Amino-Functionalized Fe3O4@SiO2 Core-Shell Magnetic Nanoparticles for Dye Adsorption.
Chun-Rong LinOxana S IvanovaDmitry A PetrovAlexey E SokolovYing-Zhen ChenMarina A GerasimovaSergey M ZharkovYaw-Teng TsengNicolay P ShestakovIrina S EdelmanPublished in: Nanomaterials (Basel, Switzerland) (2021)
Fe3O4@SiO2 core-shell nanoparticles (NPs) were synthesized with the co-precipitation method and functionalized with NH2 amino-groups. The nanoparticles were characterized by X-ray, FT-IR spectroscopy, transmission electron microscopy, selected area electron diffraction, and vibrating sample magnetometry. The magnetic core of all the nanoparticles was shown to be nanocrystalline with the crystal parameters corresponding only to the Fe3O4 phase covered with a homogeneous amorphous silica (SiO2) shell of about 6 nm in thickness. The FT-IR spectra confirmed the appearance of chemical bonds at amino functionalization. The magnetic measurements revealed unusually high saturation magnetization of the initial Fe3O4 nanoparticles, which was presumably associated with the deviations in the Fe ion distribution between the tetrahedral and octahedral positions in the nanocrystals as compared to the bulk stoichiometric magnetite. The fluorescent spectrum of eosin Y-doped NPs dispersed in water solution was obtained and a red shift and line broadening (in comparison with the dye molecules being free in water) were revealed and explained. Most attention was paid to the adsorption properties of the nanoparticles with respect to three dyes: methylene blue, Congo red, and eosin Y. The kinetic data showed that the adsorption processes were associated with the pseudo-second order mechanism for all three dyes. The equilibrium data were more compatible with the Langmuir isotherm and the maximum adsorption capacity was reached for Congo red.
Keyphrases
- aqueous solution
- magnetic nanoparticles
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- quantum dots
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- high resolution
- electronic health record
- single cell
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- walled carbon nanotubes
- big data
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- magnetic resonance imaging
- solid state
- mass spectrometry
- molecular dynamics
- deep learning
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- optical coherence tomography
- solar cells