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Record W2074094407 · doi:10.1021/ac0303160

Hydrodynamic Dispersion due to Combined Pressure-Driven and Electroosmotic Flow Through Microchannels with a Thin Double Layer

2004· article· en· W2074094407 on OpenAlexafffund
Emilij K. Zholkovskij, Jacob H. Masliyah

Bibliographic record

VenueAnalytical Chemistry · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrokinetic phenomenaChemistryMicrochannelDimensionless quantityMechanicsRADIUSEllipseRectangleCross section (physics)Dispersion (optics)GeometryElectrolyteAnnulus (botany)ThermodynamicsOpticsPhysicsMaterials scienceMathematicsComposite material

Abstract

fetched live from OpenAlex

The hydrodynamic dispersion of a nonadsorbed and nonelectrolyte solute is considered for the case of a flow driven through a straight microchannel by pressure and electric potential differences. The analysis is conducted using a thin double layer approximation developed in the previous paper (Zholkovskij, E. K.; Masliyah, J. H.; Czarnecki, J. Anal. Chem. 2003, 75, 901-909). On the basis of this approach, an expression is derived to address the dispersion coefficient for arbitrary electrokinetic potential, electrolyte type, and cross-section geometry. In the derived expression, the influence of cross-section geometry manifests itself through the channel hydrodynamic radius and through three dimensionless geometrical factors. The procedure for obtaining the geometrical factors is presented for an arbitrary cross-section geometry. The geometrical factors are evaluated for several examples of cross section: (i) unbounded parallel planes; (ii) circle; (iii) annulus; (iv) ellipse; (v) rectangle. The dependency of the dispersion coefficient on different parameters is discussed. It is shown that the dependencies are substantially affected by the cross-section geometry, electrolyte type, and electrokinetic potential.

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 score0.045
Threshold uncertainty score0.729

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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

Citations58
Published2004
Admission routes2
Has abstractyes

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