Open-source device for high sensitivity magnetic particle spectroscopy, relaxometry, and hysteresis loop tracing
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063706_1_5.0191946.pdf
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10.74 MB
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Author(s)
Mattingly, E.
Barksdale, A. C.
Śliwiak, M.
Chacon-Caldera, J.
Mason, E. E.
Wald, L. L.
Date Issued
June 26, 2024
Journal
Review of Scientific Instruments
Publisher
AIP Publishing
Citation
E. Mattingly, A. C. Barksdale, M. Śliwiak, J. Chacon-Caldera, E. E. Mason, L. L. Wald; Open-source device for high sensitivity magnetic particle spectroscopy, relaxometry, and hysteresis loop tracing. Rev. Sci. Instrum. 1 June 2024; 95 (6): 063706.
Version
Final published version
Abstract
Magnetic nanoparticles (MNPs) are used extensively across numerous disciples, with applications including Magnetic Particle Imaging (MPI), targeted hyperthermia, deep brain stimulation, immunoassays, and thermometry. The assessment of MNPs, especially those being designed for MPI, is performed with magnetic particle spectrometers, relaxometers, loop tracers, or similar devices. Despite the many applications and the need for particle assessment, there are few consolidated resources for designing or building such a MNP assessment system. Here, we describe the design and performance of an open-source device capable of spectroscopy, relaxometry, and loop tracing. We show example measurements from the device and quantify the detection sensitivity by measuring a dilution series of Synomag-D 70 nm (from 0.5 mg Fe/ml to 7 ng Fe/ml) with a 10 mT drive field at 23.8 kHz. The device measures 260 pg Fe with SNR = 1 and 1.3 ng at SNR = 5 in spectroscopy mode in under one second of measurement time. The system has a dynamic range of 60 μg to 260 pg Fe without changing the hardware configuration. As an example application, we characterize Synomag-D’s relaxation time constant for drive fields 2–18 mT and compare the magnetization responses of two commonly used MNPs.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1063/5.0191946
