Magnetic nanowires for quantitative detection of biopolymers

Mohammad Reza Zamani Kouhpanji, Bethanie J.H. Stadler

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Magnetic nanowires (MNWs) rank among the most promising multifunctional magnetic nanomaterials for nanobarcoding applications, especially biolabeling, owing to their nontoxicity and remote excitation using a single magnetic source. Until recently, the first-order reversal curve (FORC) technique has been broadly used to study the MNWs for biolabeling applications. However, since FORC measurements require many data points, this technique is very slow which makes it inapplicable for clinical use. For this reason, we recently developed a fast new framework, named the projection method, to measure the irreversible switching field (ISF) distributions of MNWs as the magnetic signature for the demultiplexing of magnetic biopolymers. Here, we illustrate the ISF distributions of several MNWs types in terms of their coercivity and interaction fields, which are characterized using both FORC and projection methods. Then, we explain how to tailor the ISF distributions to generate distinct signature to reliably and quantitatively demultiplex the magnetically enriched biopolymers.

Original languageEnglish (US)
Article number125231
JournalAIP Advances
Issue number12
StatePublished - Dec 1 2020

Bibliographical note

Funding Information:
This work is primarily supported by the National Science Foundation (NSF) under grant number CMMI-1762884. Part of this work was performed at the Institute for Rock Magnetism (IRM) at the University of Minnesota. The IRM is a U.S. National Multi-user Facility supported through the Instrumentation and Facilities program of the National Science Foundation, Earth Sciences Division (NSF/EAR 1642268), and by funding from the University of Minnesota. Portions of this work were conducted in the Minnesota Nano Center, which is supported by the National Science Foundation through the National Nano Coordinated Infrastructure Network (NNCI) under Award Number ECCS-2025124. Parts of this work were also carried out in the Characterization Facility, University of Minnesota, which receives partial support from NSF through the MRSEC program.

Publisher Copyright:
© 2020 Author(s).


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