Effects of Radiolysis Products and Acidic Media on the Aggregation Behaviour of Nuclear Fuel Debris Nanoparticle Simulants via Stochastic Simulations

In this work, we investigated computationally the effects of water radiolysis products (e.g., H₂O₂) and acidic media (e.g., HNO₃) on the aggregation kinetics of metal oxide nanoparticles analogous to nanoscale particles generated by fuel debris retrieval (e.g., CeO₂ and ZrO₂ nanoparticles) in aqueous solution. Our computational approach is based on a dynamic version of the so-called Monte Carlo (MC) simulation approach in conjunction with the classical and extended versions of the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory for nanoparticle aggregation in suspensions and Brownian diffusion. In comparison with other simulation methods of particle aggregation (e.g., Molecular dynamics or Brownian dynamics), the DLVO-MC approach allows us to treat the simulation domains as statistical particle ensembles where each interaction event has a probability of occurrence quantified via transition rates (or frequency functions) calculated based on particle-particle interaction energetics. The time evolution of the aggregation diameter (AD) and its corresponding particle size distribution (PSD), which can be directly compared to dynamic light scattering experiments, are the main outcomes of our computational approach. We focused on nanoparticle aggregation kinetics under batch conditions but also continuous ones in microfluidic channels. Apart from the calculation of the AD and PSD for several case studies, a series of sensitivity analyses were carried out to illustrate the influence of various model parameters (e.g., pH, primary nanoparticle diameter, concentrations of water radiolysis products and acidic media, temperature) on aggregation kinetics. Our simulation results suggest that our computational approach has the potential to become an analysis tool for predicting the aggregation behaviour of nanoparticles generated from nuclear fuel retrieval under aqueous conditions