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Preparation and characterization of amino–silane modified superparamagnetic silica nanospheres

Open access
Liu, Xianqiao, Ma, Zhiya, Xing, Jianmin, Liu, Huizhou·2004·Journal of Magnetism and Magnetic Materials

Abstract

Magnetite nanoparticles were prepared by coprecipitation of Fe2+ and Fe3+ with NH4OH, and then, silica was coated on the surface of the magnetite nanoparticles to form well-dispersed magnetic silica nanospheres. The size of spheres was controlled by changing the ratio of SiO2 to Fe3O4. Amino-silane was covalently coupled to the surface of the magnetic silica nanospheres and activated by glutaraldehyde. The morphology and magnetic properties of magnetic silica nanospheres were examined by TEM and VSM. The nanospheres have an average size of 100-200 nm diameter and possess superparamagnetic characteristics. Powder X-ray diffraction measurements show the spinel structure for the magnetite particles coated by silica. By protein immobilization assay, it was proved that the amino-silane treated magnetic silica nanospheres could significantly improve the capacity of protein immobilization. (C) 2003 Published by Elsevier B.V.

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Metadata

Authors
Liu, X., Ma, Z., Xing, J., & Liu, H.
Venue
Journal of Magnetism and Magnetic Materials
Publisher
Elsevier BV
Published
2004-03
Volume
270
Issue
1-2
Pages
1-6
ISSN
0304-8853 (ISSN-L)
OpenAlex
W1969346899
Cited by
517
References
5
FWCI
5.93
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Preparation and characterization of amino–silane modified superparamagnetic silica nanospheres. https://app.cochat.ai/papers/10.1016/j.jmmm.2003.07.006
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Liu, X., Ma, Z., Xing, J., & Liu, H. (2004). Preparation and characterization of amino–silane modified superparamagnetic silica nanospheres. Journal of Magnetism and Magnetic Materials. https://doi.org/10.1016/j.jmmm.2003.07.006

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.