boltzmann distribution. The combined effect of the 3D flow field and disturbed double layer region on measurable quantities such as the streaming potential is discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.345

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 head0.007
GPT teacher head0.168
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 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

Citations74
Published2002
Admission routes1
Has abstractyes

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