(482c) Complex Nanostructured Materials with Nonrandom Disorder:a Path to Sustainability

High-performance resource-conscious composites represent the critical bottleneck in nearly many modern technologies. Following the blueprints from Nature, these uniquely capable materials always contain a superposition of order and disorder that can also be cumulatively described as material’s complexity. Performance and complexity are inherently intertwined because repeatable structural patterns at different scales must be synergistically integrated to obtain tunable combinations of application-specific properties. While being nonrandom, the structural patterns with large degree of stochasticity make it difficult to describe these complex materials using methodologies developed for crystals, quasicrystals, and glasses. Recently we showed that structural patterns of high-performance nanocomposites can be accurately described, purposefully designed, and scalably reproduced using graph theory (GT), which is equally applicable to biological tissues and their technological replicas (Fig. 1). Graphs, i.e. sets of nodes and edges, are able to capture both ordered and disordered components of high-performance composites. GT descriptors can quantify the structural pattern with both short- and long-range regularities. The fundamental significance of GT models as ‘chemical formulas’ of nanostructures and their practical utility in the materials design will be demonstrated by cartilage-like composites based on aramid nanofibers (ANFs, Fig. 1C,D) for Zn ion batteries, Li-S batteries, and supercapacitors. The graph-property relations will be demonstrated based on ANF composites, nanoparticle gels, layered nanocomposites, and hierarchically organized hedgehog particles. The designed materials include ion-conducting, chiroptical and catalytic nanocomposites with unique combinations of toughness, solvent dispersibility, polarization rotation, temperature resilience and chemical activity.