Synthesis and Characterization of Magnetite (Fe3O4) Nanoparticles from Iron Sand as a Catalyst in Biodiesel production using waste cooking oil

Authors

  • Samaila Muazu Batagarawa Department of Pure and Industrial Chemistry, Umaru Musa Yar’adua University, Katsina, Katsina State, Nigeria Author
  • Ahmed Lawal Mashi Department of Pure and Industrial Chemistry, Umaru Musa Yar’adua University, Katsina, Katsina State, Nigeria Author
  • Sada Bello Department of Pure and Industrial Chemistry, Umaru Musa Yar’adua University, Katsina, Katsina State, Nigeria Author

DOI:

https://doi.org/10.33003/

Keywords:

Magnetite, Haemetite, Biodiesel, Heterogeneous catalyst, recycle

Abstract

Iron sand is a readily available natural material. This study involved the extraction of 
magnetite (Fe3O4), an iron oxide material, from locally accessible iron sand. co-precipitation 
method was employed using hydrochloric acid and ammonium hydroxide. The 
characterization methods for the catalysts were XRD, SEM, FTIR, and Magnetic 
susceptibility. From the XRD analysis, 76% of the sample appeared as hematite but changed 
to magnetite (55%) when activated and 25% as hematite. Furthermore, the extracted Fe3O4 
was used as a catalyst for producing biodiesel from waste cooking oil. The variables studied 
were the catalyst loading (0.25-2.00 g) and duration of reaction (200 m). The results revealed 
that all parameters influence the transesterification experiment for biodiesel production. FT
IR, basic back titration, and ASTM methods characterized the biodiesel produced. 
Optimization of reaction parameters was performed, and a maximum yield of 97.3% was 
obtained using the conditions of a 1.125 g catalyst load, a 6:1 methanol to oil ratio, 115 
minutes of reaction time, and a reaction temperature of 65 °C. After one cycle of catalyst 
regeneration, a 92% yield was obtained. Remarkably, a yield of 97.3% was obtained when the 
transesterification reaction was carried out according to the model's recommended conditions, 
which was in line with the model's predicted value 

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Published

2024-12-12