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用于研究填充颗粒多孔空隙空间的度量标准。

Metrics for studying the porous void space of packed particles.

作者信息

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

机构信息

Department of Biomedical Engineering, Duke University, Durham, North Carolina, 27708, USA.

equal contribution.

出版信息

bioRxiv. 2025 Jul 28:2025.07.23.666315. doi: 10.1101/2025.07.23.666315.

Abstract

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 and void volume fraction - 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 and , 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.

摘要

表征填充颗粒组件中的孔隙率是一项复杂的任务,需要先进的分析工具。我们展示了一个视觉丰富且广泛的库,包含用于分析此类组件中孔隙率特征的全局、基于孔隙和其他指标。我们的库包括使用我们的LOVAMAP软件识别的超过25个“3D孔隙”描述符。通过将我们的指标应用于一组因颗粒大小、形状和刚度而异的模拟填料,我们揭示了颗粒与空隙空间特征之间可预测的关系。我们确定了单分散颗粒系统的两个基本参数——颗粒直径和空隙体积分数——它们控制着几个空隙空间特征,例如瓶颈(即孔隙之间的通道)的总数、填料中所有孔隙内最大封闭球体的中值以及反应中心“热点”的比例。通过对颗粒直径和空隙体积分数的变换进行回归分析,我们量化了多个填料描述符之间的关系,例如证明了填料属性与组件中基于长度的描述符的中值呈线性比例关系。我们还介绍了计算3D孔隙的顶点、边和面数量的方法,以便近似为更简单的多面体。其他指标探索了进入颗粒支架的表面入口、穿过空隙空间的可通行路径以及基于尺寸的可达性。这些描述符已被整合到LOVAMAP中,共同为颗粒 - 孔隙结构和空间组织提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0633/12324226/8ba387619128/nihpp-2025.07.23.666315v1-f0001.jpg

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