Large-Area Phase Mapping Using PhAse Recognition and Characterization (PARC) Software

Scanning electron microscopy (SEM) combined with X-ray microanalysis is widely used for the characterization of solid materials. With the current generation of hardware—including field-emission electron guns, energy-dispersive X-ray analysis systems employing silicon drift detectors (SDD), and fast acquisition capabilities—chemical information from each pixel in an SEM image can be acquired within milliseconds. The greatest challenge is how to efficiently extract the maximum information from such a spectral imaging (SI) data set. Locked within the acquired SI data is spatially resolved chemical information that can be used to define phases, map their distribution and relative abundances, and determine the accurate chemistry of not only the individual phases but also the local bulk composition on the millimeter to centimeter scale. To unlock this potential at Tata Steel, we have developed the PhAse Recognition and Characterization (PARC) post-processing software for SI data [1–3]. The ability to determine the distributions and chemical compositions of individual phases is crucial for the characterization of materials. Phases, as opposed to elements, are the most directly relevant building blocks of many complex materials and are indicative of the conditions of a material’s formation and its chemical and mechanical properties. The spatial distribution of phases can be related to gradients in chemistry or physical conditions like temperature and oxygen activity (fO2). Often gradients in phase distribution become apparent only at a scale of centimeters (for example, in geological materials) requiring characterization of microstructure and microchemistry on this length scale. Moreover, phases can have internal chemical zonation. For tackling such materials characterization, the acquisition of SIs from large areas is particularly powerful, avoiding many of the pitfalls of manual point analysis and associated user bias.