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This paper addresses the performance study on, low viscosity, nano-modified adhesives by graphene. For achieving this goal, single-lap joints following ASTM D 5868-01 were manufactured and tested. X-ray diffraction, scanning electron microscopy and nanoindentation were employed for graphene based nanostructures characterization. The increase on joint strength was around 57% when compared against the control group. Furthermore, all failures for the nano-modified adhesive were cohesive failure for the carbon fibre/epoxy composites indicating that the adhesive was tested. X-ray diffractions signatures indicate formation of nano-structures with 17-19 nm diameters. Moreover, nanoindentation tests revealed a homogeneous dispersion of graphene.

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This paper deals with the post fire behavior of hybrid nanocomposites under dynamic loadings. A series of tests were performed to investigate how nanoparticles (i.e. nanoclay and graphene nanosheets) affect the post-fire overall composite behavior. Carbon fiber/epoxy-nanoclay and carbon fiber/epoxy-graphene nanosheets were manufactured. The nanoparticles employed were Cloisite 30B nanoclay, and surface modified graphene nanosheets. The epoxy system used was RemLam M/HY956. The nanocomposites were made using ultrasonic mixer for nanoparticle dispersion in acetone followed by a shear mixing of acetone/nanoparticle/hardener. The following steps involved degassing, the addition of resin to the mixture and, the hand lay-up with vacuum assisted cure. Thermo gravimetric analysis (TGA) indicates an average decrease on peak mass loss around 41% with the addition of small amount of nanoparticles. The sample plates were exposed to a heat flux of 800 kW.m -2 for a period up to 120 seconds. The post-fire low velocity impact tests indicated the impact resistance degraded as a function of heat exposure. However, the addition of nanoclay leads to an increase on impact peak force of 11.69%. The carbon oxidation could be the main cause of the increase on impact peak load is lower than expected, only 6.72%. The model predictions are overestimated by approximately 8%. Even though, it can be a good tool for composites design.

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