In 1956 Duncumb and Cosslett developed the first imaging method capable of showing the location of elements in a solid with a spatial resolution of a micrometer. This technique was dubbed “X-ray mapping” probably because a separate image was used to show the presence or absence of each element within the field of view. Since its first demonstration, X-ray mapping has become one of the most popular and useful methods of X-ray microanalysis. It has been widely applied in areas of biology, chemistry, physics, geology, environmental science, and materials science.

This review traces the development of X-ray mapping from its beginning 50 years ago through current analysis procedures that can reveal otherwise obscure elemental distributions and associations. X-ray mapping or compositional imaging of elemental distributions is one of the major capabilities of electron beam microanalysis because it frees the operator from the necessity of making decisions about which image features contain elements of interest. Elements in unexpected locations, or in unexpected association with other elements, may be found easily without operator bias as to where to locate the electron probe for data collection. X-ray mapping in the SEM or EPMA may be applied to bulk specimens at a spatial resolution of about 1 μm. X-ray mapping of thin specimens in the TEM or STEM may be accomplished at a spatial resolution ranging from 2 to 100 nm, depending on specimen thickness and the microscope. Although mapping has traditionally been considered a qualitative technique, recent developments demonstrate the quantitative capabilities of X-ray mapping techniques. Moreover, the long-desired ability to collect and store an entire spectrum at every pixel is now a reality, and methods for mining these data are rapidly being developed.

Electron-excited X-ray mapping is a key operational mode of the scanning electron microscope (SEM) equipped with energy dispersive X-ray spectrometry (EDS). The popularity of X-ray mapping persists despite the significant time penalty due to the relatively low output count rates, typically less than 25 kHz, that can be processed with the conventional EDS. The silicon drift detector (SDD) uses the same measurement physics, but modifications to the detector structure permit operation at a factor of 5–10 times higher than conventional EDS for the same resolution. Output count rates as high as 500 kHz can be achieved with 217 eV energy resolution (at MnKα). Such extraordinarily high count rates make possible X-ray mapping through the method of X-ray spectrum imaging, in which a complete spectrum is captured at each pixel of the scan. Useful compositional data can be captured in less than 200 s with a pixel density of 160 × 120. Applications to alloy and rock microstructures, ultrapure materials with rare inclusions, and aggregate particles with complex chemistry illustrate new approaches to characterization made practical by high-speed X-ray mapping with the SDD.Note: The Siegbahn notation for characteristic X-rays is commonly used in the field of electron beam X-ray spectrometry and will be used in this article. The equivalent IUPAC notation is indicated in parentheses at the first use.In this article, the following arbitrary definitions will be used when referring to concentration (C) ranges: major: C > 0.1 (10 wt%), minor: 0.01 ≤ C ≤ 0.1 (1–10 wt%), and trace: C < 0.01 (1 wt%).

A comprehensive three-dimensional (3D) microanalysis procedure using a combined scanning electron microscope (SEM)/focused ion beam (FIB) system equipped with an energy-dispersive X-ray spectrometer (EDS) has been developed. The FIB system was used first to prepare a site-specific region for X-ray microanalysis followed by the acquisition of an electron-beam generated X-ray spectral image. A small section of material was then removed by the FIB, followed by the acquisition of another X-ray spectral image. This serial sectioning procedure was repeated 10–12 times to sample a volume of material. The series of two-spatial-dimension spectral images were then concatenated into a single data set consisting of a series of volume elements or voxels each with an entire X-ray spectrum. This four-dimensional (three real space and one spectral dimension) spectral image was then comprehensively analyzed with Sandia's automated X-ray spectral image analysis software. This technique was applied to a simple Cu-Ag eutectic and a more complicated localized corrosion study where the powerful site-specific comprehensive analysis capability of tomographic spectral imaging (TSI) combined with multivariate statistical analysis is demonstrated.

A new Monte Carlo program, Win X-ray, is presented that predicts X-ray spectra measured with an energy dispersive spectrometer (EDS) attached to a scanning electron microscope (SEM) operating between 10 and 40 keV. All the underlying equations of the Monte Carlo simulation model are included. By simulating X-ray spectra, it is possible to establish the optimum conditions to perform a specific analysis as well as establish detection limits or explore possible peak overlaps. Examples of simulations are also presented to demonstrate the utility of this new program. Although this article concentrates on the simulation of spectra obtained from what are considered conventional thick samples routinely explored by conventional microanalysis techniques, its real power will be in future refinements to address the analysis of sample classifications that include rough surfaces, fine structures, thin films, and inclined surfaces because many of these can be best characterized by Monte Carlo methods. The first step, however, is to develop, refine, and validate a viable Monte Carlo program for simulating spectra from conventional samples.

We have investigated the changes produced in single-element and two-layer transmission electron microscope (TEM) specimens irradiated by an intense nanometer-sized electron probe, such as that produced in a field-emission or aberration-corrected TEM. These changes include hole formation and the accumulation of material within the irradiated area. The results are discussed in terms of mechanisms, including electron-beam sputtering and surface diffusion. Strategies for minimizing the effect of the beam are considered.

Image quality (IQ) maps constructed from electron backscatter diffraction data provide useful visualizations of microstructure. The contrast in these maps arises from a variety of sources, including phase, strain, topography, and grain boundaries. IQ maps constructed using various IQ metrics are compared to identify the most prominent contrast mechanism for each metric. The conventional IQ metric was found to provide the superior grain boundary and strain contrast, whereas an IQ metric based on the average overall intensity of the diffraction patterns was found to provide better topological and phase contrast.

The ability to map plastic deformation around high strain gradient microstructural features is central in studying phenomena such as fatigue and stress corrosion cracking. A method for the visualization of plastic deformation in electron back-scattered diffraction (EBSD) data has been developed and is described in this article. This technique is based on mapping the intragrain misorientation in polycrystalline metals. The algorithm maps the scalar misorientation between a local minimum misorientation reference pixel and every other pixel within an individual grain. A map around the corner of a Vickers indentation in 304 stainless steel was used as a test case. Several algorithms for EBSD mapping were then applied to the deformation distributions around air fatigue and stress corrosion cracks in 304 stainless steel. Using this technique, clear visualization of a deformation zone around high strain gradient microstructural features (crack tips, indentations, etc.) is possible with standard EBSD data.

Following is a list of microscopy-related meetings and courses. The editors would greatly appreciate input to this list via the electronic submission form found in the MSA World-Wide Web page at http://www.msa.microscopy.com. We will gladly add hypertext links to the notice on the web and insert a listing of the meeting in the next issue of the Journal. Send comments and questions to JoAn Hudson, joanh@clemson.edu or Nestor Zaluzec, zaluzec@aaem.amc.anl.gov.