摘要:
Polymer nanocomposites (PNCs) represent a radical alternative to conventional filled polymers or polymer blends. In contrast to conventional composites, where the in- cluded phase is on the order of micrometers, PNCs are defined as those that have discrete constituents on the order of a few hundred nanometers. The value of PNCs is not solely based on tailoring mechanical properties, as in traditional composite design and manufacture, but rather on the potential for the design and optimization of multi-functional properties. There is major interest in these polymeric materials embedded with a conductive nanoscale filler. This is due to the possibility of design- ing a composite that retains the easy processing of plastics but can take advantage of a conductive nanoscale phase; providing electrical conductivity in additional to structural reinforcement. The challenge to modeling multi-functional properties of PNCs is that, tradition- ally, different models have been applied to model different properties. Mechanical properties are most often modeled using mean-field models from micromechanics; properties depend on the microstructural arrangement of the included phase, phase properties and volume fraction. Electrical conductivity has primarily been modeled using percolation theory and power-law models; properties depend on theoretical or simulation based estimates of a percolation threshold, phase properties and volume fraction. However, models of both properties should ideally be built on specific mi- crostructural descriptions as well as include a probabilistic framework to capture percolation effects. In this work a modeling framework, developed for predicting composite mechan- ical properties, is investigated for its applicability in modeling effective electrical composite properties. The basis for using a micromechanics approach for predictions of conductivity is presented as well as how the model is adapted to model conductiv- ity. A comparison of the adapted and