Colloidal structure, energy extensivity and Monte Carlo sampling properties of improved short-range interaction models for surfactant-coated magnetic nanoparticles

TL;DR

This paper compares short-range interaction models for magnetic colloids, showing that detailed models improve Monte Carlo simulations and reveal significant effects of particle-size dispersity on colloidal behavior.

Contribution

It introduces a more detailed short-range interaction model that enhances the accuracy of Monte Carlo simulations for magnetic colloids compared to standard models.

Findings

More detailed models align better with theoretical expectations.

Energy scaling shows slight deviation from extensivity.

Particle-size dispersity significantly affects colloidal interactions.

Abstract

The standard DLVO theory offers a limited description of ionic-surfacted magnetic colloids in near aggregation regimes. Correcting the electrical double layer term for ionic surfactants is not enough to successfully simulate the systems. The correction of the van der Waals energy divergence at short interparticle distances is fundamental for proper Monte Carlo sampling of nanoparticles' configurations. We compare different short-range interaction models and show that a more detailed model leads to Monte Carlo simulations that better match theoretical expectations. Studying the energy scaling with the number of particles, we observe a slight deviation from energy extensivity across all models, small but still detectable via Akaike's information criterion. The more detailed model predicts a strong effect of particle-size dispersity on the transition between overall attraction and repulsion. More precise modeling can significantly affect numerical predictions, in particular, the effect of particle-size dispersity on the spatial structure of colloids with high volume fraction. This emphasizes the importance of nailing down better models for describing complex colloidal dispersions.