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Magnetic particles–integrated microfluidics: from physical mechanisms to biological applications

  • Amirhosein Paryab , Mahshid Saghatchi , Bahareh Zarin , Shadi Behsam , Sorosh Abdollahi , Adrine Malek Khachatourian ORCID logo EMAIL logo , Muhammet S. Toprak , Shukufe Amukarimi , Anjum Qureshi and Javed H. Niazi EMAIL logo
Published/Copyright: October 23, 2024
Become an author with De Gruyter Brill

Abstract

Magnetic nanoparticles (MNPs) have garnered significant attention in biomedical applications. Due to their large surface area and tunable properties, MNPs are used in microfluidic systems, which allow for the manipulation and control of fluids at micro- or nanoscale. Using microfluidic systems allows for a faster, less expensive, and more efficient approach to applications like bioanalysis. MNPs in microfluidics can precisely identify and detect bioanalytes on a single chip by controlling analytes in conjunction with magnetic particles (MPs) and separating various particles for analytical functions at the micro- and nanoscales. Numerous uses for these instruments, including cell-based research, proteomics, and diagnostics, have been reported. The successful reduction in the size of analytical assays and the creation of compact LOC platforms have been made possible with the assistance of microfluidics. Microfluidics is a highly effective method for manipulating fluids as a continuous flow or discrete droplets. Since the implementation of the LOC technology, various microfluidic methods have been developed to improve the efficiency and precision of sorting, separating, or isolating cells or microparticles from their original samples. These techniques aim to surpass traditional laboratory procedures. This review focuses on the recent progress in utilizing microfluidic systems that incorporate MNPs for biological applications.


Corresponding authors: Adrine Malek Khachatourian, Department of Materials Science and Engineering, Sharif University of Technology, Tehran, Iran, E-mail: ; and Javed H. Niazi, Nanotechnology Research and Application Center (SUNUM), Sabanci University, Istanbul, Türkiye, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2023-12-17
Accepted: 2024-09-18
Published Online: 2024-10-23
Published in Print: 2024-11-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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