Taoreed Olakunle Owolabi

This study tackles the critical demand for sustainable synthesis methods of reduced graphene oxide (rGO), highlighting the environmental drawbacks of conventional chemical processes. We introduce a novel, green synthesis technique involving the irradiation of a 500 eV argon ion beam, which not only facilitates the creation of micro-patterned rGO on a graphene oxide (GO) film but also enables simultaneous material characterization and patterning. By adjusting the irradiation exposure time between 0 and 80 s, we achieve meticulous control over the attributes and the reduction process of the material. The use of X-ray photoelectron spectroscopy (XPS) allows for real-time monitoring of the reduction from GO to rGO, evidenced by a notable reduction in the intensities of C-O, C=O, and O-C=O bonds, and an increase in C-C bond intensities, indicating a significant reduction level. Our research demonstrates the efficient production of eco-friendly rGO using precise, controlled argon ion beam irradiation, proving its advantages over traditional methods. These results contribute to the development of sustainable material science technologies, with potential applications in electronics, energy storage, and more.

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.

Nanocrystalline spinel ferrite based compounds are technological driven materials with interesting potentials in photocatalysis for renewable energy generation, gas sensing for pollution control, magnetic drug delivery, rod antennas, storage media (high density) and supercapacitive materials, among others. Specific surface area of spinel ferrite based compounds contributes immensely to the application of this semiconductor in industrial domains. Experimental determination of specific surface area is laborious and costly and consumes appreciable time. Compositional substitutions in crystal structure effectively improve physical properties and enhance specific surface area through alteration of moment distribution between tetrahedral oxygen sites and octahedral coordination. With the aid of distorted lattice parameters due to compositional substitution and the spinel ferrite nanocrystallite size as model descriptors, this present work models the specific surface area of spinel ferrite nanomaterial through extreme learning machine (ELM) based intelligent modeling method. The developed sigmoid activation function-based ELM (S-ELM) model shows superior performance over genetic algorithm based support vector regression (GBSVR) and stepwise regression (STWR) models existing in the literature with performance improvement of 61.31% and 70.01%, respectively, using root mean square error performance metric. The significances of cobalt and lanthanum compositional substitution on the specific surface area of spinel ferrite nanomaterials were investigated using S-ELM model. Ease of implementation of S-ELM model as compared with the existing GBSVR model, coupled with the demonstrated improved performance and persistent closeness of its predictions with the experimental values, would be highly meritorious for quick and precise characterization of specific surface area of spinel ferrite nanomaterials for various desired applications.

Tetragonality occurs as a result of stretching the crystal structural lattice of perovskite along one of its lattice vectors such that the three axes are mutually perpendicular with two of the axes having equal lengths. This tetragonality distortion easily triggers functional properties such as pyroelectricity, ferroelectricity, capacitance and piezoelectricity among others, while synthesizing functional ceramics for a particular application. This work addresses and circumvents the challenges of experimental stress involved in functional ceramics synthesis by developing Newtonian search based support vector regression (GSB-SVR) model for perovskite tetragonality prediction using dopants concentration and ionic radii as the model predictors. The performance of proposed GSB-SVR model is compared with the existing method and better performance is obtained. The influence of lanthanides and zirconium incorporation on functional ceramics on the material tetragonality is also modeled by the developed GSB-SVR model. The precision of the developed model, its easily fetched predictors and pre-laboratory ability to effectively and efficiently model the perovskite tetragonality are of high importance in tailoring and enhancing functional properties of materials for desired applications.

Magnesium diboride (MgB₂) superconductor combines many unique features such as transparency of its grain boundaries to super-current flow, large coherence length, absence of weak links and small anisotropy. Doping is one of the mechanisms for enhancing these features, as well as the superconducting critical temperature, of the compound. During the process of doping, the MgB₂ superconductor structural lattice is often distorted while the room temperature resistivity, as well as residual resistivity ratio, contributes to the impurity scattering in the lattice of doped samples. This work develops three extreme learning machine (ELM)-based empirical models for determining MgB₂ superconducting critical temperature (T_C) using structural distortion as contained in lattice parameters (LP) of doped superconductor, room temperature resistivity (RTR) and residual resistivity ratio (RRR) as descriptors. The developed models are compared with nine different existing models in the literature using different performance metrics and show superior performance over the existing models. The developed SINE-ELM-RTR model performs better than Intikhab et al. (2021)_linear model, Intikhab et al. (2021)_Exponential model, Intikhab et al. (2021)_Quadratic model, HGA-SVR-RRR(2021) model and HGA-SVR-CLD(2021) model with a performance improvement of 32.67%, 29.56%, 20.04%, 8.82% and 13.51%, respectively, on the basis of the coefficient of correlation. The established empirical relationships in this contribution will be of immense significance for quick estimation of the influence of dopants on superconducting transition temperature of MgB₂ superconductor without the need for sophisticated equipment while preserving the experimental precision.