(440c) Quantitative Characterization of Particulate Materials from Microtomography Imaging

Traditional particle-characterization techniques require particles to be dispersed into solution. This constraint is a disadvantage in systems where agglomeration can occur and it presents a problem in cases where the packing structure of the particles is of interest (e.g., computational modeling of the mechanical behavior of granular materials). Microtomography techniques offer an alternative approach because they allow packings of particulate materials can be imaged directly, and subsequent numerical analysis of the data can be used to interpret particle parameters.

We present a numerical algorithm for quantitative characterization of binary microtomgraphy images. It allows for rapid particle-by-particle computer reconstruction of the particulate material. Once the reconstruction process is complete, the data can be used to generate a wealth of particle-scale statistics. Results include parameters of general interest such as particle size distributions, surface areas, and aspect ratios. In addition, information relevant to the packing structure is obtained: particle contact areas, coordination numbers, orientations, radial distribution functions, and more.

In addition to the algorithm, we present data from a particulate material undergoing vibrational densification. The imaging and subsequent analysis is used to reveal the evolution of packing structure that accompanies the densification process. The resulting data can be used to help interpret changes in bulk parameters associated with heat transfer and mechanical behavior.