the Conventional EDS Paradigm to Overcome Contemporary ChallengesBenefits of COMPASS over Other TechniquesSalient
Features of COMPASSRapid Discovery: Gaining More Accurate
Answers Faster with Less Acquired DataPixel-by-Pixel Data –
The Discovery EngineConclusionAbout
Thermo Scientific – Molecular Spectroscopy
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
Applying the Conventional EDS Paradigm to Overcome
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.
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.
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.
Figure 3. Phase map of five phases found in the sample.
The white phase is the Nb-enriched phase
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.
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
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.
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
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.
accelerates interpretation with less data and time. Three examples are shown how
mapped spectra can enhance accuracy and speed discovery.
- 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.
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
- 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.
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
- 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.
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
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
Figure 10. The spectrum indicates significant Fe, Cu, S
in C epoxy binder
Figure 11. 256 × 192 pixel acquisition, 15
Figure 12. The spectrum indicates significant Ni, Co, Cr,
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.
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.
Figure 14. SEM image at 45× magnification and associated
spectrum (5.7 microns/pixel)
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.
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.
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
enables rapid discovery of unexpected elemental phases and it does so
from a single measurement of the area of interest.
About Thermo Scientific – Molecular Spectroscopy
From NIR, FT-IR and Fluorescence, to Raman and UV-Vis, Thermo
Scientific deliver a full spectrum of answers with instruments and software
that remove all ambiguity from the analysis. Their innovations make this science
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This information has been sourced, reviewed and adapted from materials
provided by Thermo Scientific.
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