Development and characterization of graphene-reinforced epoxy nanocomposites optimized via Box-Behnken design

Abstract

This work presents a statistically guided development and optimization of graphene-nanoplatelet (gNP)–reinforced epoxy nanocomposites using a Box–Behnken Design (BBD). Formulations were prepared by varying gNP content (0.1–0.5 wt%), mixing time (10–60 min), and cure temperature (30–120 ◦C). Comprehensive characterization, tensile testing, TGA, DSC, SEM/TEM, and AFM, was combined with multivariate modeling to identify optimal processing; XPS corroborated a processing-dependent, polymer-dominated interphase (high C–C, low-BE O 1s, predominantly neutral N 1s) consistent with advanced epoxy–amine curing. The predictive models showed excellent reliability (R2 = 0.99), selecting 0.1 wt% gNP, 60 min mixing, and 75 ◦C as the optimal condition. Under these parameters, the composite achieved +37 % in tensile modulus (977.2 MPa), +171 % in tensile strength (56.3 MPa), +2.5 % in Tonset (351.5 ◦C; Δ = +8.5 ◦C), and +19 % in Tg (97.2 ◦C; Δ = +15.2 ◦C) relative to neat epoxy. Crucially, non-isothermal cure-kinetics assessed by the isoconversional Ozawa–Flynn–Wall (OFW) method revealed systematically lower activation energies with gNP, Ea(α) ≈ 124–157 kJ mol􀀀 1 for epoxy/0.1 wt% gNP versus ≈ 157–224 kJ mol􀀀 1 for neat epoxy (α = 0.1–0.9), indicating facilitated curing at a given conversion without hindering network development. These results demonstrate that statistically optimized, ultralow-filler epoxy/gNP systems can surpass neat epoxy and rival higher-loading formulations, offering a scalable, cost-effective route to high-performance structural applications