Lawrence Berkeley National Laboratory

Toughening polymers has attracted significant interest. Traditionally, polymer toughness is enhanced by constructing polymer networks or introducing sacrificial bonds into the chains between crosslink points. These strategies, though, introduce pronounced energy dissipation and associated heat, both of which are undesirable under long-term cyclic loading, for example at the interface of implants in the human body. By incorporating single-chain nanoparticles (SCNPs) into linear polymer chains to generate all-polymer nanocomposites (APNCs), we have been able to achieve high strength, high toughness with low energy dissipation. Using a combination of simulation and experimental results, we are advancing a “SCNPs effect” where tightly cross-linked SCNPs produce a modulus contrast to achieve strengthening and toughening. Benefitting from the soft interface, the penetrable and deformable SCNPs cause the surrounding polymer chains to move in concert, significantly reducing the interfacial friction to achieve low energy dissipation. The intramolecular cross-linking of the SCNPs and adhesion between the SCNPs and polymer matrix are critical for realizing such high-performance systems. Based on a Gaussian regression model and back propagation (BP) neural network, the mechanical strength can be predicted and is supported by simulations. The APNC concept described can be applied to elastomers and gels, broadening its utilization in high-cycle and low-dissipation applications, like soft robots, flexible sensors and cartilage replacements, and artificial heart valves.