Python Based Property Evaluation of Co-Zn Ferrite: Using results of XRD and FTIR

Abstract

The study focuses on making a new material by mixing different combinations of cobalt, zinc, and iron with sodium hydroxide. These formed materials are known as ferrite. The main goal of this study is to investigate how the properties of synthesized ferrite change as we change the composition. These types of ferrites have practical uses in industries like electronics, magnets, nuclear energy sector, and healthcare. By understanding how various variations in material's composition affect the properties of material researchers can design materials which have higher efficiency and greater longevity. Based on the result of the above study this research presents a further investigation into the analysis of Co-Zn substituted ferrite nanoparticles by applying advanced data analytics using Python. Python allows for statistical evaluation, processing and modeling, as well as visualization of XRD for structural analysis of material and FTIR data for functional group analysis of material. Different aspects of XRD analysis cover the intensity vs. 2Theta spectrum visualization which gives various information about composition of material, identification of peaks, Scherrer methodbased crystallite size estimation, and determination of lattice parameters. FTIR techniques involve spectral peaks identification, functional groups assignment, and an estimation of the material’s band gap. The integration of these technologies provides an improved understanding of spinel phase formation, nanoparticle size distribution, and other structural changes in material. By integrating FTIR analysis, peak detection, functional group identification, and FWHM-based crystallite size estimation, we can obtain better interpretation of experimental data, which leads to enhanced insights into material properties. This procedure contributes significantly to advancing research in nanoscience and the thorough characterization of synthesized materials.