X-Ray Microanalysis – Rapid Discovery of Elemental Phases

X-ray microanalysis is a powerful tool with high precision and speed, often revealing unexpected chemical phases, which cannot be detected by element-based image analysis alone as there are limitations on distilling large amounts of spectral data into meaningful chemical composition. Researchers at Sandia National Laboratory, Albuquerque, New Mexico, combined elemental analysis with multivariate statistical analysis to enable true discovery.

On leveraging the spatial, spectral, numeric and elemental data, conclusions can be arrived at with elemental mapping. This paper will deal with conclusive inferences possible from samples ranging from simple to complex by conducting spectrum-focused analysis on a pixel-by-pixel basis.

The Thermo Scientific X-ray Microanalysis version of multivariate statistical analysis is called COMPASS, and is offered for the NORAN System 7 microanalysis system and its EDS and WDS detectors.

Applying the Conventional EDS Paradigm to Overcome Contemporary Challenges

Up until now, spectral imaging has been associated with tedious image analysis and capture, however today’s high resolution, high speed data capture at over 10,000 counts/s not only accelerates the data capture process but also offers high resolution elemental maps that result from advanced spectral imaging software. It is now possible for analysts to identify unexpected discrete elements. However these elements exist in phases rather than elemental states. Using traditional EDS, it is possible to determine base elements of discrete elemental phases. The challenges faced are:

  • Determining the exact phase with certainty
  • Ensuring that the final image highlights the region of interest where the unexpected element is located, which is almost impossible with point and shoot or basic element mapping alone.

COMPASS automatically identifies the phases from Spectral Imaging data (256 × 192 px, 200 seconds, 150 c/p, 4000 µs/pixel)

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

Benefits of COMPASS over Other Techniques

In this example, sectioning and polishing of a production turbine blade was performed for EDS analyses in the scanning electron microscope. A spectral imaging data set was collected at 5 kV, 256 × 198 pixels. The nominal composition of the alloy was Ni-20Cr-12Al-4Mo. Elemental maps of the known elements were observed. The right and left particle bands indicated differing amounts of the primary elements (Ni, Cr, Al) with the region between the particles having a third composition. The central band showed high content of Mo as seen in Figure 2.

Gross counts elemental map of Mo-L X-rays. Increased brightness indicates more characteristic 1 X-ray intensity

Figure 2. Gross counts elemental map of Mo-L X-rays. Increased brightness indicates more characteristic 1 X-ray intensity

Although the results are quite conclusive, sophisticated processing techniques including multivariate statistical analysis and deconvolution showed that another particle phase was present.

Analysis was performed using the COMPASS software showed another particle phase as in Figure 3. As seen in Figure 4 these have enrichment of Mo-L and Nb-L. In the initial analysis this Nb contribution was missed due to the heavy overlap of Nb-L lines with Mo-L lines, minimal composition of Nb with the alloy and minimal number of pixels in the Nb enriched phase.

Phase map of five phases found in the sample. The white phase is the Nb-enriched phase

Figure 3. Phase map of five phases found in the sample. The white phase is the Nb-enriched phase

COMPASS phase spectrum of the Nb-enriched phase found in the sample. Note that the Nb-L and Mo-L families of X-ray lines have a very large overlap of a number of peaks, but COMPASS was able to differentiate their contributions in the two sets of particles.

Figure 4. COMPASS phase spectrum of the Nb-enriched phase found in the sample. Note that the Nb-L and Mo-L families of X-ray lines have a very large overlap of a number of peaks, but COMPASS was able to differentiate their contributions in the two sets of particles.

As shown in Figure 5, once enrichment of Nb was determined, this was added to the elemental map list and quantitative map results were produced.

Nb-L weight percent quantitative map of the sample. The quantitative routines are able to extract the Nb-L contribution from the Mo-L contribution and provide the true distribution of the elements in particles.

Figure 5. Nb-L weight percent quantitative map of the sample. The quantitative routines are able to extract the Nb-L contribution from the Mo-L contribution and provide the true distribution of the elements in particles.

Careful measurement of these particles indicated that they are between 0.3 and 0.5 µm in width.

Salient Features of COMPASS

Some of the highlights of COMPASS are:

  • There is evidence within the initial data when spectra are examined on a pixel-by-pixel basis determining an element that the analyst never expected.
  • COMPASS facilitates discovery. While using COMPASS in spectrum-focused analysis, the analyst and researcher’s time is optimized, and streamlining of the data analysis process is done.
  • Instead of characterizing a complete interest-based area on certain locations, more definitive characterization is possible.

Spectral display showing the overlap of the cumulative spectrum from all pixels in a spectralimaging 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 6. Spectral display showing the overlap of the cumulative spectrum from all pixels in a spectralimaging 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

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

It is possible for analysts to arrive at more precise answers faster with less acquired data and eliminate black-box or user algorithm bias by working with mapped spectra and avoiding quantification and peak identification.

An advanced deconvolution scheme can be run using any modern analyzer in order to identify, separate and quantify overlapping peaks. Manual intervention is also permitted.

COMPASS accelerates interpretation with less data and time. Three examples are shown how mapped spectra can enhance accuracy and speed discovery.

  1. It is possible to ensure that minority elements are detected with high accuracy. When the identification, deconvolution or quantification process fails to identify a minority element present in a particular region of the sample, the analyst eliminates analysis of the element. However when individual pixel spectra are investigated, minority elements are easily found and reported to the analyst as shown in Figure 6. Phase separation using spectral analysis helps quantify the netire spectrum and this can help determine the phases. It is possible to find minute differences and report the same to the analyst as shown in Figure 7.

    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 7. 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.

  2. Analysis of complex geological samples is hastened and simplified. Figure 8 shows an FeS phase embedded within a complex geological sample. The spatially unique FeS phase is first separated and then the associated spectra is separated involving a simpler and therefore more accurate 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 8. 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.

  3. Non-value added analysis is eliminated and phase identification is hastened. In Figure 9, the analyst peak identification is altogether avoided and the sample is matched against a pre-defined spectral library to quickly identify hematite intermixed 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 9. 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 Data – The Discovery Engine

Until COMPASS was introduced, traditional element mapping was the only effective way for sample characterization with EDS. The sample shown in Figure 10 indicates sulfide compounds embedded in epoxy. The electron image contrast is not enough to identify the phases. When COMPASS was applied and a reference library for sulfides was referenced, phase identification from the spectral image without additional input was automatically determined.

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

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

Figure 10. The spectrum indicates significant Fe, Cu, S in C epoxy binder

256 × 192 pixel acquisition, 15 counts/pixel

Figure 11. 256 × 192 pixel acquisition, 15 counts/pixel

The spectrum indicates significant Ni, Co, Cr, Al

The spectrum indicates significant Ni, Co, Cr, Al

Figure 12. The spectrum indicates significant Ni, Co, Cr, Al

Defect analysis of a turbine starting with pixel by pixel spectral data at the beginning of analysis allowed the analyst to differentiate subtle details of the phases outside the majority region. The electron image shows that there are two unique phases within the material. Quantitative maps of the analyzed region show NiAl and CoCr enriched phases, a y-enriched phase is determined by the analyst as shown in Figure 13.

Going a step beyond Quant maps, COMPASS automatically identifies an unexpected phase in the sample

Going a step beyond Quant maps, COMPASS automatically identifies an unexpected phase in the sample

Figure 13. Going a step beyond Quant maps, COMPASS automatically identifies an unexpected phase in the sample

Finally analysts study a sample of the Huckitta meteorite to determine all the phases in the sample as shown in Figure 14 and 15.

SEM image at 45× magnification and associated spectrum (5.7 microns/pixel)

SEM image at 45× magnification and associated spectrum (5.7 microns/pixel)

Figure 14. SEM image at 45× magnification and associated spectrum (5.7 microns/pixel)

Quant maps indicate three primary elements – Mg, Fe and Si

Figure 15. Quant maps indicate three primary elements – Mg, Fe and Si

Although the quant element maps indicate three primary elements, COMPASS automatically identifies the distinct phases within the map that include haematite, Olivine, SiAlFeNi-O, and SiFeNi-O; all of which the experienced analyst would expect to see. However, along with the previously mentioned distinct phases, COMPASS identifies two additional phases defined by previously unknown trace phases – FeS-O and FeCr-O – and also identified the unique phase which they compose as shown in Figure 16.

COMPASS also automates the identification of phases that are unexpected and would otherwise go undetected

Figure 16. COMPASS also automates the identification of phases that are unexpected and would otherwise go undetected

The location and quantity of the majority elements can be automatically mapped using COMPASS but also finds unknown trace phases that quant mapping could not detect.

Conclusion

The modern EDS workflow begins by collecting the spectrum at each pixel in the image and is created in the same amount of time. The COMPASS application hastens and automates discovery by generating spectral phases from which it automatically maps and types discrete elemental phases. Analysts and researchers can make inferences with high level of confidence using these spectral results.

COMPASS enables rapid discovery of unexpected elemental phases and it does so from a single measurement of the area of interest.

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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