journal article·2023-02-09

Facile preparation of magnetite–cellulose nanocomposite from a sustainable resource

Kebede Gamo Sebehanie, Surafel Shiferaw Legese, Alberto Velázquez del Rosario, Abubeker Yimam Ali, Femi Emmanuel Olu

Bulletin of Materials Science· Volume 46· Issue 1
Publisher page

Abstract

Because of the synergistic effect of the individual nanomaterials, nanocomposites are becoming an important class of materials, allowing the creation of end-products with different properties and greater performance than when used individually. This study aims to create magnetite nanoparticles (MNPs) from iron ore and nanocellulose fibre (CNF) from sugar bagasse to make magnetite–cellulose nanocomposites. X-ray diffraction and Fourier-transform infrared spectroscopy were used to confirm the prepared nanomaterials, and scanning electron microscopy was used to investigate the morphology of the nanomaterials. The nanocomposites were created using two methods: (1) adding CNF before the MNPs precipitated and (2) adding CNF after the MNPs precipitated. There was a difference in crystallite size, with the smallest being 23 nm for the one prepared before precipitation and 31 nm for pure MNPs. The magnetic properties of the as-prepared nanomaterials were also investigated using the vibrating sample magnetometer technique. MNPs and MNPs–CNF nanocomposites have superparamagnetic properties, with nearly zero coercivity for all samples and saturation magnetizations of 42.8, 30.7, and 23.2 emu g–1 for pure MNPs, MNPs–CNF (before precipitation) and MNPs–CNF (after precipitation), respectively. Thermogravimetric analysis was used to investigate the thermal behaviour of the nanocomposite. The incorporation of cellulose nanofibre prevented aggregation while having a negligible effect on thermal stability.
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Research context

OpenAlex topics: Advanced Cellulose Research Studies · Electromagnetic wave absorption materials · Electrospun Nanofibers in Biomedical Applications

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

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