A reverse Monte Carlo algorithm to simulate two‐dimensional small‐angle scattering intensities. Issue 6 (29th November 2022)

• Record Type: Journal Article
• Authors: Barnsley, Lester C.; Nandakumaran, Nileena; Feoktystov, Artem; Dulle, Martin; Fruhner, Lisa; Feygenson, Mikhail
• Is Part Of: Journal of applied crystallography. Volume 55:Issue 6(2022)
• Page Start: 1592
• Page End: 1602
• Publication Date: 2022-11-29
• Subjects: small‐angle neutron scattering -- small‐angle X‐ray scattering -- magnetic nanoparticles -- superparamagnetic iron oxide nanoparticles -- reverse Monte Carlo simulations; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S1600576722009219
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : Small‐angle neutron scattering and small‐angle X‐ray scattering are important experimental techniques for studying the behaviour and properties of materials on the nanoscale. This article describes a numerical algorithm that uses reverse Monte Carlo simulations to model scattering intensities observed on a two‐dimensional small‐angle scattering detector. Abstract : Small‐angle scattering (SAS) experiments are a powerful method for studying self‐assembly phenomena in nanoscopic materials because of the sensitivity of the technique to structures formed by interactions on the nanoscale. Numerous out‐of‐the‐box options exist for analysing structures measured by SAS but many of these are underpinned by assumptions about the underlying interactions that are not always relevant for a given system. Here, a numerical algorithm based on reverse Monte Carlo simulations is described to model the intensity observed on a SAS detector as a function of the scattering vector. The model simulates a two‐dimensional detector image, accounting for magnetic scattering, instrument resolution, particle polydispersity and particle collisions, while making no further assumptions about the underlying particle interactions. By simulating a two‐dimensional image that can be potentially anisotropic, the algorithm is particularly useful for studying systems driven by anisotropic interactions. The final output of the algorithm is a relative particle distribution, allowing visualization of particle structures that form over long‐range length scales ( i.e. several hundred nanometres), along with an orientational distribution of magnetic moments. The effectiveness of the algorithm is shown by modelling a SAS experimental data set studying finite‐length chains consisting of magnetic nanoparticles, which assembled in the presence of a strong magnetic field due to dipole interactions.