Spinel ferrites nanomaterials are magnetic semiconductors with excellent chemical, magnetic, electrical, and optical properties which have rendered the materials useful in many technological driven applications such as solar hydrogen production, data storage, magnetic sensing, converters, inductors, spintronics, and catalysts. The surface area of these nanomaterials contributes significantly to their targeted applications as well as the observed physical and chemical features. Experimental doping has shown a great potential in enhancing and tuning the specific surface area of spinel ferrite nanomaterials while the attributed experimental challenges call for viable theoretical model that can estimate the surface area of doped spinel ferrite nanomaterials with high degree of precision. This work develops stepwise regression (STWR) and hybrid genetic algorithm-based support vector regression (GBSVR) intelligent model for estimating specific surface area of doped spinel ferrite nanomaterials using lattice parameter and the size of nanoparticle as descriptors to the models. The developed hybrid GBSVR model performs better than STWR model with the performance improvement of 7.51% and 22.68%, respectively, using correlation coefficient and root mean square error as performance metrics when validated with experimentally measured specific surface area of doped spinel ferrite nanomaterials. The developed GBSVR model investigates the influence of nickel, yttrium, and lanthanum nanoparticles on the specific surface area of different classes of spinel ferrite nanomaterials, and the obtained results agree excellently well with the measured values. The accuracy and precision characterizing the developed model would be of immense importance in enhancing specific surface area of doped spinel ferrite nanomaterial prediction with circumvention of experimental stress coupled with reduced cost.

Growing environmental concerns have increased the scientific interest in the utilization of natural fibers for the development of epoxy biocomposite materials. The incorporation of one or more fibers in the production of hybrid epoxy polymer composites has been a subject of discussion. It is interesting to acknowledge that natural/synthetic fiber hybridized epoxy composites have superior properties over natural/natural fiber hybridized epoxy composites. Significant efforts have been devoted to the improvement of natural fiber surface modifications to promote bonding with the epoxy matrix. However, to achieve sufficient surface modification without destroying the natural fibers, optimization of treatment parameters such as the concentration of the treatment solution and treatment time is highly necessary. Synthetic and treated natural fiber hybridization in an epoxy matrix is expected to produce biocomposites with appreciable biodegradability and superior mechanical properties by manipulating the fiber/matrix interfacial bonding. This paper presents a review of studies on the processing of epoxy natural fiber composites, mechanical properties, physical properties such as density and water absorption, thermal properties, biodegradability study, nondestructive examination, morphological characterizations, and applications of epoxy-based natural fiber biocomposites. Other aspects, including a review of variables that enhance the mechanical and functional performance of epoxy/natural fibers composites while also increasing the biodegradability of the composite material for environmental sustainability, were presented. The future research focus was elucidated. It is hoped that this review will stimulate and refocus research efforts toward advancing the manufacture of epoxy/natural fiber composites to meet the growing demand for biocomposite materials in the global world.

Structural transformation and magnetic ordering interplays for emergence as well as suppression of superconductivity in 122-iron-based superconducting materials. Electron and hole doping play a vital role in structural transition and magnetism suppression and ultimately enhance the room pressure superconducting critical temperature of the compound. This work models the superconducting critical temperature of 122-iron-based superconductor using tetragonal to orthorhombic lattice (LAT) structural transformation during low-temperature cooling and ionic radii of the dopants as descriptors through hybridization of support vector regression (SVR) intelligent algorithm with particle swarm (PS) parameter optimization method. The developed PS-SVR-RAD model, which utilizes ionic radii (RAD) and the concentrations of dopants as descriptors, shows better performance over the developed PS-SVR-LAT model that employs lattice parameters emanated from structural transformation as descriptors. Using the root mean square error (RMSE), coefficient of correlation (CC) and mean absolute error as performance measuring criteria, the developed PS-SVR-RAD model performs better than the PS-SVR-LAT model with performance improvement of 15.28, 7.62 and 72.12%, on the basis of RMSE, CC and Mean Absolute Error (MAE), respectively. Among the merits of the developed PS-SVR-RAD model over the PS-SVR-LAT model is the possibility of electrons and holes doping from four different dopants, better performance and ease of model development at relatively low cost since the descriptors are easily fetched ionic radii. The developed intelligent models in this work would definitely facilitate quick and precise determination of critical transition temperature of 122-iron-based superconductor for desired applications at low cost with experimental stress circumvention.

Zinc selenide (ZnSe) nanomaterial is a binary semiconducting material with unique features, such as high chemical stability, high photosensitivity, low cost, great excitation binding energy, non-toxicity, and a tunable direct wide band gap. These characteristics contribute significantly to its wide usage as sensors, optical filters, photo-catalysts, optical recording materials, and photovoltaics, among others. The light energy harvesting capacity of this material can be enhanced and tailored to meet the required application demand through band gap tuning with compositional modulation, which influences the nano-structural size, as well as the crystal distortion of the semiconductor. This present work provides novel ways whereby the wide energy band gap of zinc selenide can be effectively modulated and tuned for light energy harvesting capacity enhancement by hybridizing a support vector regression algorithm (SVR) with a genetic algorithm (GA) for parameter combinatory optimization. The effectiveness of the SVR-GA model is compared with the stepwise regression (SPR)-based model using several performance evaluation metrics. The developed SVR-GA model outperforms the SPR model using the root mean square error metric, with a performance improvement of 33.68%, while a similar performance superiority is demonstrated by the SVR-GA model over the SPR using other performance metrics. The intelligent zinc selenide energy band gap modulation proposed in this work will facilitate the fabrication of zinc selenide-based sensors with enhanced light energy harvesting capacity at a reduced cost, with the circumvention of experimental stress.

No abstract