Noise Power Properties of Magnetic Nanoparticles as Measured in Thermal Noise Magnetometry

Abstract

Magnetic nanoparticles have proven to be extremely useful in a broad range of biomedical applications. To ensure optimal efficiency, a precise characterization of these particles is required. Thermal Noise Magnetrometry (TNM) is a recently developed characterization technique that has already been validated against other techniques. TNM offers a unique advantage in that no external excitation of the system is required to drive the measurement. However, the extremely small stochastic signal in the fT range currently limits the accessibility of the method, and a better understanding of the influences of the sample characteristics on the TNM signal is necessary. In this study, we present a theoretical framework to model the magnetic noise power properties of particle ensembles and their signal as measured via TNM. Both intrinsic sample properties (such as the number of particles or their volume) and the geometrical properties of the sample in the setup have been investigated numerically and validated with experiments. It is shown that the noise power depends linearly on the particle concentration, quadratically on the individual particle size, and linearly on the particle size for a constant total amount of magnetic material in the sample. Furthermore, an optimized sample shape is calculated for the given experimental geometry and subsequently 3d printed, giving rise to an 3.5 fold increase in TNM signal from approximately 0.007 to 0.026 pT 2 with less than half of the magnetic material.

Monitoring magnetic nanoparticle clustering and immobilization with thermal noise magnetometry using optically pumped magnetometers

“…The amplitude ⟨B 2 ⟩ of the fluctuations depends on the total magnetic moment of the MNPs, and the distance from the sample at which the magnetic field is measured. At typical distances of few mm to few cm, the TNM signal a of MNP ensemble ranges from pT-fT. Due to the stochastic nature of TNM, the amplitude of the fluctuations is given by the variance of the stochastic variable, which scales linearly with the amount of particles and quadratically with the volume of the fluctuators 31 . Therefore, TNM is fundamentally more sensitive to clustering events than corresponding deterministic magnetic characterization methods.…”

“…For deterministic processes, the signal depends linearly on both the number of magnetic moments and their amplitude, and thus also linearly on the iron concentration. However, TNM is a stochastic method, and the signal depends only on the square root of the number of magnetic moments 31 . Therefore, if only the iron concentration of the sample is known, the number of magnetic moments and the amplitude of the moments cannot be decoupled, and a meaningful normalization is impossible.…”

“…tive magnetic field sensors. Superconducting Quantum Interference Devices (SQUIDs), which were used in previous TNM studies [29][30][31] , have a well established reputation in magnetometry and biomedical applications. Their success is attributed to their excellent sensitivity, wide bandwidth, and durability [32][33][34] .…”

“…Compared to other characterization techniques, the signals in TNM are small (in the sub-picoTesla range) and require sensitive magnetic eld sensors. Superconducting quantum interference devices (SQUIDs), which were used in previous TNM studies, [29][30][31] have a well-established reputation in magnetometry and biomedical applications. Their success is attributed to their excellent sensitivity, wide bandwidth, and durability.…”

Perspective: magnetic nanoparticles in theranostic applications

“…Additionally, this technique offers a unique and sensitive window into the aggregation state of the particles thanks to the underlying scaling laws of some of the noise properties with respect to particle clustering. This can be exploited in applications, e.g., to monitor the particle aggregation in immunoassays with increased accuracy 86 .…”