Computational Study on Assembly of Colloidal Nanoparticles using Kinetic Monte Carlo Simulation

Computational Study on Assembly of Colloidal Nanoparticles using Kinetic Monte Carlo Simulation
Computational; Colloidal; Nanoparticles; Kinetic Monte Calro; Clustering; Scale invariant; Fractal; Dimension; polymer gel; Janus nanoparticles
Issue Date
International Conference on Electronic Materials and Nanotechnology for Green Environment (ENGE 2014)
Colloidal dispersion of nanoparticles (CNPs) has interesting properties both in terms of fundamental studies and industrials applications. Particular focus on the phase separation dynamics of CNPs has been necessary for understanding how exactly and fast CNPs are assembled and for controlling the assembly structure and dynamic properties. For understanding and controlling assembly structure and dynamics of CNPs, theoretical analysis with computational approaches supported by experimental validation is necessary. To address the phase separation of CNPs, I present computational studies on two main mechanisms; (1) cluster formation and (2) spinodal decomposition. For the computational study, kinetic Monte Carlo (KMC) algorithms were applied. For the cluster formation, firstly, the scaling behavior of the cluster growth was analyzed. To verify the computational analysis, a kinetic model based on rate theory (RT) was used to analyze the temporal evolution of the monomer and clusters concentrations. The KMC simulations agreed well with the RT predictions for the scaling behaviors. Secondly, for the spinodal decomposition of CNPs, free energy calculated from the phase field model was considered and a KMC algorithm (free energy-limited next reaction method (FENRM)) was applied. Computer simulations based on the FENRM exhibited the typical temporal evolution of microstructures in the course of phase separation governed by spinodal decomposition except for the existence of an intermediate stage before the late stage. The proposed KMC algorithm successfully described dynamics of unstable systems by capturing the critical characteristics of the microstructural evolution and phase separation with the observation of novel dynamic properties. We expect that the KMC algorithm can be; 1) used to design and control the colloidal quality of NP dispersions by understanding the cluster growth behavior and its dynamics and 2) extended and
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