Effect of functionalization of graphene nanoplatelets on the mechanical response of hybrid graphene-fiber reinforced epoxy laminates
Source abstract
Abstract
This study examines the mechanical and fracture behavior of glass fiber-reinforced epoxy laminates enhanced with graphene nanoplatelets (GNPs) to explore the effect of GNP through-thickness distributions on tensile, pin-bearing, and in-plane shear performance. Four symmetric and asymmetric functionally graded-nanocomposite (FGNCs) were produced through a systemic procedure and examined versus homogeneous (HGNC) and pure glass-fiber (PG) laminates. Furthermore, the fracture morphologies were examined and assessed using image processing technique. The results showed that the strategic distribution of GNP significantly altered the strength, stiffness, and damage resistance of the laminates. The modified stiff-surface layers of FG laminates produced enhanced tensile performance, reaching up to 9% higher strength and 8% greater stiffness than the PG laminate. In-plane shear results revealed that HGNC enhanced shear strength by 29.3%, while symmetric gradation structures in FGNC2 and FGNC3 exhibited enhanced shear response, increasing peak load by 16.3% and shear strength to 22.3%. In pin-bearing tests, certain FGNC laminates showed improved bearing strengths and more consistent load–displacement responses through mitigating fiber tearing and stress arising around the hole. Fracture analysis showed a change from PG's brittle matrix failure to more controlled damage in GNP-modified structures. The damage extent in PG reduced from D a = 167.04 mm 2 to 12.44 mm 2 in FGNC4, and it was the lowest in FGNC3. SEM and microscopy analyses confirmed decreased delamination and increased crack-bridging behavior, especially in FGNC3 with surface-rich GNP.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Graphene research and applications · Carbon Nanotubes in Composites · Natural Fiber Reinforced Composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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