Abstract
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.
Highlights
Spinel ferrite nanomaterials have gained a significant and considerable attention lately due to their unique chemical, physical, magnetic, electrical, and optical features that are of great interest in many technological advancement and applications such as gas sensor, drug-delivery, photocatalysts, water splitting, spintronics, and supercapacitors [1,2,3,4]
This work models the specific surface area of spinel ferrite nanomaterials doped with foreign materials through stepwise regression-based model and hybrid genetic algorithmbased support vector regression (GBSVR) intelligent computational method using lattice parameter and the size of the nanomaterial as descriptors to the models
The outcomes of the developed hybrid GBSVR model are discussed
Summary
Spinel ferrite nanomaterials have gained a significant and considerable attention lately due to their unique chemical, physical, magnetic, electrical, and optical features that are of great interest in many technological advancement and applications such as gas sensor, drug-delivery, photocatalysts, water splitting, spintronics, and supercapacitors [1,2,3,4]. The specific surface area of spinel ferrite nanomaterials contributes significantly to their technological applications especially during organic pollutant treatment [6,7,8]. Tuning of specific surface area of spinel ferrite nanomaterials is often carried out experimentally. Journal of Nanomaterials through doping whereby foreign and external materials are incorporated into the parent spinel ferrite ceramic compounds and leads to alteration in magnetic, electrical, and optical properties coupled with change in specific surface area of the nanomaterials [12,13,14,15]. This work models the specific surface area of spinel ferrite nanomaterials doped with foreign materials through stepwise regression-based model and hybrid genetic algorithmbased support vector regression (GBSVR) intelligent computational method using lattice parameter and the size of the nanomaterial as descriptors to the models
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
