Synergistic fracture toughening in epoxy composites with hybrid micro- and nano-silica: a new dual-mechanism model approach
Source abstract
Abstract
Epoxy resins exhibit excellent mechanical, thermal, and adhesive properties but suffer from inherent brittleness, which restricts their suitability for high-impact structural applications. This study examined the synergistic fracture toughening effects of micro-silica, nano-silica, and hybrid micro–nano-silica fillers in epoxy composites. Composites containing 5–35 wt.% filler were prepared and evaluated for Young’s modulus, fracture toughness, and fracture energy through standardized single-edge notch bending (SENB) testing, with fracture surface morphology being characterized using field emission scanning electron microscopy (FESEM). Hybrid filler systems delivered the most significant performance gains, achieving a maximum fracture toughness of 2.55 MPa√m and fracture energy of 1318.97 J/m2 at 35 wt.% loading (17.5 wt.% micro-silica + 17.5 wt.% nano-silica), surpassing the results obtained from single-filler composites. Enhanced performance was attributed to multi-scale toughening mechanisms, including crack deflection and bridging from microparticles, and shear banding with void growth from nanoparticles. A modified Huang–Kinloch model, incorporating particle size effects, shielding interactions, and realistic void fractions, demonstrated strong predictive capability for hybrid systems, with correlation coefficients of approximately 0.90. The outcomes highlight the potential of optimally dispersed dual-scale silica fillers to achieve superior fracture resistance, providing a validated predictive framework for designing high-toughness epoxy composites in aerospace, automotive, and protective engineering applications.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Epoxy Resin Curing Processes · Polymer Nanocomposites and Properties · Mechanical Behavior of Composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
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