Computational evaluation of effective storage modulus and loss factor in nano-SiO2/carbon nanofiber/polymer hybrid nanocomposites

This paper presents a computational investigation of the dynamic mechanical properties of hybrid polymer matrix nanocomposites (PMNCs) reinforced with both silica nanoparticles (SNPs) and carbon nanofibers (CNFs). A micromechanical framework, based on finite element (FE) analysis of representative volume elements (RVEs), is developed to predict the effective storage modulus and viscoelastic loss properties of the SNP/CNF/epoxy hybrid nanocomposite. The methodology utilizes the dynamic moduli of the constituent phases from established literature, while the properties of the filler-matrix interphase regions are carefully calibrated against experimental data. This calibration ensures an accurate model of both viscoelastic behavior and frictional energy dissipation. The validated model is then employed to perform a sensitivity analysis, exploring the influence of key nanostructural parameters on the effective damping performance of the nanocomposite. The results demonstrate that increasing the CNF content from 0 to 5 wt.% enhances the storage modulus by up to 50.9% and the loss factor by up to 14.8%, depending on the CNF aspect ratio. This work provides a reliable predictive tool for designing hybrid nanocomposites with tailored damping properties, offering significant contributions to academia and industries requiring high-performance, vibration-damping materials.