journal article·2022-06

Composite silicon-iron nanoparticles: physical properties and potential application in MRI contrasting

Yulia V. Kargina, Alexey V. Sobolev, Ekaterina S. Kozlyakova, Alexander N. Vasiliev, Alexander Yu. Kharin, Nina V. Sharonova, Alexander M. Perepukhov, Anna V. Stavitskaya, Anatoly A. Ischenko, Victor Yu. Timoshenko

Journal of Nanoparticle Research· Volume 24· Issue 6
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Abstract

Composite nanoparticles (NPs) based on nanocrystalline silicon (Si) with iron (Fe) content varied from 0.2 to 10 at.% are prepared by an arc-discharge plasma-ablative synthesis and investigated by means of the transmission and scanning electron microscopy, dynamic light scattering, Fourier-transform infrared spectroscopy, Mössbauer spectroscopy, electron paramagnetic resonance, magnetic susceptibility measurements, X-ray diffraction, and nuclear magnetic resonance relaxometry. It is shown that an increase in the Fe content results in an increase in the mean size of Si-Fe NPs from 30 to 400 nm. Iron-enriched agglomerates, which consist of α-FeSi2, α-Fe, and iron oxide, are detected in Si-Fe NPs with Fe content above 2.5 at.%. The magnetization relaxometry reveals a strong shortening of both the longitudinal and transverse proton relaxation times in aqueous suspensions of Si-Fe NPs. The enhanced proton relaxation is explained by the impact of both Fe ions and paramagnetic centers like Si dangling bonds, the contribution of which to the proton relaxation depends on the Fe content. In vitro experiments show that prepared Si-Fe NPs with concentration up to 50 μg/mL are characterized by low cytotoxicity, which slightly increases with increasing iron content. The obtained results demonstrate the possibility of using composite Si-Fe NPs as potential contrast agents for MRI-guided cancer theranostics. Graphical abstract
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Research context

OpenAlex topics: Crystallography and Radiation Phenomena · X-ray Spectroscopy and Fluorescence Analysis · Nanoparticle-Based Drug Delivery

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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