Dr. Shangshang Mu, Applications Engineer, Gatan/EDAX
The new APEX™ 3.0 is the ultimate materials characterization software, integrating Energy Dispersive Spectroscopy (EDS), Electron Backscatter Diffraction (EBSD), and Wavelength Dispersive Spectrometry (WDS) to deliver previously unattainable solutions. This optimized configuration offers the uncompromised performance of each technique and allows users to combine them for the ultimate materials insight. All three techniques seamlessly operate within the APEX, blending powerful elemental and crystallographic analysis routines through an intuitive interface to deliver outstanding data collection, faster analysis, and flexible reporting for users of all levels.
What does APEX WDS look like?
WDS functionalities are implemented seamlessly with the EDS graphical user interface. The user can quickly adapt to the new functionalities and employ WDS when and where EDS reaches the limit. With one-click from start to finish, Auto WDS allows fully automated WDS scan list generation, optimum sample height determination, and spectrum collection. It simultaneously collects EDS and WDS spectra and displays them side-by-side or overlaid for easy data visualization and interpretation (Figure 1), with no overlapping or overloading of windows.
Figure 1. Simultaneous EDS-WDS spectrum acquisition user interface.
APEX allows you to set an intermediate position for the EDS detector to ensure optimal count rates for both techniques.
Figure 2. Simultaneous EDS-WDS mapping user interface.
Sets of combined EDS-WDS spectrum, linescan, and mapping data at different stage positions can be done via automated batch collection routines (Figure 2) to streamline SEM experiments. EDS and WDS data collection settings are managed in one user-friendly batch scan list (Figure 3).
Figure 3. Combined EDS-WDS batch list.
The quantitative elemental analysis supports individual technique or combined EDS-WDS standards. You can easily switch between EDS and WDS standards for each element by clicking on the icon in front of the element (Figure 4).
Figure 4. Quantitative results with combined EDS-WDS standards.
With the addition of WDS capabilities, APEX 3.0 now includes EDS, EBSD, and WDS. Each characterization tool can operate independently to utilize EDAX’s technological advancements or integrates data to provide solutions that were once unachievable.
Although red, green, and blue are placed at a high symmetry axis, the remaining colors are not uniformly distributed
Saturated rainbow palettes are not perceptually uniform, so the same orientation gradient will have different apparent intensities when centered around different orientations
Groups with two or four high symmetry directions do not have a natural mapping to three principal colors
Choosing red and green as principal colors result in poor contrast for individuals with red-green color vision deficiency (CVD)
OIM Analysis™ v9 implements four new Inverse Pole Figure (IPF) color palettes to address these issues, as shown in Figure 1. For fundamental sectors with three principal directions, CVD colors replace green with yellow for the second principal color. For fundamental sectors with four principal directions, red, yellow, green, and blue are used for traditional colors, and red, yellow, cyan, and blue are used for CVD colors. Notice that the new legends distribute colors smoothly while the old ones have large patches of red, green, and blue extending from the corners and sharp bands of yellow, cyan, and magenta.
Figure 1. The m3m (top) and m3 (bottom) IPF legend is shown from left to right for OIM Analysis v8 colors, new saturated colors, perceptually uniform colors, CVD saturated colors, and perceptually uniform CVD colors.
Figure 2. A nickel dataset is IPF colored with saturated (left) and perceptually uniform (right) color maps using traditional (top) and CVD (middle) colors. Notice that some significant orientation gradients in the KAM map (bottom left) are visible with perceptually uniform colors but may be invisible if the orientation falls in a low contrast region of the saturated color map. OIM Analysis v8 coloring is shown in the bottom right.
Figure 3. A partially recrystallized steel dataset is IPF colored with saturated (left) and perceptually uniform (right) color maps using traditional (top) and CVD (middle) colors. Notice that orientation gradients are over-emphasized in darker regions of the saturated color maps (blue and purple) and under-emphasized in brighter regions (green, yellow, and cyan).
Perceptually uniform color maps are designed so that a constant size step in the data being colored results in an apparent color change of constant magnitude regardless of the starting value. The uniformity of a color map can be visualized by imposing a ripple onto a ramp, as shown in Figure 3 and described by Kovesi . The ripple disappears in brighter regions of traditional saturated color maps but has a uniform relative intensity in perceptually uniform maps, as shown in Figure 4. The new perceptually uniform IPF colors in OIM Analysis v9 extend perceptually uniform cyclic color maps to a hemisphere by adding a white center point.
Figure 4. A perceptually uniform ramp is modified by a sine wave to create a test signal (green). The test signal is colored with a perceptually uniform black to white color map with maximum sine wave amplitude at the top of the image and minimum amplitude at the bottom. Note that the relative intensity of the ripple is the same at every gray level near the top edge and the ramp appears extremely smooth near the bottom edge. Figure adapted from Kovesi .
Figure 5. Traditional saturated color maps (top) are shown for heat (left) and rainbow (right) colors. Notice that the ripples are nearly invisible near red on both maps, yellow on the heat map, and green on the rainbow map. Perceptually uniform equivalents (bottom) sacrifice some color saturation/vividness to achieve a uniform sensitivity response across the entire map. Legends from Kovesi .
Deuteranomaly (red-green CVD) is the most common form of CVD and is simulated in Figure 6 to illustrate how much ambiguity is introduced in traditional colors. CVD impacts roughly 1 in 12 men and 1 in 200 women, so CVD colors should be preferred for papers and presentations.
Figure 6. Deuteranomaly is simulated with increasing severity from left to right (normal, 30%, 70%, 100%/Deuteranopia) for the traditional (top) and CVD (bottom) saturated palettes. Notice that in the far-right column, the traditional map has different directions with the same color, while the CVD map is significantly less ambiguous.
Enhanced IPF saturated color palettes maintain a similar look and feel while more uniformly distributing the available gamut. Perceptually uniform IPF color palettes sacrifice the full use of the RGB gamut to render crystal directions with increased precision, and CVD colors avoid red-green ambiguity. Together these new palettes enable visualization and accurate interpretation of orientation data for the widest range of audiences.
Kovesi, P. (2015). Good colour maps: How to design them. arXiv preprint arXiv:1509.03700.
Nolze, G., & Hielscher, R. (2016). Orientations–perfectly colored. Journal of Applied Crystallography, 49(5), 1786-1802.
Dictionary indexing compares experimental electron backscatter diffraction (EBSD) patterns against a dictionary of simulated patterns for each orientation on a uniform grid in orientation space [1,2]. Synthetic patterns are generated by rotating the Kikuchi sphere by the crystal orientation and projecting onto a plane using the experimental geometry. Comparison against a physics-based forward model gives excellent precision and noise tolerance at the cost of significant computational overhead. Spherical harmonic-based indexing uses the same Kikuchi sphere or ‘master pattern,’ but back projects experimental patterns onto the sphere instead. The orientation is indexed using the maximum spherical cross-correlation between the back-projected pattern and the Kikuchi sphere [3,4]. Mathematically, dictionary and spherical indexing are extremely similar, but the spherical approach is more numerically efficient since it can leverage fast Fourier transforms for the computations. In practice, spherical indexing provides similar precision  and noise tolerance to dictionary indexing but at much faster speeds.
A GPU implementation of spherical harmonic-based EBSD indexing implemented in OIM Analysis™ as part of the OIM Matrix module provides excellent indexing quality at hundreds or thousands of patterns per second. Here, we applied it to a range of scans to demonstrate the indexing quality and user parameters.
Spherical harmonic indexing has two parameters: bandwidth and grid size. Bandwidth is how far in frequency space to compute harmonics (analogous to a low pass filter on the EBSD pattern). Grid size is the correlation resolution with an Euler angle cube of (grid size)3 used for correlation (i.e., 0 – 360 for phi1, Phi, and phi2). In general, computation time scales with the number of Euler angle grid points, and a reasonable bandwidth is one less than half the grid size. For example, the following are some reasonable pairs of values:
Once the best Euler grid point (maximum cross-correlation) is selected, subpixel resolution can be achieved through a refinement step.
This dataset is a scan of the same region at different camera gains to intentionally produce corresponding sets of low and high-quality patterns.
Figure 1. Shows a) the result of indexing high-quality patterns, b) spherical harmonic indexing at a bandwidth of 63 and Euler grid of 1283 without refinement, and c) at a bandwidth of 63 with refinement.
Figure 1 shows a) the result of indexing high-quality patterns, b) spherical harmonic indexing at a bandwidth of 63 and Euler grid of 1283 without refinement, and c) at a bandwidth of 63 with refinement. Note that since grid point spacing is ~2.8° (360° / 128), the unrefined result has a stepped appearance due to the discrete orientation possibilities. After refinement, any orientation is possible, providing smooth results.
Figure 2. KAM maps are shown for the same region at a) 0°, b) 1°, and c) 2°.
In Figure 2, KAM maps are shown for the same region at a) 0°, b) 1°, and c) 2°. Notice that without refinement, there is no misorientation within a patch and a sharp spike between them. Even though both the Hough and refined spherical appear smooth, the slight orientation noise in the Hough indexing is visible using KAM.
Figure 3. With low-quality patterns, Hough indexing a) starts to fail, but b) spherical indexing still provides robust solutions and c) accurately captures continuous orientation gradients after refinement.
With low-quality patterns, Hough indexing a) starts to fail, but b) spherical indexing still provides robust solutions and c) accurately captures continuous orientation gradients after refinement (Figure 3).
Figure 4. a) bandwidths of 63, b) 95, and c) 127 are compared before (a – c) and after (d – f) refinement.
For very low-quality patterns, higher bandwidths may be required for better indexing results. In Figure 4, a) bandwidths of 63, b) 95, and c) 127 are compared before (a – c) and after (d – f) refinement. Note that the discrete steps in orientations before refinement become smaller with increased Euler angle grid resolution, but they refine to similar orientations. For all three bandwidths, the grid size is 2 * (bandwidth + 1).
Figure 5. 4. a) Raw pattern and b) NPAR pattern using Hough indexing and c) raw pattern and d) NPAR pattern using spherical indexing with a bandwidth of 127.
With spherical indexing integrated into OIM Analysis, existing image processing algorithms can be used for especially difficult patterns. At extremely high noise levels, Hough indexing cannot index any points, and the spherical indexing begins to fail for some points. NPAR trades spatial resolution for pattern quality by averaging each pattern with its neighbors. The improved patterns can be indexed reliably by both methods but Hough indexing struggles with the resulting overlap patterns near grain boundaries (Figure 5).
Hot Rolled Mg
Figure 6. Hough indexing struggles to index when pattern quality is reduced by a) high deformation, but b) spherical indexing is robust against significantly degraded pattern quality. Note that the d) spherical indexing confidence index strongly correlates with c) image quality but is high even in some regions with extremely low IQ.
Hough indexing struggles to index when pattern quality is reduced by a) high deformation, but b) spherical indexing is robust against significantly degraded pattern quality. Note that the d) spherical indexing confidence index strongly correlates with c) image quality but is high even in some regions with extremely low IQ (Figure 6).
Figure 7. Excellent results are possible even with a single pattern center used for the entire dataset. Vignetting is visible in a) an IPF+IQ map of Hough indexing with a fixed pattern center. The field is flat over the entire area for b) an IPF+CI map of spherical indexing with a fixed pattern center.
Spherical indexing can use a unique pattern center for each point at no extra cost for large fields of view. Excellent results are possible even with a single pattern center used for the entire dataset, as shown in Figure 7.
Deformed Duplex Steel
Figure 8. Phase discrimination depends on the similarity of the phases with a two-phase steel. In addition to the quality in orientation results with d – f) spherical indexing vs. a – c) Hough indexing, b – c & e – f) phase discrimination is improved with spherical BCC and FCC iron well separable.
Spherical indexing can be applied to multiple phases in the same way as any other indexing technique. Phase discrimination depends on the similarity of the phases with a two-phase steel shown in Figure 8. In addition to the quality in orientation results with d – f) spherical indexing vs. a – c) Hough indexing, b – c & e – f) phase discrimination is improved with spherical BCC and FCC iron well separable. Real space refinement may be required for particularly difficult cases in addition to the spherical harmonic refinement shown.
Figure 9. a) spherical CI + IPF shows similar trends as b) Hough IQ + IPF.
Again, spherical indexing’s confidence index correlates well with pattern quality. In Figure 9, a) spherical CI + IPF shows similar trends as b) Hough IQ + IPF.
Callahan, P. G., & De Graef, M. (2013). Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations. Microscopy and Microanalysis, 19(5), 1255-1265.
Callahan, P. G., & De Graef, M. (2013). Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations. Microscopy and Microanalysis, 19(5), 1255-1265.
Lenthe, W. C., Singh, S., & De Graef, M. (2019). A spherical harmonic transform approach to the indexing of electron backscattered diffraction patterns. Ultramicroscopy, 207, 112841.
Hielscher, R., Bartel, F., & Britton, T. B. (2019). Gazing at crystal balls: Electron backscatter diffraction pattern analysis and cross-correlation on the sphere. Ultramicroscopy, 207, 112836.
Sparks, G., Shade, P. A., Uchic, M. D., Niezgoda, S. R., Mills, M. J., & Obstalecki, M. (2021). High-precision orientation mapping from spherical harmonic transform indexing of electron backscatter diffraction patterns. Ultramicroscopy, 222, 113187.
Dr. Chang Lu, Application Specialist, Gatan & EDAX
In early 2022, Gatan and EDAX completed the integration, and our new division was named Electron Microscope Technology (EMT). As an EMT application scientist on the China applications team, I am responsible for almost all the Gatan and EDAX products for Northern China, on both Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) platforms. Therefore, I work with diversified products and diversified user groups that focus on different subject matters. In the first half of this year, I found that the data analysis software from EMT Gatan’s DigitalMicrograph® (DM) and EDAX’s OIM Analysis™ are not completely isolated, but in many cases, they can cooperate with each other to help our customers.
For instance, DM can do a series of electron microscopy-related data processing. For some energy dispersive spectroscopy (EDS) mapping data from the minor content, there are various methods to achieve smoothing and enhance the contrast. While in the MSA panel, the principal component analysis (PCA) function can be helpful in terms of high-resolution EDS mapping. However, in today’s EDAX blog, I will talk a little bit more about one feature in OIM Analysis that could potentially benefit a lot of Gatan camera users.
In northern China, there are a group of Gatan users who are focused on nanoscale phases and grains in the TEM. In most scenarios, they heavily employ electron diffraction or bright field imaging to make judgments. However, it is really difficult to determine the unknown (unidentified but has a known x-ray diffraction (XRD) pattern and chemical composition, so there is a potential for it) phase by simply relying on the minor changes of grayscale bright field images. You may say diffraction could help. Yes, a clean, beautiful diffractogram of a particular crystal direction can be helpful. But, no, you need to find the zone axis carefully. If this unknown phase has a crystal structure of low symmetry, most of the time, the effort will be in vain. Generally speaking, the Difpack tool in the DM software could help in determining d-spacing and angles, however, it is not intuitive enough to know the sample at first sight.
The solution is pattern simulation with OIM Matrix™. At first, I noticed this feature because it helped an EDAX user who was studying strains. It can easily export a theoretical Kikuchi pattern for a specific sample orientation with zero stress. Then one day, I had a sudden thought during my morning shower. Maybe I can change the acceleration voltage to 200 kV (typical for TEM), and the sample tilt angle to 0° (make it flat). After entering a specific orientation, we can get a Kikuchi pattern under TEM conditions! For example, take the simulated pattern from NdCeB. With Kinematic Color Overlay, we can also find out what crystal plane corresponds to a specific Kikuchi line. Now, when we start changing the zone axis in an unidentified sample, we can first simulate several orientations and compare them with what we see under TEM. In this way, the process of finding the Kikuchi pole turns out to be very convenient.
Figure 1. A simulated pattern from NdCeB using OIM Matrix.
Now, when some Gatan users bring in some “weird” unidentified samples and say they want to find various zone axis for doing diffractions. I don’t worry about it. I think from a problem-solving point of view, the powerful software from both Gatan and EDAX, like the integration of two companies, can also be combined to solve complex and difficult problems for our customers in the future.
Dr. David Stowe, Senior Product Manager – EDS and SEM Products, EDAX/Gatan
My friends and family have always thought that, as a microscopist, I spend my working days in a darkened room staring at dimly lit screens or developing negatives. Yet, the reality of working for a commercial company in the electron microscopy business could hardly be more different—scientific meetings, workshops, and spending time with users have allowed me to travel the world and make friends with some of the most interesting people. It’s always been a source of wonder and amazement for my family that the microscopic world could provide so many opportunities to see our world at large!
Sadly, over the last two years, our daily routines have been aligned much more closely to those visions of darkened rooms and computer screens than we care to remember. However, for many of us, there does appear to be (sun)light at the end of the tunnel. In recent weeks, I attended my first in-person workshop in almost two years. Together with colleagues and more than 60 researchers, I traveled to Munich to attend the Gatan-EDAX Leading Edge Workshop held at the Allianz Arena (home to Fußball-Club Bayern München e. V.—known to many as Bayern Munich). As lovely as it was to visit such a famous sporting stadium, the enjoyment of attending scientific talks and engaging in exciting technical discussions with leading researchers far outweighed the attraction of the venue. The feedback from everyone who participated in the day was incredibly positive. Many of us walked away from the event with new ideas inspired by discussions that day.
One of my fears regarding the impact of COVID-19 on the scientific community is the impact that the lack of these in-person interactions has had on innovation, both in terms of new scientific ideas and technological advancements. While working from home, many of us have missed those stimulating ‘coffee break conversations’ with colleagues outside of our teams. To add, there has been a noticeable drop in interactions on virtual platforms at scientific conferences and commercial webinars, with many preferring to review material offline at a time of their choosing.
Fortunately for EDAX, the opportunity for engagement with others during the pandemic had rarely been as high. Since 2019, we have been joined in the Materials Analysis Division of AMETEK by Gatan; the exchange of ideas between the R&D and Applications teams of the two companies has been significant. Within the last year, we have already seen the first innovations arising from the interactions between the two companies with the announcement of EDAX EDS Powered by Gatan for elemental analysis in the transmission electron microscope, simultaneous EDS and cathodoluminescence spectroscopy, and the development of a workflow solution for lithium analysis in the scanning electron microscope.
Within the last month, our applications teams were able to quantify the lithium content in lithium-ion battery cathode materials for the first time using the lithium-composition by difference method (Li-CDM).
Figure 1. First quantitative analysis of lithium content in cathode materials using the Li-CDM technique.
Their analysis used a range of tools from EDAX and Gatan to prepare, transfer, and analyze the specimen in the scanning electron microscope, highlighting the potential for innovation by Gatan and EDAX working together.
Figure 2. The tools from EDAX and Gatan used for the quantitative analysis of lithium in metal oxide cathode materials.
With the imminent arrival of the latest Microscopy and Microanalysis conference in Portland, OR, I am sure we will learn far more about how the changes to working practices have impacted innovation in our world. I am excited to leave my darkened room and discover your latest works in electron microscopy. I am sure that all participants will enjoy and value the personal interactions that are so important for innovation.
Matt Chipman, Sales Manager – Western U.S., EDAX and Gatan
I recently watched a local news story about inflation in consumer goods. The reporter wanted to know if the dollar store could save you money on groceries. The general answer was perhaps on some items, but it wasn’t significant. However, it was interesting to see how some stores focus on a perceived value instead of a real value to its consumer. First, the dollar store raised its starting price from $1.00 to $1.25. Then they used odd-sized packages that were not equivalent to regular grocery store items, making a direct comparison difficult and offering minimal to no real savings. Finally, the dollar store’s selection was very limited so you may end up back at the regular grocery store for anything other than packaged goods.
So, what does this have to do with the microanalysis business? Well, I believe it’s important to look at the big picture with real, tangible benefits that can impact your research. By offering both EDAX and Gatan products, there are more opportunities to combine different technologies to enable unique analyses that can provide a tremendous value to your material studies.
One great example is the quantification of lithium on a scanning electron microscope. By uniting Gatan’s low-kV OnPoint™ Backscattered Electron Detector with EDAX’s Octane Elite Super EDS Detector, this one-of-a-kind analysis is now possible, surpassing what can be done by either technique alone.
Figure 1. The lithium mapping from joint characterization of the EDAX Octane Elite EDS Detector and Gatan OnPoint BSE Detector.
Not to forget, we’ve also been combining the strengths of the Gatan DigitalMicrograph® Software with the EDAX EDS detector technology for TEMs. I believe we are just beginning to scratch the surface of creative things we can do by joining microanalysis systems and techniques. I love discussing creative ways my customers can coalesce microanalysis techniques to do something new.
Figure 2. Multimodal data acquisition of EELS and EDS data combines the chemical sensitivity of EELS with the broad compositional mapping of EDS. Pictured – STEM EELS/EDS mapping of vertical channel 3D NAND acquired with DigitalMicrograph software.
I hope we can all figure out ways to get a real, noticeable value from the equipment we purchase during this time of inflation. I hope to hear ideas from some of you as you tell me about the needs of your laboratories.
It has been an interesting experience to build our OIM Matrix™ software package. As you may know, OIM Matrix is partially a front-end user interface to the EMsoft package developed by Professor Marc De Graef’s group at Carnegie Mellon University to make it convenient to use within the framework of OIM Analysis™. I have learned a lot in the process and am grateful for Marc’s patience with my many questions. Will Lenthe recently joined the EBSD group at EDAX. Will worked as a Post-Doc in Marc’s group, and his additional insights have been invaluable as we are striving to build the second generation of OIM Matrix. It will be easier to use, more robust, and provide some significant speed gains.
While our initial focus for OIM Matrix was on helping users improve the indexing of EBSD patterns from difficult-to-index materials, I’ve been surprised by how useful it has been for testing our software. It has also helped us in developing some of our new features. Having well-simulated patterns for known orientations and EBSD/SEM geometries is very helpful.
I used OIM Matrix for a study on feldspars. According to Wikipedia:
“Feldspars are a group of rock-forming aluminum tectosilicate minerals containing sodium, calcium, potassium, or barium. The most common members of the feldspar group are the plagioclase (sodium-calcium) feldspars and the alkali (potassium-sodium) feldspars. Feldspars make up about 60% of the Earth’s crust and 41% of the Earth’s continental crust by weight.”
Given that feldspars are relatively common, we are frequently asked to help index them. They are difficult, as a poster at the 2019 Quantitative Microanalysis (QMA) conference detailed . I thought it might be interesting to see what we could learn about the limits of EBSD in characterizing these materials. I won’t give you all that we learned in that little study, but what I thought was an interesting snapshot. Figure 1 shows a phase diagram for the feldspar group of minerals.
Figure 1. Phase diagram for the feldspar group.
To start, I looked in the American Mineralogist Crystal Structure Database (AMCSD) for all the relevant entries I could find. There are a lot of variants. Here is a table:
Table 1. Number of entries in AMCSD for each feldspar.
I enjoy seeing pattern simulation results, but producing 149 master patterns  would take more patience than I have (each master pattern calculation can take several hours for these low-symmetry materials). So, I selected one entry for each mineral type. I tried to find one that seemed most representative of all the other entries in the set. After calculating the eight master patterns, I simulated one individual pattern at the same orientation for each mineral, as shown in Figure 2. Note that they are all similar, with the most deviation coming from the anorthite and sanidine end members of the series.
Figure 2. Patterns were simulated at Euler angles of (30°, 30°, 30°) for each feldspar.
To quantify the differences, I calculated the normalized dot-products  for all pattern pairs to get the following table. A value of “1” indicates the patterns are identical. As expected by the initial observation, the biggest difference is the sanidine to albite pair of patterns.
Table 2. Normalized dot products.
Of course, the next step would be to see how this holds up to experimental patterns and dictionary indexing . I hope to eventually do this with samples Professor Rudy Wenk of Stanford University kindly gave me. Rudy has been one of the major contributors to the entries in the AMCSD for feldspars.
There was one more virtual experiment I thought would be interesting. I wanted to ascertain how much the chemical species in the feldspar series influenced the patterns. To do this, I created an average structure instead of using the lattice parameters for each feldspar. I then populated these structures with atoms to maintain the chemical composition ratios specified for each series. A master pattern for each ideal structure was calculated. Three hundred forty patterns were simulated uniformly, covering orientation space with a spacing of approximately 30° between orientations. The average normalized dot products were calculated for each pattern against the albite pattern at the same orientation. Figure 3 shows the results.
Figure 3. The normalized dot product of simulated patterns for idealized structures against the albite simulated patterns.
Clearly, the dot products are all very near 1, indicating that the differences in the simulated patterns due to chemical composition are small for these chemical species. This suggests that coupling EBSD with EDS is critical when trying to differentiate the different feldspar minerals. While this small study has not changed the world of feldspar indexing, it has, at least, been a stimulating study of simulating for me.
 B Schneider, and J Fournelle (2019) “Using Quantitative and Qualitative Analysis to Confirm Phase Identities for Large Area EBSD Mapping of Geological Thin Sections” Poster at Microanalysis Society Topical Conference: Quantitative Microanalysis, University of Minnesota, Minneapolis MN, June 2019.
 PG Callahan, and M De Graef (2013) “Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations” Microscopy and Microanalysis, 19, 1255-1265.
 S Singh, and M De Graef (2016) “Orientation sampling for dictionary-based diffraction pattern indexing methods” Modelling and Simulation in Materials Science and Engineering, 24, 085013.
 K Marquardt, M De Graef, S Singh, H Marquardt, A Rosenthal, and S Koizuimi (2019) “Quantitative electron backscatter diffraction (EBSD) data analyses using the dictionary indexing (DI) approach: Overcoming indexing difficulties on geological materials” American Mineralogist: Journal of Earth and Planetary Materials, 102, 1843-1855.
As a classically trained chemist, most of my career has been spent looking into the challenges presented within the materials science world. Not so coincidentally, I never spent much time thinking about the problems that face our scientific brethren in the life sciences. Cells, tissues, biomolecules – these things were the squishy equivalent of a foreign language to me.
In the past few years, while working in the Atomic Force Microscopy space and the Electron Microscopy (EM) space, I kept coming across researchers from the life sciences who asked questions that sounded more and more like traditional materials science questions. What is the structure of my biomolecule? What elements are present, and in what proportion in this tissue or vesicle? What are the mechanical properties of my cell? And I thought, what kinds of questions should we be asking, and where can we use classic materials analysis methods (in this case, Energy Dispersive Spectroscopy (EDS)) to answer questions in the life sciences? Like any good researcher, this led me to a literature search to see what’s been done before. I’d like to share what I found and what problems we can potentially solve with EDS.
Taking a half-step back, I should briefly explain the principles of EDS as a technique to make sure we’re all on the same page. The too long; didn’t read (TL;DR) explanation of EDS goes something like this. When the electron beam interacts with a sample within an electron microscope, X-rays are emitted. Each element releases X-rays with a unique energy signature, proportionally increasing as a function of their atomic number (e.g., carbon X-rays are lower energy than iron X-rays, which are lower still than lead X-rays). An EDS X-ray detector (like EDAX manufactures) captures these X-rays, identifies the elements present in the sample, and quantifies their concentration. The big advantage of an EDS detector in your SEM is identifying which elements are present. The disadvantages are that it doesn’t discriminate the structure of how these elements are bonded to one another, what molecules or more complex compounds might be present, and historically, EDS hasn’t been tremendously efficient at detecting or quantifying elements lighter than, say, fluorine (the latest generation EDS detectors by-and-large have overcome this, and now routinely measure elements as light as boron, beryllium, and in the right conditions, lithium).
The latter disadvantage has historically been only part of the limiting factor in the general acceptance of EDS in the life science community. Biological systems are basically a collection of carbon, nitrogen, hydrogen, and oxygen, and the structural arrangement of these elements is what matters. EDS didn’t do that, unlike, say, vibrational spectroscopy (Fourier-transform infrared spectroscopy (FTIR) or Raman) or mass spectroscopy, and therefore was discounted for lack of usefulness to the standard biologist. But, with the novel researcher looking for any edge they can find to learn more about their system, new attention is being given to attributes where heavy element detection or accumulation is present in biological systems.
Mineralization and bioaccumulation of materials in tissues and systems is a perfect example of how EDS provides new insights into biological processes. Heavier elements like calcium, phosphorus, and potassium are accumulated and concentrated in easily detectable amounts in tissues leading to the formation of biominerals like kidney stones, sclerotic materials, and bone spurs. EDS provides a simple and clear method to visualize and quantify how these elements are distributed.
Figure 1. STEM EDS images of human sclerotic tissues to show elemental concentrations.
There are numerous examples in the literature of using nanoparticles from a host of different elements to understand cellular and biomechanistic behaviors better. From tumor growth studies using iron and copper nanoparticles to track the deposition of drugs within a cancer cell, to using zinc and iron nanoparticles to understand biomaterial scaffolding, to using silver and gold nanoparticles to understand the efficacy of erectile dysfunction drugs, nanoparticles allow for a targeted tracking of materials using EDS. 
Toxicological leaching of biological implants for dental and orthopedic research to novel biomaterials always require a toxicological study to ensure that the materials used do not leach or otherwise migrate during in vivo applications. EDS is a suitable tool to evaluate lifetime studies looking for the flow of titanium, nickel, iron, or other metallic elements during post-mortem analysis of implanted structures. In addition, environmental leaching of hazardous materials, which are accumulated in plant life, can also be measured via SEM EDS. The ability of different phenotypes of plants to absorb iron or manganese from the soil and concentrate it in the cellular structure can be measured effectively.
Figure 2. EDS spectra and cartoon characterization showing changes in Fe or Mn uptake and concenration as a function of phenotype of Arabidopsis thaliana cotelydons seed pods. 
These are just a few examples of the numerous ways that SEM, STEM, and TEM-based EDS can be used to complement research in the life sciences. As we continue to see a blending of the materials and biological worlds, I look forward to seeing more examples of elemental analysis being used to further scientific discovery.
Satoshi Hara, E. et al, Nanostructural analysis of distinct nucleation sites in pathological mineralization. RSC; Mater. Adv., 2021, 2, 4423.
As an applications engineer, it is always fun to play with cutting-edge products. Last year, I got an exciting new lab partner, an Orbis PC Micro X-ray Fluorescence (Micro-XRF) Analyzer, which is an excellent complement to Scanning Electron Microscopy – Energy Dispersive Spectroscopy (SEM-EDS)-based X-ray microanalysis.
Figure 1. The Orbis PC Micro-XRF Analyzer.
For those of you who are not familiar with Micro-XRF, it is a technique similar to Energy Dispersive Spectroscopy (EDS) in that they both detect generated X-rays after interaction with the sample. For EDS, X-rays are generated by electrons boarding the sample, while in a Micro-XRF unit, fluorescent X-rays are excited by high-energy X-rays emitted from the X-ray tube. Silicon Drift Detectors (SDDs) are used for X-ray detection in modern EDS and Micro-XRF systems. Data collection is also similar because it is possible to use either one to do qualitative and quantitative analysis, mapping, and linescan.
This benchtop Orbis PC analyzer utilizes the benefits of conventional XRF while implementing micro-spot X-rays down to 30 μm by employing a polycapillary technique with a moveable stage. For higher Z elements, it improves the detection limits ten times or more than SEM-EDS. It uses higher-energy X-rays to generate lines that are not detectable with EDS, such as Sr L, Zr K, and Ag K, which is useful when lower energy lines overlap in the EDS spectrum. The industry-exclusive motorized turret, integrating video and X-ray optics, provides coaxial X-ray analysis and sample view perpendicular to the sample surface for more accurate sample positioning and no shadowing of the X-ray beam. The analysis is non-destructive, with no beam damage to the sample, and minimal sample preparation is required.
Grinding and polishing of the sample are not generally required, and conductivity is not an issue. Sample loading is flexible in that thicker samples can be loaded directly on the stage, and thinner samples, particulates, and fibers can be mounted. The sample shape and height can be irregular, and the large sample chamber in this benchtop Micro-XRF unit can accommodate a wide range of sample sizes. Samples can be run either in low-vacuum mode or air mode, allowing the analysis of liquids or samples that will dehydrate in a vacuum. An SEM-based Micro-XRF system does not have many of the benefits brought by this benchtop unit. Once the sample is loaded in an SEM chamber, all the requirements of SEM samples apply. The chamber size and stage of an SEM largely limit the sample dimensions, and non-conductive samples must be coated. The ability to analyze samples that cannot tolerate a vacuum atmosphere is also lost.
Figure 2. The unique four-position turret. Position 1 is the high magnification video, and position 2 is the 30 μm polycapillary X-ray optic. Position 3 and 4 are 1 mm and 2 mm collimators, respectively.
Figure 3. The video (green) and X-ray (dark red) paths of the Orbis Analyzer are coaxial and perpendicular to the sample surface, which means the X-ray path is observable in the video, and there is no shadowing of the X-ray beam. If the X-ray path is non-coaxial (red), it can be blocked by the high topography object.
Since SEM-EDS and Micro-XRF share many similarities and work together to accomplish the complete needs of spectral analysis (see the How to Correlate Micro-XRF and SEM-EDS for Optimal X-ray Characterization of Materials article in the March 2022 issue of the Insight newsletter), I always like to correlate them from every aspect. The absorption edge is the most recent one that caught my attention. For EDS users, if you ever take a close look at the Bremsstrahlung background modeling in the APEX™ software, it is not a smooth curve but exhibits sharp edges (e.g., Figure 4). These are absorption edges, indicating the minimum energy required for an element to eject an electron from its core orbital to create a vacancy. For example, the absorption edge of Ni K lies at approximately 8.33 keV. As the electron energy reaches this value, there is a huge spike in energy attenuation because this is the point that the excitation of Ni K lines begins (Figure 5). The Mass Absorption Coefficient quantitatively represents each element’s absorption of energy. The self-absorption of X-ray photons in a specimen is the dominant effect in EDS, as well as the Bremsstrahlung background distribution shape. The mass absorption coefficient jumps visibly influence the spectrum, mainly in the soft X-ray region. Our Bremsstrahlung background modeling includes these absorption edges for fine control and accurate background correction.
Figure 4. The absorption edge of Fe K at approximately 7.11 keV in an EDS spectrum.
Figure 5. The absorption edges of Cr, Fe, and Ni K lines.
The absorption edge plays an extraordinary role in Micro-XRF since the design of primary beam filters employs the knowledge of absorption edges. The Orbis PC unit is equipped with six primary beam filters to preferentially absorb X-rays at certain ranges to reduce the background to improve detection limits and eliminate artifact peaks. The filter wheel is placed between the X-ray tube and X-ray optic, so the X-rays scattered by the filter do not reach the sample (Figure 6). Only X-rays focused by the optic or collimated by the collimator reach the sample for accurate sample targeting. Figure 7 shows the background in the spectrum if the X-rays generated from the X-ray tube are exposed to a Ni filter. There is a strong correlation between the background in this figure and the graph illustration in Figure 5. The X-ray attenuation decreases as the absorption edge at approximately 8.33 keV is approached in Figure 5. This coincides with more and more of the tube X-rays penetrating through the filter and being present in the spectrum. Once the energy reaches 8.33 keV, there is a sudden increase in the absorption of X-rays shown in Figure 5, and this is why a huge amount of the background signal is absent in Figure 7 since most of the signal is now absorbed by the Ni filter. After the significant jump at the absorption edge, the attenuation continues to decrease as energy increases in Figure 5. This correlates to the background getting higher and higher in Figure 7 since more and more tube X-rays continue to penetrate through the Ni filter. The area with the lowest background signal in Figure 7 is the high-sensitivity region where the Ni filter cleans up the spectrum, allowing true elemental peaks of interest to show up. Figure 8 is an example of the detection limits of As in an As2O3 sample. The Al-heavy and Ni filters significantly increase the peak-to-background ratio to push the detection limit to a single-digit ppm-level.
Figure 6. Schematic of filter wheel design in Orbis system.
Figure 7. Background spectrum from the X-ray tube after being exposed to a Ni filter.
Figure 8. A spectrum overlay of As2O3 was collected using an Orbis PC without a filter (red), with Al-heavy (blue), and Ni (green) filters.
Expect a few new application notes and experiment briefs from this unique lab partner!