Using X-Ray Microanalysis to Enable Rapid Discoveries

X-ray microanalysis is an analytical technique that provides immense possibilities to extend beyond high resolution mapping of elements. Speed and precision are the real powers of X-ray microanalysis, allowing it to uncover unexpected chemical phases, which is otherwise not possible using element-based image analysis. Although more data can be obtained, there is no definite means of obtaining meaningful chemical composition.

A new solution, developed by scientists at Sandia National Laboratory, Albuquerque, New Mexico, combines multivariate statistical analysis with elemental analysis to allow true discovery. When spatial, spectral, numeric and elemental data all effectively exploited, concrete conclusions can be drawn than using elemental mapping alone.

The Analyst's Challenge: Arriving at a Conclusive Answer

Point-and-shoot X-ray microanalysis data capture was adequate for validating the expected elements, but in view of complexity of present-day materials and applications, validating reasonable expectations can limit discovery, i.e., discovering the location and existence of unexpected compounds and elements.

In analysis applications and quality control, locating an unexpected element provides the opportunity cost to produce components that may ultimately fail in some way.

Applying the Conventional EDS Paradigm to Overcome Contemporary Challenges

Spectral mapping involves tedious and time-consuming image capture and analysis. Today’s high resolution, high speed data capture speeds the data capture process and provides higher resolution elemental maps ensuing from advanced spectral imaging software.

However, elements do not always exist in distinct elemental states, but rather exist in phases whose majority content hides the presence of a vital minority element, in spite of advancements made in mapping and spectral imaging technologies such as deconvolution of spectral curves.

Elements form compounds of atoms that include sulfates, oxides, alloys or chlorides. The base elements of such distinct elemental phases can be detected by means of standard EDS techniques. However, it is quite difficult to identify the accurate phase with absolute certainty.

So far, the solution has been to obtain more information from a larger area of interest and to improve the spectral images to minimize overlap. Although this solution helped in pinpointing the possibility for extra elements, it failed to give any actionable information.

Researchers at Thermo scientific called this phenomenon as the "verge of discovery." Until the integration of the COMPASS software (Figure 1) into the EDS system, the only choice was to collect additional information from a larger sample size and search for similarities.

COMPASS automatically identifies the phases from spectral imaging data (256 x 192 px, 200 seconds, 150 c/p, 4000µs/pixel)

Figure 1. COMPASS automatically identifies the phases from spectral imaging data (256 x 192 px, 200 seconds, 150 c/p, 4000µs/pixel)

Thanks to the COMPASS software, Thermo scientists have discovered that conclusive evidence does exists within the original data when spectra are studied on a pixel-by-pixel basis. Such discoveries bring researchers and analysts one step closer to a solution.

When COMPASS is used in spectrum-focused analysis, time can be optimized and the data analysis process can be made simple and easy. The main advantage is that instead of defining a whole area of interest based on a few point and shoot locations, the COMPASS software allows for more definitive characterization as it is based on all of the spectra from each pixel in the imaged area rather than only a few hand chosen spectra in point and shoot. This leads to more precise results without involving significant amount of time.

Pixel-Focused Analysis: The New Paradigm in EDS

Over the past three decades, elemental maps and filter-based correction of peak overlaps in quantitative maps have offered an excellent way to detect discrete or hidden elements. Quick discovery is about making the link between distinct elements to draw deeper conclusions about elemental mixtures with more confidence and also to carry out rapidly and repeatably.

Rapid Discovery: Gaining More Accurate Answers Faster with Less Acquired Data

Analysts can obtain fast and more precise answers with less acquired data and prevent user or "black-box" algorithm bias. This can be achieved by working with mapped spectra and eliminating peak detection and quantification until the very end.

In conventional EDS data processing, any errors in the quantification or identification process will be carried forward through all additional analysis and interpretation. In contemporary EDS data processing, COMPASS speeds up interpretation with less amount of time and data. The following examples demonstrate how mapped spectra can enhance the speed and precision.

When the identification, deconvolution, or quantification process does not effectively detect an important minority element present in a particular region of the sample, analysis of that element is unknowingly eliminated from the outset (Figure 2).

However, upon inspection of individual pixel spectra, the minority elements can be easily identified and reported to the analyst for interpretation. COMPASS can locate these slight variations and report them to the analyst (Figure 3).

Spectral display showing the overlap of the cumulative spectrum from all pixels in a spectral imaging data set (black) and the spectrum from very small particles of a Ca-P enriched phase (red). Note that the amplitude of the P peak at ~2 keV cannot be detected with conventional peak identification routines.

Figure 2. Spectral display showing the overlap of the cumulative spectrum from all pixels in a spectral imaging data set (black) and the spectrum from very small particles of a Ca-P enriched phase (red). Note that the amplitude of the P peak at ~2 keV cannot be detected with conventional peak identification routines.

Spectral display of the overlap of the cumulative spectrum and a number of Si-Al-X compounds found in the sample. Trying to distinguish the elemental overlap in the spectra and specific location in a map can be a difficult task even for experts unless statistical techniques are used.

Figure 3. Spectral display of the overlap of the cumulative spectrum and a number of Si-Al-X compounds found in the sample. Trying to distinguish the elemental overlap in the spectra and specific location in a map can be a difficult task even for experts unless statistical techniques are used.

Sophisticated deconvolution process may not be required or may be better carried out on a simpler set of spectra. Figure 4 illustrates an FeS phase integrated into a geological sample. isolating the unique FeS phase and then measuring the related spectra involves a simpler and more precise analysis.

Spectral display showing the overlap of the cumulative spectrum from all pixels in a spectral imaging data set (black) and the spectrum from small particles of a Fe-S phase (red). By extracting the spectrum of the Fe-S particles, a correct determination of the composition can be made.

Figure 4. Spectral display showing the overlap of the cumulative spectrum from all pixels in a spectral imaging data set (black) and the spectrum from small particles of a Fe-S phase (red). By extracting the spectrum of the Fe-S particles, a correct determination of the composition can be made.

In Figure 5, the analyst does not make peak identification at the same time but rather matches the sample against a pre-defined spectral library to detect hematite mixed within olivine based on spectral identification.

Compound maps and spectra for Olivine and Hematite found in the analysis area. The compounds were identified by matching each spectral shape to the best match in a database of compound shapes, not by traditional spectral peak identification methods. COMPASS performs spatial separation before peak identification and aids in spectral deconvolution of peaks.

Figure 5. Compound maps and spectra for Olivine and Hematite found in the analysis area. The compounds were identified by matching each spectral shape to the best match in a database of compound shapes, not by traditional spectral peak identification methods. COMPASS performs spatial separation before peak identification and aids in spectral deconvolution of peaks.

Pixel-by-Pixel Spectral Data: The Discovery Engine

Until the introduction of the COMPASS software, traditional element mapping was the only appropriate technique used for characterizing a sample with EDS. As a result, point-and-shoot data capture combined with elemental mapping became the desired method for the characterization of elements.

In addition, the time spent on data analysis can be reduced considerably by rastering a specific area to capture spectra at each pixel within the area of interest. This will also remove the need to perform additional measurements and give more conclusive results.

While rastering the entire field-of-view may take as long as a conventional multi-spectrum analysis, COMPASS automates the identification of phases to precisely characterize the sample in less amount of time than that of traditional EDS spectral analyses.

For instance, the following sample denotes sulfide compounds mixed in epoxy. The electron image contrast is not adequate to detect the phases (Figure 6).

The spectrum indicates significant Fe, Cu, S in C epoxy binder

Figure 6. The spectrum indicates significant Fe, Cu, S in C epoxy binder

By applying COMPASS and referencing a Match Library for sulfides, the software detects the phases from the spectral image sans any extra input and arrives at the same conclusion even when small amount of data is gathered (Figure 7).

256 x 192 pixel acquisition, 15 counts/pixel

Figure 7. 256 x 192 pixel acquisition, 15 counts/pixel

With the help of COMPASS, analysts can automatically map the quantity and location of the majority elements, similar to a traditional EDS. The software is also capable of identifying the majority phases and locates unknown trace phases, which could not be detected by quant mapping.

Conclusion

The traditional EDS workflow tends to limit discovery and fails to give a precise representation of obvious and not-so-obvious elements present in the sample. Traditionally, the EDS workflow starts with a single measurement of an area of interest rastered to produce X-ray spectra.

In contrast, the advanced EDS workflow starts with the collection of a spectrum at each pixel in the image and is generated in about the same period of time.

The use of the COMPASS software accelerates and automates the discovery of unexpected elemental phases by producing spectral phases from which it maps and types distinct elemental phases. By leveraging these spectral phases, analysts and researchers can infer deeper conclusions about elemental mixtures with greater reliability and confidence.

This information has been sourced, reviewed and adapted from materials provided by Thermo Fisher Scientific – Materials & Structural Analysis.

For more information on this source, please visit Thermo Fisher Scientific – Materials & Structural Analysis.

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