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.

中文翻译：

---

使用混合智能计算方法模拟掺杂尖晶石铁氧体纳米材料的比表面积

尖晶石铁氧体纳米材料是具有优异化学、磁性、电学和光学特性的磁性半导体，这使得该材料可用于许多技术驱动的应用，如太阳能制氢、数据存储、磁传感、转换器、电感器、自旋电子学和催化剂。这些纳米材料的表面积对它们的目标应用以及观察到的物理和化学特征有很大贡献。实验掺杂在增强和调整尖晶石铁氧体纳米材料的比表面积方面显示出巨大的潜力，而归因的实验挑战需要可行的理论模型，可以高精度地估计掺杂的尖晶石铁氧体纳米材料的表面积。这项工作开发了逐步回归 (STWR) 和基于混合遗传算法的支持向量回归 (GBSVR) 智能模型，用于使用晶格参数和纳米颗粒尺寸作为模型描述符来估计掺杂尖晶石铁氧体纳米材料的比表面积。当使用实验测量的掺杂尖晶石铁氧体纳米材料的比表面积进行验证时，使用相关系数和均方根误差作为性能指标，开发的混合 GBSVR 模型的性能优于 STWR 模型，性能分别提高了 7.51% 和 22.68%。开发的 GBSVR 模型研究了镍、钇和镧纳米粒子对不同类别尖晶石铁氧体纳米材料的比表面积的影响，得到的结果与测量值非常吻合。表征开发模型的准确性和精度对于提高掺杂尖晶石铁氧体纳米材料预测的比表面积、规避实验应力和降低成本具有极其重要的意义。