journal article·2021-08

Silica coating of Fe3O4 magnetic nanoparticles with PMIDA assistance to increase the surface area and enhance peptide immobilization efficiency

Alexander M. Demin, Aleksei I. Maksimovskikh, Alexander V. Mekhaev, Dmitry K. Kuznetsov, Artyom S. Minin, Alexandra G. Pershina, Mikhail A. Uimin, Vladimir Ya Shur, Victor P. Krasnov

Ceramics International· Volume 47· Issue 16
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Abstract

Abstract The high efficiency of using N-(phosphonomethyl)iminodiacetic acid (PMIDA) as a surfactant for formation of a silica coating on Fe3O4 magnetic nanoparticles (MNPs) with a large surface area has been demonstrated. The coating of PMIDA-stabilized MNPs with silica and their further APS-functionalization significantly increased the specific area (up to 203 m2 g−1) and the number of amino groups (up to 1.12 mmol/g) grafted on their surface compared to nanomaterials synthesized without preliminary SiO2-coating. The comparative study of the peptide modification efficiency, using as an example pH-low insertion peptide (pHLIP), of MNPs coated with 3-aminopropylsilane (APS) or SiO2/APS was carried out. It has been shown that silica coating of PMIDA-stabilized MNPs leads to a significant increase in the degree of immobilization of the peptide (up to 22 μmol per g of MNPs). Comprehensive characterization of the obtained materials at each stage of the synthesis was carried out using scanning electron microscopy (SEM), energy dispersive X-ray fluorescence spectroscopy (EDX), BET analysis, ATR Fourier transformed infrared spectroscopy (FTIR), termogravimetric analysis (TGA), CHN-elemental analysis, dynamic light scattering (DLS), and vibrating sample magnetometry (VSM). The proposed approach to applying SiO2-coating of MNPs can be useful for design of new materials for biomedical and chemical purposes.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Nanoparticle-Based Drug Delivery · Advanced biosensing and bioanalysis techniques · Nanocluster Synthesis and Applications

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

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