(310d) Two-Point Correlation Function Based Statistical Approach for Tracking Morphological Changes in Nanoparticle Systems

During the synthesis of nanoparticles and nanoclusters, their size, shape, morphology, porosity and number density can vary as a function of time. The exact dynamics depends on the synthesis conditions, and questions regarding the sensitivity towards the synthesis parameters arise. Experimental imaging techniques and stochastic modeling methods can generate spatially-resolved snapshots of the nanoparticle system. Given the randomness inherent in these images, directly comparing two snapshots may not convey the relative importance of the underlying factors. In this context, statistical techniques are better suited for tracking the dynamical transformation in such systems. In this talk, I introduce a technique based on the 2-point correlation (2PC) function for studying morphological transformations in nanoparticle systems. For our model system, the nanoparticle agglomeration dynamics is simulated using the kinetic Monte Carlo (KMC) method. The progress of the nanoparticle is quantified in terms of percentage transformation. The effect of the nanoparticle synthesis conditions on the morphology can be assessed. Additionally, rather than use multiple nanoparticle features (size, shape, number density, etc.) in tandem, as is conventionally done, the 2PC function can single-handedly characterize the nanoparticle evolution. Furthermore, quantities such as the nanoparticle size, shape and spacing are shown to be related to the 2PC function. This validates that our 2PC method as superior approach compared to the existing particle size measuring techniques, for characterizing nanoparticle structures.