ArGSLab

ArGSLab analyzes particle networks within aggregated particulate matter, such as colloidal gels, to quantify mesoscopic network structure from microscopy image stacks and simulation coordinate data.

Key Features:

• Multiscale Data Analysis: Focuses on mesoscopic length scales to extract structural information from both local and extended particle arrangements.
• Network Backbone Extraction: Identifies and extracts a network backbone and represents essential connectivity for downstream analysis.
• Versatile Data Processing: Processes microscopy image stacks and explicit coordinate data from particle-based simulations for quantitative comparison.
• Graph Theory Integration: Transforms particle arrangements into graph structures (nodes and links) to enable analysis of connectivity, clustering, and path lengths.
• Handling Complex Microscopy Data: Interprets datasets affected by an extended point spread function or obscured particles to produce reliable network representations.

Scientific Applications:

• Colloidal gel characterization: Quantifies mesoscopic network structure and topology in colloidal gels.
• Soft matter and materials science: Enables comparison of experimental microscopy data and particle-based simulations to study aggregated particulate systems.
• Network topology analysis: Supports investigation of connectivity, clustering, and path-length distributions in particle networks.

Methodology:

Accepts microscopy image stacks or simulation coordinate data; extracts a network backbone; transforms the backbone into a graph of nodes and links; and performs quantitative analyses on the resulting graphs.