(362n) Spin-Ising Potts Model for Network Structure Prediction in Regular Arrays of Engineered Nanoparticles

Polymer linked nanoparticle networks have various applications in drug delivery, flexible electronics, and new computing materials. Revealing the mechanism of polymer network formation between nanoparticles is critical to developing high performance soft materials. I will present a three states Spin-Ising Potts model capable of predicting the network structure in regular arrays of polymer linked engineered nanoparticles (ENPs). Both Monte-Carlo (MC) simulation and mean-field theory (MFT) methods are used to solve the Potts model. I will show that MFT and MC simulations of our Potts model are in good agreement in describing the thermodynamics of its network connections for three different types of ENP regular arrays, viz. 2D square, 2D hexagonal, and 3D cubic. The theoretical predictions from the Potts model are also compared with dissipative particle dynamics (DPD) simulations. At various temperatures, I will show agreement between the Potts DPD models in calculating the nearest neighbor links for these simple regular arrays with isotropic network structures. However, DPD simulations take much more computational resources and data analysis time to reach the equilibrium result. More importantly, our Potts model is capable of predicting network structures under an external E-field polarization, where the DPD model is not able to reach equilibrium.