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Record W2065294967 · doi:10.1002/aic.12713

Sherwood number in flow through parallel porous plates (Microchannel) due to pressure and electroosmotic flow

2011· article· en· W2065294967 on OpenAlexafffund
Nallapusa Vennela, Sourav Mondal, Sirshendu De, Subir Bhattacharjee

Bibliographic record

VenueAIChE Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSherwood numberMicrochannelDebye lengthReynolds numberMass transferChemistryMechanicsElectrokinetic phenomenaThermodynamicsMaterials scienceNusselt numberPhysicsTurbulence

Abstract

fetched live from OpenAlex

Abstract An expression for Sherwood number is developed from first principles for combined pressure‐driven and electroosmotic flow in a porous rectangular microchannel. This quantifies the mass transfer of an electrically neutral solute in the microchannel and is useful for designing microfluidic devices and porous media flows. The convective‐diffusive species balance equation, coupled with the velocity field, is solved within the mass transfer boundary layer utilizing similarity method. From the simulations, it is observed that the Sherwood number increases as the electric double layer near the channel wall becomes more compact (as manifested through a decrease in the Debye length), and it reaches a constant value around the scaled Debye length of 40. The Sherwood number becomes constant at higher Debye lengths as electrokinetic effects become negligible. A detailed analysis of dependence of Reynolds number, dimensionless permeation velocity, ratio of driving force and scaled Debye length on Sherwood number is presented. © 2011 American Institute of Chemical Engineers AIChE J, 58: 1693–1703, 2012

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)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.199
Teacher spread0.190 · 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

Citations38
Published2011
Admission routes2
Has abstractyes

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