Preparation strategy for statistically significant micrometer-sized particle systems suitable for correlative 3D imaging workflows on the example of X-ray microtomography

The characterization of multidimensional particle property distributions through computed tomography requires an adapted sample preparation strategy. This strategy should both generate as many spatially separated particles as possible in the smallest achievable volumes and also enable mechanically and vacuum-stable samples that are suitable for correlative measurement, for example with high-energy ion beam methods. In the present study an epoxy-based method is presented that minimizes the negative influence of particle sedimentation by adding very low X-ray absorbing graphite nanoparticles as spacer. A machine learning-based method is presented to discretize the particle system. Results are compared with data from 2D SEM validation measurements and data of a previous study. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Characterization of multidimensional particle property distributions through computed tomography requires an adapted sample preparation strategy to generate spatially separated particles and ensure sample stability. In this study, an epoxy-based method with low X-ray absorbing graphite nanoparticles as spacer is presented, along with a machine learning-based method for discretizing the particle system. Results are compared with data from previous studies and validation measurements.