Thursday  |  Conference Center A  |  02:50 PM–03:10 PM

#13960, Forward Prediction of Mechanical Properties of Polymer Nanocomposites

To enable the forward prediction of mechanical properties in polymer nanocomposites (PNCs), a novel framework that uses experimental methods and finite element analysis (FEA) has been developed. The validity of this framework is demonstrated for a PNC made of a polydimethylsiloxane (PDMS) matrix and silica particles (SiO2). Atomic force microscopy (AFM) is used to determine the microstructure distribution and the local mechanical properties of the extent of the interphase, and dynamic mechanical analysis (DMA) is used to determine the macroscale properties of the PDMS matrix and the PNCs. We hypothesize that by accurately determining the local properties in the interphase we can predict the behavior of the macroscale nanocomposite; as the interphase between the polymer matrix and filler has altered properties that contribute to the enhancement of properties observed polymer nanocomposites. A key challenge in accurately determining the extent of the interphase around a nanoparticle via AFM occurs as a result of the complex contact conditions between the AFM tip and the polymer near an interphase. A method similar to one used in nanoindentation has been used to address this and eliminate the indentation depth dependence of the local property measurements. Consequently, we have performed FEA to predict the macroscale properties of the PNCs and validated them with experimental data for PNC from DMA. This framework provides a method to achieve the forward prediction of mechanical properties of polymer nanocomposites with limited experimental work, optimizing the process to discover and develop materials with desired properties.

Heer Majithia   Duke University
Boran Ma   Duke University
L. Catherine Brinson   Duke University