MétaCan
Menu
Back to cohort
Record W4408568208 · doi:10.1016/j.ces.2025.121547

Nanoparticle and scalar mixing of magnetic colloids in microchannels—Prevalence of Kelvin body force over spin-up flow

2025· article· en· W4408568208 on OpenAlexafffund
La Zakaria, Faı̈çal Larachi, Abdelwahid Azzi

Bibliographic record

VenueChemical Engineering Science · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMechanicsScalar (mathematics)Flow (mathematics)Mixing (physics)NanoparticleMagnetic nanoparticlesColloidMaterials sciencePhysicsNanotechnologyChemistryPhysical chemistryMathematics

Abstract

fetched live from OpenAlex

This study investigates the underlying mechanisms of active transverse mixing in dilute magnetic colloidal suspensions in microchannels , focusing on the interplay between the Kelvin Body Force (KBF) and spin-up flow under a rotating magnetic field (RMF). By studying the effects of KBF-induced flow on mixing, we identify the KBF as the dominant force that generates strong transverse motion, which significantly enhances the transverse mixing of scalar and nanoparticles . Using a Y-shaped microchannel model with one-sided injection of magnetic nanoparticles (MNPs), we show how the KBF disrupts the axial flow pattern, thereby promoting rapid mixing and reducing scalar field segregation. In comparison, the spin-up flow shows limited influence, suggesting the clear advantage of the KBF in optimizing mixing efficiency. These results highlight the potential of tuning RMF parameters to maximize KBF-driven mixing in microfluidic applications. On the other hand, further investigation of spin-up flow in the cluster regime could improve our understanding of the dynamics of KBF-driven mixing and provide new insights for microfluidic reactor design.

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 score0.110
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.196
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

Citations3
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueChemical Engineering ScienceSame topicMicrofluidic and Capillary Electrophoresis ApplicationsFrench-language works237,207