Emergence of Polymer-Networked Nanoparticle Structures as Primitive Neuromorphic Computing States

Polymer-networked nanoparticles are a promising alternative to silicon semiconductors for the realization of neuromorphic computing platforms. Variations in the interaction between gold nanoparticles (AuNPs) and polyelectrolyte linkers lead to the controlled formation of engineered nanoparticle network (ENPN) structures exhibiting a broad range of topologies and dynamics. Using dissipative particle dynamics (DPD) simulations, we designed triblock copolymers with polyelectrolyte ends that can selectively attach to each of two AuNPs and bridged them together through a middle polymer segment (or block). We leverage our earlier finding that AuNPs have well-defined valencies─that is, an optimal number of polymers that can fit (or fill) their surface, for a specific choice of the outer blocks at a given polymer length. The precise selection of the AuNP valence allows for controlled binding between the polymers and AuNPs. Meanwhile, the choice of the middle block enables control over internanoparticle spacing and network topology. We found that ENPNs can achieve distinct and stable states, satisfying a necessary condition for primitive neuromorphic computing. By swapping the surface coating ligand from citrate to mercaptopropionic acid (MPA), the valence on a given nanoparticle is also increased. Thus, we found that the selection of the surface coating consequently affects the designed ENPN structures, allowing for more flexibility in searching for the optimal components.