Controlled surface functionalization of silica-coated magnetic nanoparticles with terminal amino and carboxyl groups
Source abstract
Abstract
General and versatile methods for the functionalization of superparamagnetic, silica-coated, maghemite nanoparticles by surface amino and/or carboxyl groups have been established. The nanoparticles were synthesized using co-precipitation from aqueous solutions and coated with a thin layer of silica using the hydrolysis and condensation of tetraethoxysilane (TEOS). For the amino functionalization, 3-(2-aminoethylamino)propylmethyldimethoxysilane (APMS) was grafted onto the nanoparticle surfaces in their aqueous suspensions. The grafting process was followed by measurements of the ζ-potential and a determination of the concentration of the surface amino groups with conductometric titrations. The surface concentration of the amino groups could be varied by increasing the amount of APMS in the grafting process up to approximately 2.3 –NH2 groups per nm2. The carboxyl functionalization was obtained in two ways: (i) by a ring-opening linker elongation reaction of the surface amines at the functionalized nanoparticles with succinic anhydride (SA) in non-aqueous medium, and (ii) by reacting the APMS and SA first, followed by grafting of the carboxyl-terminated reagent onto the nanoparticle surfaces. Using the first method, the SA only reacted with the terminal primary amino groups (–NH2) of the surface-grafted APMS molecules. Infra-red spectroscopy (ATR FTIR) and mass spectrometry (HRMS) showed that the second method enables the bonding of up to two SA molecules per one APMS molecule, since the SA reacted with both the primary (–NH2) and secondary amino (–NH–) groups of the APMS molecule. When using both methods, the ratio between the surface amino and carboxyl groups can be controlled.
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Research context
OpenAlex topics: Nanoparticle-Based Drug Delivery · Mesoporous Materials and Catalysis · Molecular Junctions and Nanostructures
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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