Facile decoding of quantitative signatures from magnetic nanowire arrays

Mohammad Reza Zamani Kouhpanji, Ali Ghoreyshi, P. B. Visscher, Bethanie J.H. Stadler

Research output: Contribution to journalArticlepeer-review

Abstract

Magnetic nanoparticles have been proposed as contact-free minimal-background nanobarcodes, and yet it has been difficult to rapidly and reliably decode them in an assembly. Here, high aspect ratio nanoparticles, or magnetic nanowires (MNWs), are characterized using first-order reversal curves (FORC) to investigate quantitative decoding. We have synthesized four types of nanowires (differing in diameter) that might be used for barcoding, and identified four possible “signature” functions that might be used to quickly distinguish them. To test this, we have measured the signatures of several combination samples containing two or four different MNW types, and fit them to linear combinations of the individual type signatures to determine the volume ratios of the types. We find that the signature which determines the ratios most accurately involves only the slope of each FORC at its reversal field, which requires only 2–4 data points per FORC curve, reducing the measurement time by a factor of 10 to 50 compared to measuring the full FORC.

Original languageEnglish (US)
Article number15482
JournalScientific reports
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2020

Bibliographical note

Funding Information:
This work is based upon work supported primarily by the National Science Foundation under Grant No. CMMI-1762884. 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-1542202. Part of this work was performed at the Institute for Rock Magnetism (IRM) at the University of Minnesota. The IRM is a US 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.

Publisher Copyright:
© 2020, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, Non-U.S. Gov't

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