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Identification and analysis of 3D pores in packed particulate materials

Publication ,  Journal Article
Riley, L; Cheng, P; Segura, T
Published in: Nature Computational Science
November 21, 2023

We took the classic ‘guess the number of beans in a jar game’ and amplified the research question. Rather than estimate the quantity of particles in the jar, we sought to characterize the spaces between them. Here we present an approach for delineating the pockets of empty space (three-dimensional pores) between packed particles, which are hotspots for activity in applications and natural phenomena that deal with particulate materials. We utilize techniques from graph theory to exploit information about particle configuration that allows us to locate important spatial landmarks within the void space. These landmarks are the basis for our pore segmentation, where we consider both interior pores as well as entrance and exit pores into and out of the structure. Our method is robust for particles of varying size, form, stiffness and configuration, which allows us to study and compare three-dimensional pores across a range of packed particle types. We report striking relationships between particles and pores that are described mathematically, and we offer a visual library of pore types. With a meaningful discretization of void space, we demonstrate that packed particles can be understood not by their solid space, but by their empty space.

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Published In

Nature Computational Science

DOI

EISSN

2662-8457

Publication Date

November 21, 2023

Volume

3

Issue

11

Start / End Page

975 / 992

Publisher

Springer Science and Business Media LLC
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Riley, L., Cheng, P., & Segura, T. (2023). Identification and analysis of 3D pores in packed particulate materials. Nature Computational Science, 3(11), 975–992. https://doi.org/10.1038/s43588-023-00551-x
Riley, Lindsay, Peter Cheng, and Tatiana Segura. “Identification and analysis of 3D pores in packed particulate materials.” Nature Computational Science 3, no. 11 (November 21, 2023): 975–92. https://doi.org/10.1038/s43588-023-00551-x.
Riley L, Cheng P, Segura T. Identification and analysis of 3D pores in packed particulate materials. Nature Computational Science. 2023 Nov 21;3(11):975–92.
Riley, Lindsay, et al. “Identification and analysis of 3D pores in packed particulate materials.” Nature Computational Science, vol. 3, no. 11, Springer Science and Business Media LLC, Nov. 2023, pp. 975–92. Manual, doi:10.1038/s43588-023-00551-x.
Riley L, Cheng P, Segura T. Identification and analysis of 3D pores in packed particulate materials. Nature Computational Science. Springer Science and Business Media LLC; 2023 Nov 21;3(11):975–992.

Published In

Nature Computational Science

DOI

EISSN

2662-8457

Publication Date

November 21, 2023

Volume

3

Issue

11

Start / End Page

975 / 992

Publisher

Springer Science and Business Media LLC