Issue 36, 2021

ArGSLab: a tool for analyzing experimental or simulated particle networks

Abstract

Microscopy and particle-based simulations are both powerful techniques to study aggregated particulate matter such as colloidal gels. The data provided by these techniques often contains information on a wide array of length scales, but structural analysis methods typically focus on the local particle arrangement, even though the data also contains information about the particle network on the mesoscopic length scale. In this paper, we present a MATLAB software package for quantifying mesoscopic network structures in colloidal samples. ArGSLab (Arrested and Gelated Structures Laboratory) extracts a network backbone from the input data, which is in turn transformed into a set of nodes and links for graph theory-based analysis. The routines can process both image stacks from microscopy as well as explicit coordinate data, and thus allows quantitative comparison between simulations and experiments. ArGSLab furthermore enables the accurate analysis of microscopy data where, e.g., an extended point spread function prohibits the resolution of individual particles. We demonstrate the resulting output for example datasets from both microscopy and simulation of colloidal gels, in order to showcase the capability of ArGSLab to quantitatively analyze data from various sources. The freely available software package can be used either with a provided graphical user interface or directly as a MATLAB script.

Graphical abstract: ArGSLab: a tool for analyzing experimental or simulated particle networks

Supplementary files

Article information

Article type
Paper
Submitted
10 May 2021
Accepted
20 Aug 2021
First published
24 Aug 2021
This article is Open Access
Creative Commons BY license

Soft Matter, 2021,17, 8354-8362

ArGSLab: a tool for analyzing experimental or simulated particle networks

J. N. Immink, J. J. E. Maris, R. F. Capellmann, S. U. Egelhaaf, P. Schurtenberger and J. Stenhammar, Soft Matter, 2021, 17, 8354 DOI: 10.1039/D1SM00692D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements