Monte Carlo simulation for morphology of nanoparticles and particle size distributions: comparison of the cluster–cluster aggregation model with the sectional method

Abstract

This study presents the validity and ability of an aggregate mean free path cluster-cluster aggregation (AMP-CCA) model, which is a direct Monte Carlo simulation, to predict the aggregate morphology with diameters form about 15-200 nm by comparing the particle size distributions (PSDs) with the results of the previous stochastic approach. The PSDs calculated by the AMP-CCA model with the calculated aggregate as a coalesced spherical particle are in reasonable agreement with the results of the previous stochastic model regardless of the initial number concentration of particles. The shape analysis using two methods, perimeter fractal dimension and the shape categories, has demonstrated that the aggregate structures become complex with increasing the initial number concentration of particles. The AMP-CCA model provides a useful tool to calculate the aggregate morphology and PSD with reasonable accuracy.

Prediction of the aggregation rate of nanoparticles in porous media in the diffusion-controlled regime

“…Therefore, most models have used large timesteps to study the movement of particles in porous media and utilized probabilities for particle attachment and collision to predict the aggregation of NPs in porous media 1 , 17 , 25 , 27 . Many researchers have utilized the Smoluchowski model 20 – 25 and Monte Carlo methods 3 , 26 , 27 , 30 to predict the aggregation kinetics of nanoparticles in porous media.…”

GRAPE: A Stochastic Geometrical 3D Model for Aggregates of Particles With Tunable 2D Morphological Projected Properties

“…The modeling of aggregation phenomena provides more complete data regarding the geometrical and morphological characteristics of the aggregates, since the 3D geometry of the latter is known, with the exception of the population balance models, which does not allow for 3D visualization of the aggregates, thus preventing 2D projection images from being generated and measured. Numerous techniques exist outside of population balance models, such as molecular dynamics (Dong et al, 2017;Zheleznyakova, 2021), stochastic processes such as Monte-Carlo (Schmid et al, 2004;Kadota et al, 2011;Hussain et al, 2014;Ono et al, 2015;Morán et al, 2020;Shen et al, 2021), Langevin equations (Henry et al, 2013;Lazzari et al, 2016), and, in a more general way, all techniques based on the framework of Discrete Elements Methods (DEM) (Shyshko and Mechtcherine, 2013;Spettl et al, 2015;Deng and Davé, 2017;Zhang et al, 2018;Shi et al, 2020). Morán et al (2020) provide in their introduction a fairly complete picture of the models available for simulating nano-particles agglomerates, each of them allowing to model different characteristics of the aggregates, whether it is their shape or size.…”