• James, Simon
• Gureyev, Timur
• Wilkins, Steve
• Stevenson, Andrew
• Nesterets, Yakov

Abstract

Synchrotron-based hard X-ray imaging techniques open up exciting new opportunities for the quantitative characterization of materials down to the micron scale or below, and with elemental and compositional specificity. .Conventional X-ray microtomography (micro-CT) provides attenuation coefficient distributions in 3D that do not directly relate to compositional information. By a process of data segmentation pixel by pixel, distributions of materials components are often derived from such data. An alternative approach that we will describe and which has many advantages is termed data-constrained microstructure modeling (DCM). This methodology can overcome some limitations of the conventional approaches and also combine data from different analysis techniques in a self-consistent manner. In the DCM approach, each voxel is assumed to contain a mixture of multiple materials including voids. Compositions of voxels and their relations to neighboring voxels are subject to the data constraints resulting from X-ray micro-CT data sets recorded with different beam energies [1]. Such data may be augmented, in principle, by other data such as that from X-ray micro-fluorescence, neutron imaging, SAXS and other techniques.In the present talk, by way of illustration, we will present results obtained via the DCM approach on samples such as hydrocarbon reservoir rocks, coal, aerospace primers and metal corrosion products. In particular, we will show results where the DCM approach has been applied to predict the corrosion inhibitor and filler distribution in a polymer matrix paint primer. The DCM-predicted compositional microstructures have produced a level of agreement with an EDX image taken on the sample surface. Results for other systems will also be presented.

Topics

• impedance spectroscopy
• microstructure
• surface
• polymer
• corrosion
• Energy-dispersive X-ray spectroscopy
• void
• small angle x-ray scattering