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Energy-barrier-mediated particle and cell transport switching and sorting in a magnetophoretic microfluidic platform under a rotating magnetic field.

Created on 04 Jul 2026

Authors

Roozbeh Abedini-Nassab, Atabak Mohammadi Moazed, Yaping Dan

Published in

Scientific reports. Jul 03, 2026. Epub Jul 03, 2026.

Abstract

Sorting particles based on intrinsic properties remains a central challenge in Lab-on-a-Chip technologies. Here, we present a magnetophoretic microfluidic platform for the controlled transport of magnetic microparticles and magnetically labeled cells along predefined magnetic tracks, as well as size- and magnetization-based sorting. The system integrates patterned magnetic thin films within a chip and operates in an in-plane rotating magnetic field that synchronizes particle motion, enabling precise positioning and transport. Introducing a small gap in the magnetic pattern allows selective particle transmission only under specific combinations of particle properties and field parameters, resembling semiconducting-like transport behavior. By tuning the magnetic field parameters, selective sorting is achieved based on two parameters: (1) particle size and (2) effective magnetic moment. The system is studied using simulations and experiments to identify critical frequencies governing particle transport across the gap. Machine learning models are further employed to classify particle transport states, achieving up to 95% prediction accuracy. Experimentally, the platform achieves approximately 96 ± 1.4% efficiency for size-based sorting of particles and approximately 98 ± 1.4% efficiency for sorting of HEK-293T cells with different magnetic loading conditions. For mixed populations of T cells and HEK-293T cells, approximately 98.33 ± 1.44% sorting efficiency is achieved through combined size and magnetization differences. This robust platform provides an efficient solution for particle and cell sorting, with applications in biomedical diagnostics and single-cell analysis.

PMID:
42399379
Bibliographic data and abstract were imported from PubMed on 04 Jul 2026.

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