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Identification and analysis of 3D pores in packed particulate materials

Nature Computational Science volume 3, pages 975–992 (2023)Cite this article

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Abstract

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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Fig. 1: LOVAMAP software locates 3D pores of packed particles by accurately identifying important spatial landmarks of the void space.
Fig. 2: LOVAMAP workflow.
Fig. 3: 3D pores of packed particles with varying particle size, shape, stiffness, composition, polydispersity and container shape.
Fig. 4: 3D pores are compared against 2D approximations and used to understand void space directionality.
Fig. 5: 3D pores organized by the number of particles surrounding them.
Fig. 6: Investigating the composition of 3D pores within different packed particle domains.

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Data availability

Source datasets for Figs. 3–6 are available with this paper. Raw datasets are available in a Code Ocean compute capsule (https://doi.org/10.24433/CO.4876664.v1)81 and at https://seguralab.duke.edu/what/highlights/lovamap/.

Code availability

LOVAMAP segmentation and descriptor code are available through a Code Ocean compute capsule (https://doi.org/10.24433/CO.4876664.v1)81. The copyrights of the LOVAMAP code are owned by Duke University. The code was developed by L.R., P.C. and T.S. at the Segura Lab, Duke University Pratt School of Engineering. To request a license for the code, please contact the Digital Innovations department at the Duke Office for Translation and Commercialization (OTC) (https://olv.duke.edu/software/) at otcquestions@duke.edu with reference to ‘OTC File No. 7784’. CC BY-NC-SA licenses for the code are free for academics. To learn more about LOVAMAP, visit https://seguralab.duke.edu/what/highlights/lovamap/.

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Acknowledgements

We extend a special thanks to M. Roozbahani, who was a senior graduate student under D. Frost at Georgia Tech, when he kindly shared his experience and expertise during the early stages of the project. We thank the National Institutes of Health, the National Institutes of Neurological Disorders and Stroke (1R01NS112940 (T.S.), 1R01NS079691 (T.S.), R01NS094599 (T.S.)) and the National Institute of Allergy and Infectious Disease (1R01AI152568 (T.S.)).

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Authors and Affiliations

  1. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Lindsay Riley & Tatiana Segura

  2. Ninjabyte Computing, Sunnyvale, CA, USA

    Peter Cheng

  3. Department of Medicine, Neurology, Dermatology, Duke University, Durham, NC, USA

    Tatiana Segura

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Contributions

L.R. headed the development of the work by designing the computational framework, writing and implementing code, generating data, analyzing data, writing the paper and creating figures. P.C. provided computational expertise through guidance and discussion, contributed to the design of the computational framework, wrote code, played a key role in improving software runtime speeds, designed and simulated input domains, contributed to writing methods, and organized the flow of the final paper. T.S. was pivotal in the conception and direction of the project, including applications to real data, provided guidance and discussion throughout, and made substantial contribution to data analysis, figure preparation, and paper organization and editing.

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Correspondence to Tatiana Segura.

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Competing interests

P.C. is the founder of Ninjabyte Computing, a company that offers computational consulting and simulation expertise. L.R., P.C. and T.S. have filed for patent protection pertaining to LOVAMAP as an analytical tool to facilitate material optimization (reference number DU7784PCT).

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Nature Computational Science thanks Florian Bouvill, T. Matthew Evans and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Jie Pan, in collaboration with the Nature Computational Science team.

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Riley, L., Cheng, P. & Segura, T. Identification and analysis of 3D pores in packed particulate materials. Nat Comput Sci 3, 975–992 (2023). https://doi.org/10.1038/s43588-023-00551-x

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