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DOI 10.1101/2025.07.23.666315

Медицинская библиотека · bioRxiv : the preprint server for biology · 2025

Metrics for studying the porous void space of packed particles.

Riley Lindsay, Lee Emma, Cheng Peter, Alvarez Daniel Adrianzen, Segura Tatiana

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Аннотация

Characterizing porosity in packed particle assemblies is a complex task that requires advanced analytical tools. We present a visually rich and extensive library of global, pore-based, and other metrics for analyzing features of porosity in such assemblies. Our library includes over 25 descriptors of "3D pores" that are identified using our LOVAMAP software. By applying our metrics to a set of simulated packings that vary by particle size, shape, and stiffness, we reveal predictable relationships between particle and void space characteristics. We identify two fundamental parameters of a monodisperse particle system - particle diameter ( δ ) and void volume fraction ( ϕ ) - that govern several void space features, such as the total number of bottlenecks (i.e., doors between pores), the median value of the largest enclosed sphere across all pores in a packing, and the fraction of reaction-center "hotspots." Through regression analyses on transformations of δ and ϕ , we quantify multiple packing-descriptor relationships, demonstrating, for example, that packing properties scale linearly with the median values of length-based descriptors across assemblies. We further introduce approaches for computing the number of vertices, edges, and faces of 3D pores, allowing for approximation to simpler polyhedra. Additional metrics explore surface entrances into the particle scaffold, traversable paths through the void space, and size-based accessibility. Together, these descriptors, which have been bundled into LOVAMAP, offer new insights into particle-pore architecture and spatial organization.

Для цитирования: Riley Lindsay, Lee Emma, Cheng Peter, Alvarez Daniel Adrianzen, Segura Tatiana. Metrics for studying the porous void space of packed particles.. bioRxiv : the preprint server for biology. 2025. DOI: 10.1101/2025.07.23.666315.
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