Low-energy properties of two-dimensional magnetic nanostructures: interparticle interactions and disorder effects

TL;DR

This study investigates how structural disorder and particle coverage influence the low-energy magnetic properties of two-dimensional dipole-coupled nanoparticle ensembles, revealing effects on magnetic order, energy distributions, and angular preferences.

Contribution

It provides new insights into the impact of disorder, vacancies, and finite-size effects on the magnetic configurations and energy landscape of 2D magnetic nanoparticle arrays.

Findings

Small disorder lifts microvortex degeneracy and induces noncollinear order.

Energy distribution shifts from asymmetric to Gaussian with increasing disorder.

Dipole coupling energy decreases as disorder increases.

Abstract

The low-energy properties of two-dimensional ensembles of dipole-coupled magnetic nanoparticles are studied as function of structural disorder and particle coverage. Already small deviations from a square particle arrangement lift the degeneracies of the microvortex magnetic configuration, and result in a strongly noncollinear magnetic order of the particle ensemble. The energy distribution of metastable states is determined. For a low degree of disorder a strongly asymmetric shape with a pronounced peak of the ground state energy results. In contrast, for a strong disorder a Gaussian-like distribution is obtained. The average dipole coupling energy $\bar E_\mathrm{dip}$ decreases with increasing structural disorder. The role of vacancies has been studied for a square particle array by determining the angular distribution of the preferred microvortex angle as function of the vacancy concentration. Indications for a preferred angular direction along the axial as well as along the diagonal directions of the square array are revealed. A corresponding investigation for disturbed square arrays results in a different angular distribution. The effect of dipole-quadrupole corrections resulting from the finite size of the particles is quantified.