Tailoring The Mechanical Performance Of Epoxy Resin By Various Nanoparticles

Flexible organic elastomeric nanoparticles (ENP) and two kinds of rigid inorganic silica nanoparticles were dispersed respectively into a bisphenol-A epoxy resin in order to tailor and compare the performance of mechanical properties. It was found that the well-dispersed flexible ENP greatly enhanced the toughness of the epoxy with the cost of modulus and strength. Comparatively, the rigid silica nanoparticles improved Young's modulus, tensile strength and fracture toughness simultaneously. Both fumed and sol-gel-formed nanosilica particles conducted similar results in reinforcing the epoxy resin, although the latter exhibited almost perfect nanoparticle dispersion in matrix. The toughening mechanisms of nanocomposites were further discussed based on fractographic analysis.

A dynamic coupling model for hybrid atomistic-continuum computations

A dynamic coupling model is developed for a hybrid atomistic-continuum computation in micro- and nano-fluidics. In the hybrid atomistic-continuum computation, a molecular dynamics (MD) simulation is utilized in one region where the continuum assumption breaks down and the Navier-Stokes (NS) equations are used in another region where the continuum assumption holds. In the overlapping part of these two regions, a constrained particle dynamics is needed to couple the MD simulation and the NS equations. The currently existing coupling models for the constrained particle dynamics have a coupling parameter, which has to be empirically determined. In the present work, a novel dynamic coupling model is introduced where the coupling parameter can be calculated as the computation progresses rather than inputing a priori. The dynamic coupling model is based on the momentum constraint and exhibits a correct relaxation rate. The results from the hybrid simulation on the Couette flow and the Stokes flow are in good agreement with the data from the full MD simulation and the solutions of the NS equations, respectively. (c) 2007 Elsevier Ltd. All rights reserved.