Dielectric properties of polymer nanocomposite interphases from electrostatic force microscopy using machine learning

The interphase region between nanofillers and polymer matrix drastically effects the properties of nanocomposites but is hard to characterize due to nano-scale dimensions. Electrostatic force microscopy (EFM) provides a pathway to local dielectric property measurements but extracting local dielectric permittivity in complex interphase geometries from EFM measurements remains a challenge. In this work, we report a protocol of coupling experimental measurements and numerical simulations of EFM through machine learning to extract interphase dielectric permittivity in tailored silica-based nanocomposites. Silica nanoparticles were grafted with polyaniline brush of high dielectric constant to act as interphase and dispersed in polymethacrylate. We performed EFM measurements under DC polarization and generated force gradient scan across the interphase. Numerical simulations were carried out in COMSOL to match the experimental scan across this interphase to get its dielectric permittivity. Due to convolution of signals from different regions and unknown parameters in the experimental setup, we used a machine learning model to get to the best fit between the two profiles.