Synthesis, morphology, crystallite size and adsorption properties of nanostructured Mg–Zn ferrites with enhanced porous structure
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Abstract
Abstract Mg–Zn ferrites with porous cavity structure were produced by sol-gel auto-combustion method utilizing a new mixture of dl -alanine and urea as an organic fuel-reductant. The influence of non-magnetic Zn ions content on the structural, morphological and adsorption characteristics of Mg–Zn NPs has been investigated. It was found that Zn effects the porosity of Mg–Zn ferrospinels. X-ray diffraction analysis confirmed the formation of cubic spinel phase and the crystallite size was calculated by the SSP method, W-H method and modified Scherrer formula. SEM observations revealed mesh-like microstructure with the presence of 1–2 μm macropores and agglomerated nanoparticles. The EDS spectra confirmed the desired chemical composition of ferrites powders. Mossbauer spectroscopy confirmed the valence state of cations and their distribution over the spinel sublattices. The adsorption characteristics of spinel compounds were studied on Congo Red dye removal. The appropriateness of Langmuir, Freundlich, and Dubinin-Radushkevich adsorption models were investigated and the parameters of all models were determined. The observed data fitted well by Langmuir model, indicating that the Congo Red adsorption occurred on a homogeneous surface and that the active surface centers possessed similar energy values. The Dubinin-Radushkevich isotherm model was utilized to calculate the free energy of Congo Red adsorption, i.e. setting in the range 7.81–22.4 kJ/mol and indicating that the Zn content affects the mechanism of Congo Red adsorption onto ferrite surfaces and the removal efficiency is enhanced with the rise in Zn content.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Magnetic Properties and Synthesis of Ferrites · Iron oxide chemistry and applications · Nanomaterials for catalytic reactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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