Geometry-driven segregation in periodically textured microfluidic channels

We investigate the transport dynamics of elongated microparticles in microchannel flows. While smooth-walled channels preserve the dependence of particle trajectories on initial orientation and lateral position, we show that introducing periodically textured walls can trigger robust alignment of the particle along the channel centerline. This geometry-driven alignment arises from repeated reorientations generated by spatially modulated shear gradients near the textured walls. The alignment efficiency depends on particle elongation and the relative texture wavelength, with an optimal range for the maximal effect. While the observed alignment behavior is not limited to low Reynolds numbers, the characteristic alignment length scale diverges as the Reynolds number increases toward the turbulent flow regime. These findings offer a predictive framework for designing microfluidic devices that passively sort or focus anisotropic particles, with implications for soft matter transport, biophysical flows, and microfluidic engineering.

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Publication Details

Journal
Physics of Fluids
Published
2026-04-01
DOI
https://doi.org/10.1063/5.0320693
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
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article

Geometry-driven segregation in periodically textured microfluidic channels

Hossein Hamzehpour, M. Reza Shaebani, Fatemeh Ahmadi
Physics of Fluids
Modular Robots and Swarm Intelligence
article

Geometry-driven segregation in periodically textured microfluidic channels

Hossein Hamzehpour, M. Reza Shaebani, Fatemeh Ahmadi
article en

Abstract

We investigate the transport dynamics of elongated microparticles in microchannel flows. While smooth-walled channels preserve the dependence of particle trajectories on initial orientation and lateral position, we show that introducing periodically textured walls can trigger robust alignment of the particle along the channel centerline. This geometry-driven alignment arises from repeated reorientations generated by spatially modulated shear gradients near the textured walls. The alignment efficiency depends on particle elongation and the relative texture wavelength, with an optimal range for the maximal effect. While the observed alignment behavior is not limited to low Reynolds numbers, the characteristic alignment length scale diverges as the Reynolds number increases toward the turbulent flow regime. These findings offer a predictive framework for designing microfluidic devices that passively sort or focus anisotropic particles, with implications for soft matter transport, biophysical flows, and microfluidic engineering.

Physics of FluidsVol. 38(4)
Institute for Research in Fundamental Sciences (IR), K. N. Toosi University of Technology (IR), Saarland University (DE)
Openalex Percentile: Top 100%
Modular Robots and Swarm Intelligence
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