On-the-fly coarse-graining methodology for the simulation of chain formation of superparamagnetic colloids in strong magnetic fields

TL;DR

This paper introduces an on-the-fly coarse-graining simulation method for modeling chain formation in superparamagnetic colloids under strong magnetic fields, significantly reducing computational costs while accurately reproducing detailed dynamics.

Contribution

The authors develop a novel on-the-fly coarse-grain model that dynamically redefines simulation objects, enabling efficient and accurate simulation of superparamagnetic colloid behavior under high magnetic fields.

Findings

Method accurately reproduces Langevin Dynamics results

Significantly reduces CPU time for simulations

Successfully predicts relaxation time dependence in MRI experiments

Abstract

The aim of this work is the description of the chain formation phenomena observed in colloidal suspensions of superparamagnetic nanoparticles under high magnetic fields. We propose a new methodology based on an on-the-fly Coarse-Grain (CG) model. Within this approach, the coarse grain objects of the simulation are not fixed a priori at the beginning of the simulation but rather redefined on the fly. The motion of the CG objects (single particles or aggregates) is described by an anisotropic diffusion model and the magnetic dipole-dipole interaction is replaced by an effective short range interaction between CG objects. The new methodology correctly reproduces previous results from detailed Langevin Dynamics simulations of dispersions of superparamagnetic colloids under strong fields whilst requiring an amount of CPU time orders of magnitude smaller. This substantial improvement in the computational requirements allows the simulation of problems in which the relevant phenomena extends to time scales inaccessible with previous simulation techniques. A relevant example is the waiting time dependence of the relaxation time T_2 of water protons observed in Magnetic Resonance experiments containing dispersions of superparamagnetic colloids, which is correctly predicted by our simulations. Future applications may include other popular real-world applications of superparamagnetic colloids such as the magnetophoretic separation processes.