X-ray mapping provides a clear visualization of the elemental distributions inside of a sampling area. Smart and advanced mapping tools have been built into APEX™ 2.0 to provide a more detailed view of the chemical nature of a material and to promote ease-of-use.
In this article, a granite sample has been selected to show how a range of mapping features in APEX 2.0 can be used to capture accurate and high-quality data as efficiently and quickly as possible. A variable pressure microscope combined with an EDAX Octane Elite Silicon Drift Detector was used to collect the data.
Dynamic Element Mapping
Prior to collection, minimal mapping setup parameters are required. The duration is calculated according to the average counts per pixel and the amp time is automatically chosen according to the count rate after the user has selected the map quality.
Mapping parameters can also be determined manually as an alternative method for the user.
Elements can be added or removed in live mapping mode to isolate the elements of interest when users select the Dynamic Element Mapping feature during mapping data collection. User-determined regions of interest, element lines, and elements can be edited with ease in the process of live collection (Figure 1).
This tool removes the display of element maps that are not required and provides more representative quantitative results when collecting with the proper element list.
Figure 1. Elements, element lines, and user-selected regions of interest can be easily added or removed in live mapping mode with Dynamic Element Mapping. Image Credit: EDAX
Montage Large Area Mapping
Granites are igneous rocks that are coarsely crystalline. This sample includes a number of phenocrysts that are normally greater than 2 mm in size. Montage Large Area Mapping enables users to map a complete phenocryst in the sample at a resolution determined by the user.
For sample overview, a low resolution can be used, while the full detail of the sample can be captured in high resolution. This is achieved through the application of stage movements to acquire distinct maps in a grid pattern across the phenocryst, where they are stitched into a montage.
The montage maps provide an accurate visualization of elemental distribution in a sampling area that is comparatively large, and high-quality spectra can be collected from small features. The user is guided through the complete process by a simple to use setup wizard with step-by-step instructions.
CompoMaps - Live Net Mapping
The montage map of P shows a significant quantity of phosphorus-rich grains located within the phenocryst (Figure 2b). These grains are calcium phosphate, and they can be found alongside zirconium silicate in igneous rocks.
P K and Zr L lines are significantly overlapped with just 29 eV of energy difference, so the region of interest (ROI) maps of these two elements almost match if both these minerals coexist.
The live net mapping tool called CompoMaps can carry out peak deconvolution and background subtraction for the separation of P K and Zr L during mapping for precise representation.
ROI and NET display can be switched by the user at any time in the live acquisition. A rock sample of gabbroic nature with both zirconium silicate and calcium phosphate was mapped to demonstrate this feature (Figure 3).
Figure 2. a) Si K and b) P K montage maps of an entire heart-shaped phenocryst in the granite sample. Image Credit: EDAX
Figure 3. Live ROI maps of a) P K (green) and b) Zr L (red). The two maps are almost identical due to heavily overlapped ROIs. Live NET maps of c) P K and d) Zr L. The two elements are separated out by live background subtraction and peak deconvolution. e) An overlay of P K and Zr L live NET maps show the two elements exist in separate phases. Image Credit: EDAX
Map Rebuild and EDS Quant Maps
Elements can always be added back through the map rebuild if any continue to be missing during live mapping. Aside from rebuilding maps as NET or ROI, quant maps can be produced to analyze differences in concentration between various minerals in the granite sample.
A complete quantification schedule operates on each pixel, and the maps are presented in At% or Wt%. The user can easily predict the concentration according to color shades with the scales fixed to the quant maps. The Si concentration change from zero, intermediate to high (Figure 4), can be used to determine mineral types.
Figure 4. Wt% map of Si K from the center of the phenocryst shown in Figure 1. The brightest areas in the image can be identified as quartz (silicon dioxide) due to the Wt% indicated by the color scale. Si Wt% in stoichiometric quartz is 46.7%. Image Credit: EDAX
Max Pixel Spectrum
The sum spectrum is helpful to determine the main elements in the sample. However, minor elements can be hidden in inclusions that only comprise a small contribution in relation to the primary matrix.
A maximum pixel spectrum can be produced to determine these features by locating the pixel with the highest quantity of counts for each point on the energy axis and employing that as the intensity for the artificial, processed spectrum.
Figure 5a demonstrates that the Mn Kα peak cannot be seen in sum spectrum of the montage map but is visible in the maximum pixel spectrum. Mn can be incorporated in the rebuilt montage elemental maps using this information (Figure 5b).
Spectra acquired from these tiny inclusions shows that they are iron-titanium oxides with roughly 5 Wt% of Mn. As Ti is spatially widely distributed in the phenocryst and Fe is abundant, these small oxide minerals are not easily identified in the montage maps for Fe and Ti.
As every point of data is established on a single pixel, the maximum pixel spectrum is noisy, but it does demonstrate elements below the signal-to-noise ratio in the sum spectrum.
Figure 5. a) A maximum pixel spectrum (cyan) of the montage map shows a tiny Mn Kα peak that is not visible in the sum spectrum (red outline). b) A rebuilt montage map of Mn K indicates small inclusions with minor Mn inside the phenocryst. c) A montage map of Ti K. The iron-titanium oxides with minor Mn are not obvious in this map. Image Credit: EDAX
Smart Phase Mapping
A broad range of color shades is displayed in the montage map of Si K, demonstrating the presence of various kinds of silicates and other non-silicate minerals within the phenocryst (Figure 2a).
The generic phase maps can be efficiently produced along with live acquisition for quick characterization of minerals utilizing the Smart Phase Mapping feature in APEX 2.0.
The phases can be combined or renamed during post-processing or collection if required (Figure 6). Phase separation tolerance can be controlled by the user.
During live acquisition, the phase can be further identified employing either a pre-defined phase library or the quantification of the extracted phase spectrum. Spectrum Match is also an option for the user to employ during post-processing.
From beginning to end, minimal interaction from the operator is required. These phase analysis techniques are also effective in further applications, for example, basic inclusion analysis in metals and the majority of other phase-related applications.
Figure 6. Phase map and elemental maps of the blue phase from a sampling area inside the phenocryst. The blue phase has been renamed quartz because it is composed of Si and O and confirmed by extracted spectra. Image Credit: EDAX
CPS Mapping and Normalization
Geological processes may create cracks in this sample and in other geological samples. Additionally, surface topography may be generated because of the varying polishing resistance of minerals. These conditions frequently influence the quality of X-ray maps which can produce changes in the count rate.
It has previously been left to the operator to interpret and decipher the influence of topography on the analysis of the sample. The Counts Per Second (CPS) map feature in APEX 2.0 can now take the lead in these interpretations, which are often challenging to understand.
The CPS map delivers a visual display of the X-ray count rate at each pixel in the dataset. The highest count areas are represented by the brightest pixels, and black or dark regions show little to no X-ray counts. It is a simple and efficient method to verify the differences in count rate throughout the sample region.
As an example, the black areas in Figure 7a demonstrate an absence of counts caused by topography. A CPS normalization (Figures 7b and 7c) can be used to compensate these regions, which is a further advantage of the CPS smart feature.
Figure 7. a) A CPS map of the center of the phenocryst. The black triangle region in the center is a crack on the surface. b) The original ROI map of O K. c) A ROI map of O K after CPS normalization. The variations in count rate due to topography are compensated. Image Credit: EDAX
To conclude, this type of analysis can be applied to a broad range of real-world applications and materials. The flexibility, ease-of-use, and customization of the mapping tools in APEX 2.0 provide users with a range of effective techniques to showcase their data and understand their samples better.
This information has been sourced, reviewed and adapted from materials provided by EDAX.
For more information on this source, please visit EDAX.