Statistical Optimization of Graphene Nanoplatelet-Reinforced Epoxy Nanocomposites via Box–Behnken Design for Superior Flexural and Dynamic Mechanical Performance

Abstract

Graphene nanoplatelets (GNPs) are efficient nanofillers for improving the mechanical and thermal properties of epoxy resins due to their high stiffness, aspect ratio, and interfacial reinforcement ability. This study employs a three-factor, three-level Box–Behnken Design (BBD) to investigate the combined effect of GNP content (0.5–3.5 wt.%), hardener concentration (9–17 phr), and post-curing temperature (30–120 °C) on DGEBA/TETA epoxy nanocomposites. Mechanical, thermal, dynamic mechanical, and morphological characterizations (flexural testing, DMA, TGA, DSC, FTIR, SEM, TEM, and AFM) established structure–property correlations. The optimized formulation (2.0 wt.% GNP, 9 phr hardener, and 120 °C post-curing) exhibited superior reinforcement, with flexural strength of 322.0 ± 12.8 MPa, flexural modulus of 9.7 ± 0.5 GPa, and strain at break of 4.4 ± 0.2%, corresponding to increases of 197%, 155%, and 91% compared with neat epoxy. DMA confirmed a rise in storage modulus from 2.9 to 7.5 GPa and a Tg of 143 °C, while TGA showed a 15 °C improvement in thermal stability. Statistical analysis identified post-curing temperature as the dominant factor governing Tg, stiffness, and thermal stability, with synergistic contributions from GNP content and hardener concentration to the overall network performance. These results surpass those of GO- and CNT-based systems, demonstrating the superior efficiency of GNPs under optimized conditions. The proposed approach provides a robust pathway for developing epoxy nanocomposites with low filler content and enhanced multifunctional performance.