Modeling of Time-Dependent Mechanical Behavior of Oleic Acid Nanocomposites Using Nanoindentation

Supercrystalline nanocomposites are a burgeoning class of hybrid inorganic-organic materials. Studies showed that self-assembly of iron oxide particles surface-functionalized with organic (e.g. oleic acid) ligands produces a supercrystalline nanocomposite with exceptional mechanical properties. Consequently, significant research has been conducted on these materials to experimentally characterize the mechanical properties of such materials. However, so far all modeling studies used time and rate independent elastoplastic material models. In the light of new experimental results, we propose to extent this view and use time-dependent models to capture viscoelastic behavior. To this end, we quantified this behavior using nanoindentation creep experiments and modelled it using a rheological network model with several parallel Maxwell branches and an additional elasto-plastic branch. We demonstrate how the parameters of such a model can be found using inverse analysis. With the calibrated material model, a good agreement of the time dependent behavior between simulation and experimental results is achieved. Thus, a method is provided to model time dependent behavior using complex non-classical experiments like nanoindentation.