Abstract
Abstract Spinel nano-ferrite compounds have attracted significant interest in industrial, scientific and technological communities as a result of their promising and unique features especially at nano-scale range. The present and future potentials of spinel nano-ferrite materials cut across several applications such as biotechnology, magnetic storage, sensors, magnetic hyperthermia, microwave absorbance and photo-catalysis. Enhancing the photocatalytic application of spinel nano-ferrite materials involves accommodation of foreign materials into the parent compound as well as appropriate fabrication technique which respectively alter the crystal structure and nano-size of the spinel nano-ferrite materials. This work implements the crystal lattice distortion and the size of nano-particles to develop, for the first time, hybridization of a support vector regression algorithm with a genetic algorithm for estimating the energy gap of doped spinel nano-ferrite materials. The developed hybrid genetic algorithm based support vector regression model was built using two hundred different spinel nano-ferrite materials doped with varieties of materials and synthesized through various methods. The developed genetic algorithm based support vector regression model that is characterized by low root mean square error and mean squared error of 0.3075 eV and 0.095 eV respectively, was further validated using eighteen different spinel nano-ferrite materials and the estimated energy gaps agree excellently with the experimental values. The influence of magnesium, aluminum and lanthanum on the band gap of spinel ferrite nanoparticles was investigated and studied using the developed genetic algorithm based support vector regression model. The developed model in this work ultimately provides a quick, accurate and precise method of characterizing the band gap of spinel nano-ferrite materials while circumventing experimental stress with conservation of appreciable time and other valuable resources.