journal article·2023-10

One-pot synthesis of iron core—iron oxide shell nanoparticles with potential for magnetic imaging

Aleia G. Williams, Lu Liu, Charles E. Johnson, Jacqueline. A. Johnson

Journal of Nanoparticle Research· Volume 25· Issue 10
Publisher page

Abstract

Iron based nanoparticles have shown promise in biomedical applications by possessing diagnostic and therapeutic capabilities. Currently, superparamagnetic iron oxide nanoparticles (SPIONs) are the standard magnetic materials used for magnetic based imaging and hyperthermia therapeutics due to their good magnetic properties, biocompatibility, and stability. However, further improvement of iron-based nanoparticles for such applications can be achieved by creating nanoparticles that possess a pure iron core. Therefore, this study investigates a one-pot synthesis of superparamagnetic iron core–shell nanoparticles using a thermal decomposition of iron pentacarbonyl. Reaction time and surfactant quantities were modified to investigate possible size, shape, and dispersion variations. Characterizations included transmission electron microscopy, x-ray diffraction, Mössbauer spectroscopy, vibrating sample magnetometry, zeta potential, and cytotoxicity studies. From this method, core–shell, monodispersed nanoparticles averaging 14 nm in size were produced and displayed good colloidal stability and high magnetic saturation up to 130 emu/g. Iron nanoparticles were coated with 1,2- distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy- (polyethylene glycol)-2000] (ammonium salt) (DSPE-mPEG) polymer to increase biocompatibility and showed low cytotoxicity. The nanoparticles reported display high potential for magnetic imaging and magnetic hyperthermia applications.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Characterization and Applications of Magnetic Nanoparticles · Iron oxide chemistry and applications · Nanoparticle-Based Drug Delivery

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

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