Predictive modeling of mixing and heat mechanisms in dilute, clusterless magnetocolloids

Mixing remains a major limitation in microfluidic systems, where molecular diffusion under laminar flow conditions is inefficient for rapid homogenization. Magnetic actuation using magnetic nanoparticles provides a remote, contactless, and tunable active mixing strategy. Although numerous experiments demonstrate that magnetic fields induce secondary flows and enhance mixing, their interpretation has remained empirical as predictive models were lacking. Focusing on dilute, clusterless magnetocolloidal suspensions operating at low Reynolds number, we argue that these limitations were primarily conceptual rather than experimental. Recent advances in two-phase predictive modeling now provide a description without adjustable constitutive fitting parameters within the dilute, clusterless regime, enabling spin-up flow and Kelvin body force to be identified, quantified, and controlled as distinct transport mechanisms. The corresponding closure relations are derived in the single-particle limit. These frameworks further reveal magnetocaloric heating under high-frequency rotating magnetic fields, coupling flow generation, mixing enhancement, and localized thermal effects. We show how magnetic actuation can be exploited as a design variable to control mixing and heat. • Predictive models resolve spin-up flow and Kelvin body force mechanisms. • Magnetic actuation enables controllable mixing in laminar microflows. • Spin-up flow and KBF are identified as distinct transport mechanisms. • High-frequency RMFs couple mixing enhancement with magnetocaloric heating. • Magnetic actuation evolves from heuristic aid to rational design variable.

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Publication Details

Journal
Current Opinion in Chemical Engineering
Published
2026-04-24
DOI
https://doi.org/10.1016/j.coche.2026.101257
Primary Topic
Micro and Nano Robotics
Type
article
Field-Weighted Citation Impact
0.00

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article

Predictive modeling of mixing and heat mechanisms in dilute, clusterless magnetocolloids

Zakaria Larbi, Abdelwahid Azzi, Faı̈çal Larachi, Seyed-Mohammad Taghavi
Current Opinion in Chemical Engineering
Micro and Nano Robotics
article

Predictive modeling of mixing and heat mechanisms in dilute, clusterless magnetocolloids

Zakaria Larbi, Abdelwahid Azzi, Faı̈çal Larachi, Seyed-Mohammad Taghavi
article en

Abstract

Mixing remains a major limitation in microfluidic systems, where molecular diffusion under laminar flow conditions is inefficient for rapid homogenization. Magnetic actuation using magnetic nanoparticles provides a remote, contactless, and tunable active mixing strategy. Although numerous experiments demonstrate that magnetic fields induce secondary flows and enhance mixing, their interpretation has remained empirical as predictive models were lacking. Focusing on dilute, clusterless magnetocolloidal suspensions operating at low Reynolds number, we argue that these limitations were primarily conceptual rather than experimental. Recent advances in two-phase predictive modeling now provide a description without adjustable constitutive fitting parameters within the dilute, clusterless regime, enabling spin-up flow and Kelvin body force to be identified, quantified, and controlled as distinct transport mechanisms. The corresponding closure relations are derived in the single-particle limit. These frameworks further reveal magnetocaloric heating under high-frequency rotating magnetic fields, coupling flow generation, mixing enhancement, and localized thermal effects. We show how magnetic actuation can be exploited as a design variable to control mixing and heat. • Predictive models resolve spin-up flow and Kelvin body force mechanisms. • Magnetic actuation enables controllable mixing in laminar microflows. • Spin-up flow and KBF are identified as distinct transport mechanisms. • High-frequency RMFs couple mixing enhancement with magnetocaloric heating. • Magnetic actuation evolves from heuristic aid to rational design variable.

Current Opinion in Chemical EngineeringVol. 52
University of Sciences and Technology Houari Boumediene (DZ), Université Laval (CA)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 5%
Micro and Nano Robotics
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