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[16p-P08-4]Relaxometry Imaging of Conducting Magnetite Layers on a Core-Shell Superparamagnetic Particle Using Ensemble Nitrogen-Vacancy in Diamond

〇(D)Thitinun Gasosoth1, Kunitaka Hayashi1, Dwi Prananto1, Toshu An1 (1.JAIST)
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Keywords:

Relaxation time,longitudinal spin relaxation,magnetite

Magnetic particles, especially superparamagnetic particle microbeads which consist of a magnetite (Fe3O4) layer covering the shell of the particle, have been used in many medical applications and biological separations. However, Fe3O4 is a conductive material where the electrons in the conductor are excited by thermal energy and produce fluctuating magnetic noise in the environment [1]. Although randomly diffusing electrons exhibit a zero average field, in a plane layer, the magnetic noise projected in the vertical direction is non-zero [1]. The Nitrogen vacancy (NV) center in diamonds is one of the most sensitive quantum sensors for magnetometry in many nanoscale applications. In this work, we demonstrate relaxometry imaging, based on the reduced lifetime of longitudinal spin relaxation time, T1, of a proximal ensemble NV center in a diamond [2]. An electronic grade (100)-oriented diamond was implanted with 1x1012 ion/cm2 of 14N+ at the energy of 30 keV and annealed at 900oc for 1 hour to create the NV defect center about 30 nm below the sample. The diamond sample was polished to 50 μm thick and cut into about 100 μm x 100 μm rectangular shape by a high-power laser [3]. 3 superparamagnetic particles with 3 μm diameter were prepared on a glass slide, separated and covered by the rectangular NV diamond sample with the NV layer side down. (Fig. 1(a) upper inset). The NVs in the diamond with magnetic particles were evaluated by a home-built confocal microscope. The shortened relaxation time T1 of the NV ensemble, due to random magnetic noise in high electrical conductivity of the superparamagnetic magnetite shell layer in a spherical core-shell structure, was measured and imaged. The spatial dependence of spin relaxation time T1 with proximity dependence modeling was shown in Fig. 1(c).

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