A fusion of excellence: the thrilling synergy of Gatan and EDAX in our merged company, advancing science in Central and Eastern Europe

Rudolf Krentik, Direct Sales and Distributor Manager CEE, Gatan/EDAX

In electron microscopy, precision and insight are the bedrock of scientific discovery. When Gatan, a company specializing in transmission electron microscopy (TEM), and EDAX, a leader in analytical scanning electron microscopy techniques, including energy dispersive x-ray spectroscopy (EDS), wavelength dispersive spectroscopy (WDS), and electron backscatter diffraction (EBSD) decided to merge, it created a unique and exciting environment. This is the story of how the merger of these two renowned companies changed the game, particularly how it transformed the landscape for scientists in Central and Eastern Europe (CEE), where I took a role as sales manager.

A symphony of expertise

Gatan brought its unparalleled knowledge of high-resolution TEM imaging, allowing scientists to scrutinize samples at the atomic level. On the other hand, EDAX excelled in SEM, capturing fine details while analyzing elemental composition. The merger was a meeting of minds and machines, combining the best of both worlds.

The power of integration

The fusion of Gatan and EDAX under one roof unleashed a wave of possibilities for scientists in CEE. Researchers, scientists, and engineers now have access to an unprecedented range of imaging and analytical capabilities. From exploring the innermost structure of nanomaterials with TEM to revealing the intricate topography of surfaces with SEM and conducting precise elemental analysis with EDS-WDS, the comprehensive suite of tools is a game-changer for those pushing the boundaries of science and technology in the region.

A new playground for discovery in CEE

The exciting environment that emerged from the merger has created a palpable synergy, which is especially beneficial to scientists in CEE. It’s not just about the advanced hardware but the convergence of ideas, collaboration, and innovation. Scientists in CEE are now working on projects that seamlessly transition between TEM, SEM, and EDS, gaining holistic insights that were previously unimaginable.

Whether it’s delving into the intricate lattice structures of advanced materials, meticulously examining the surface features of biological specimens, or identifying the elemental composition of a sample, the combined expertise and equipment offer the ideal platform for exploration. It’s no longer about choosing between TEM and SEM; it’s about having the best of both worlds for comprehensive analysis.

The impact on research and industry in CEE

The implications of this merger extend beyond the lab and profoundly affect research and industry in CEE. The seamless integration of TEM, SEM, and EDS accelerates research, product development, and quality control across various sectors.

One example is from the automotive industry. The fast-growing electronic vehicle market brought new challenges in analyzing lithium content in lithium batteries. Lithium is unstable when exposed to air and, hence, almost impossible to analyze in SEM. However, with the combination of a backscatter electron detector with very high dynamic range from Gatan and an EDAX EDS detector with extreme sensitivity for low energies, lithium can be mapped to see where it is and can be quantified with a high accuracy of 1 wt%.

(left) Map of the Li content in NMC 811 particles and (right) re-scaled Ni, Mn, Co, and O elemental maps after accounting for the Li content. Note that the grey color in the lithium map corresponds to regions of the sample that were not suitable for analysis by Cipher due to the significant fraction of H in the epoxy.

Figure 1. (left) Map of the Li content in NMC 811 particles and (right) re-scaled Ni, Mn, Co, and O elemental maps after accounting for the Li content. Note that the grey color in the lithium map corresponds to regions of the sample that were not suitable for analysis by Cipher due to the significant fraction of H in the epoxy.

Providing cutting-edge technology in CEE

The merger has had a transformative impact on me, who is responsible for Central and Eastern Europe. It has allowed me to provide cutting-edge technology to scientists in the region, enabling them to make groundbreaking discoveries and advancements in their respective fields. The dynamic combination of our scientific products delivers the tools needed to push the boundaries of science in CEE.

Unveiling the power of EBSD in SEM

Furthermore, the EBSD technology provided by EDAX offers complete material characterization within the SEM. This addition has expanded the capabilities, providing scientists with a comprehensive solution for studying the microstructure and crystallography of materials. The latest development at EDAX provides the fastest EBSD cameras on the market and a solution for sensitive materials requiring low kV and low current conditions in SEM. All this is addressed by the first and only direct detection EBSD system, Clarity. Seeing the customer’s enthusiasm when you show them something that wasn’t possible until recently is great.

Figure 2. The EDAX Clarity EBSD Detector Series.

Enthusiastically looking to the future

Our entire European team is honored to be part of this incredible journey. We eagerly look forward to unforeseen developments in electron microscopy, materials analysis, and the world of science in Central and Eastern Europe. The possibilities are limitless, and as we continue to pioneer breakthroughs, the future looks even more thrilling. The journey has just begun, and the world of science and industry is the ultimate beneficiary of this exciting union.

Semper Fi

Matt Chipman, Sales Manager – Western U.S., Gatan/EDAX

Over the summer, I have been reflecting on the greater impact of my sales experience with EDAX and Gatan. The research our customers do tends to make life better for all of us collectively. I am proud to be a part of that, but often it’s difficult to see immediate impacts in the lives of people.

Some years ago, I was calling on a laboratory in Pearl Harbor, Hawaii, that does forensic anthropology in an attempt to account for missing service personnel from the US military. This was close to my heart because my father was missing in action before I was even two years old and was never accounted for. This lab didn’t end up purchasing my equipment, but it was well-equipped for the types of samples they would receive. They would use SEM-EDS to analyze aircraft crash site debris or anything that could be recovered that could prove the ultimate demise of U.S. soldiers. SEM-EDS plays an important role in forensic analysis by providing characteristics and compositional information of physical evidence (e.g., gunshot residue, glass and paint fragments, and explosives), which helps identify, compare, and correlate evidence to individuals, locations, or objects.

Figure 1. Captain Ralph Jim Chipman.

I didn’t know if any of the samples would end up being related to my father’s incident, but it was nice to know they had the tools needed and the motivation to keep searching. They indeed kept searching, and more than 50 years after the loss of his aircraft, they brought home a dog tag with my father’s name on it and a few teeth and bone fragments. The teeth positively identified my father. He is no longer missing! I am so grateful for those who never gave up looking.

Figure 2. Notice saying Captain Ralph Jim Chipman is no longer missing in action.

I am hopeful that material from the crash site still being analyzed can positively identify the navigator who sat next to my father in the aircraft. I also hope to learn whether electron microscopy and x-ray spectroscopy was an instrumental part of this effort to sift through different kinds of evidence. I am glad to have associated with some of the many people who keep searching. This work makes lives better and can have a huge impact on individuals and families of those lost. I am honored to be a small part of research that makes all of our lives better and can have a huge impact on people we will likely never meet.

Semper Fi!

Blockbuster inspirations

Jordan Moering, Director Sales & Service Europe, Gatan/EDAX

When I look around me, I feel surrounded by the consequences of the good work we are doing at Gatan/EDAX.

Sometimes, this is less obvious, like knowing that electron backscatter diffraction (EBSD) and energy dispersive x-ray spectroscopy (EDS) were likely used to evaluate the microstructural performance of the steel used in the bridge I’m driving over. Or appreciating that the vaccines used to mitigate the damage of COVID-19 were developed largely due to the performance of Gatan cameras used in cryo-EM.

Recently, I felt surrounded by reminders of our equipment on blockbuster movie billboards, as I was in awe of the perseverance and astounding capabilities of scientists at the dawn of the quantum revolution. I find it humbling and inspiring that researchers from around the world raced to better understand the subatomic physics of the atom. Since then, it’s fascinating to see how nuclear science has evolved for the betterment of society to address many important issues relating to health, the environment, and fuel.

Today, many of the labs featured in a recent blockbuster movie are still doing critical work in nuclear energy, advanced materials, and reactor design that benefits all of us. As you can imagine, many challenges plaguing researchers in the late 1930s are still prevalent today. Radioactive or “hot” materials are some of the most challenging samples to study, as the very properties of these samples (e.g., radiation) that give them their unique energy-generating abilities also make them incredibly difficult to examine. For example, many detectors used in analyzing x-ray radiation can be immeasurably destroyed in the presence of gamma, beta, or nuclear radiation. Nonetheless, scientists still need to understand the chemical or crystallographic makeup of these materials.

One challenge in studying these materials is the discrimination of plutonium and uranium in the composition of mixed-oxide (MOX) fuels. As it turns out, the overlapping x-ray peak characteristics of these two very different metals make it quite difficult or impossible to qualify some of these fuels properly. The answer to this puzzle is, of course, wavelength dispersive spectroscopy (WDS). Because WDS uses diffracting crystals to interrogate the x-ray spectrum wavelength by wavelength (or energy by energy), it can present sensitivities up to 10x higher than traditional EDS alone. As a result, a single U/Pu peak that shows up in the EDS spectrum can be resolved to show two, three, or even more peaks that are all convoluted together. It is a very real statement to say that modern nuclear science has a critical need for WDS and other forms of analytical components in the scanning electron microscope (SEM).

Figure 1. WDS analysis (red) shows the presence of numerous metal peaks of Ta, Hf, and W within a single peak on the EDS (blue) spectrum. A similar result may be seen when investigating the characteristic peaks of nuclear materials like Pu and U.

Personally, I find this incredibly fulfilling and exciting, and hopefully, you’ll feel the same.

Being more precise, again

Dr. Stuart Wright, Senior Scientist, Gatan/EDAX

In my last blog posting, I was excited to show results from version 9 of EDAX OIM Analysis™ for refining EBSD orientation measurements. However, two questions have been gnawing at me since that post. (1) How much does the size of the patterns affect the results? and (2) How sensitive is the refinement to noise in the patterns? To explore these two questions, I will use data from the same silicon single crystal I used in my previous post – a 1 x 1 mm scan with a 30 µm step size. The patterns were 480 x 480 pixels and of excellent quality.

I added two levels of Poisson noise to the patterns, as shown in Figure 1, and will term these noise levels 1 and 2 for the subsequent analysis.

Figure 1. Si single crystal patterns processed with adaptive histogram equalization [1]. (a) initial pattern, (b) pattern after a moderate level of added noise, and (c) pattern after a significant level of added noise.

The next step was to bin the patterns, index them using spherical indexing, and then apply orientation refinement as implemented in version 9 of EDAX OIM Matrix™. To perform the experiments, I binned the patterns to 360 × 360, 240 × 240, 160 × 160, 120 × 120, 96 × 96, 80 × 80, 60 × 60, and 48 × 48. After binning, I re-indexed them using spherical indexing and then calculated kernel average misorientations (KAM). I used the average KAM value as a measure of precision and plotted that against the binned pattern size for all three noise levels (0, 1, and 2). Figure 2 shows the results of the experiments.

Figure 2. Plot of average KAM values vs. pattern width for all three noise levels.

I have a couple of observations from these results.

  • In general, the first level of noise has only a minimal impact on the precision, whereas the higher level of noise has a more significant impact.
  • For noise levels 0 and 1, the average KAM values remain relatively constant until the pattern size dips below 120 × 120 pixels. Surprisingly, good results can be obtained until the smallest size of 48 × 48 pixels is reached. For noise level 2, the precision drops off significantly at a pattern size of 96 × 96. Those using Velocity cameras have probably noticed that the default pattern size is 120 × 120 pixels. Similar results to those I’ve presented here lead us to choose 120 × 120 pixels as the default. These results confirm the soundness of that choice.

I hope these results can guide the expectations for what orientation refinement can achieve in your samples. We will announce the official release of EDAX OIM Analysis 9 in the next few weeks. We hope you are excited to apply it to your materials. The orientation refinement tools are part of EDAX OIM Matrix, which is an add-on module. While you wait for your copy of version 9, make sure you save the patterns you plan to apply orientation refinement measurements to. No, I’m not getting paid by the hard drive manufacturers 😉.

Figure 3. Screenshot of EDAX APEX showing where the check-box to save patterns is located within the software.

[1] Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J.B. and Zuiderveld, K., 1987. Adaptive histogram equalization and its variations. Computer vision, graphics, and image processing 39: 355-368.

Embracing the return

Dr. Shangshang Mu, Application Scientist, Gatan/EDAX

Over the past year, I’ve rekindled my enjoyment of traveling as I visited customers in the Americas, Asia, and across Europe. During my return journey, I was deeply touched by an airline billboard at the Munich, Germany airport that read, “We all live under one sun. Let’s see it again.” Indeed, it is genuinely nice to see the world once more since reemerging from the pandemic.

While flying over Hudson Bay, an inland marginal sea of the Arctic Ocean, I saw numerous ice caps floating on the water from the aircraft’s belly camera view. To me, these were very reminiscent of the counts per second (CPS) map (Figure 1) in one of the wavelength dispersive spectrometry (WDS) datasets I shared with customers during these trips. Although they were orders of magnitude larger than the micron-scale sample, the resemblance was striking.

Figure 1. Ice caps in Hudson Bay (left) resemble the CPS map of a Si-W-Ta sample (right).

Throughout these journeys, our EDAX Lambda WDS system was one of the hot topics drawing customers’ attention. This parallel beam spectrometer features a compact design compatible with almost every scanning electron microscope (SEM). The improved energy resolution and sensitivity and lower limits of detection make it an excellent supplement to your energy dispersive spectroscopy (EDS) detectors. The CPS map I referred to was captured from a Si-W-Ta sample. The energy peaks of Si K, W M, and Ta M are heavily overlapped, with only approximately 30 eV energy difference between each other. Lambda WDS systems provide up to 15x better energy resolution than typical EDS systems, effectively resolving the ambiguity in analysis.

Figure 2. Overlay of EDS (red outline) and WDS (cyan color) spectra from the central area of the Si-W-Ta sample.

The overlay of EDS/WDS spectra from the central area of the map shows that the Lambda WDS system intrinsically resolves the overlapping EDS peaks (red outline), as depicted by the cyan color WDS spectrum (Figure 2). The shortcoming of EDS in resolving these overlapping peaks results in the distributions of the three elements appearing identical in EDS maps. However, the WDS maps provide clear and distinct visualizations of the individual distributions of the three elements (Figure 3).

Figure 3. EDS (top) and WDS (bottom) maps of the Si-W-Ta sample. The WDS maps resolve the artifacts due to Ta M, Si K, and W M peak overlaps in the EDS maps.

This year’s M&M meeting is just around the corner. If you are traveling to this entirely in-person event, stop by our booth (#504) to check out our integrated EDS-WDS SEM solutions and many other products that will capture your interest.

It runs (or rolls) in the family

Matt Nowell, EBSD Product Manager, Gatan/EDAX

I have two sons graduating this year. My oldest son is graduating college with a Materials Science and Engineering degree and is interested in materials characterization. My middle son is graduating high school and has grown up refining ores in Minecraft, casting characters from Dungeons and Dragons, and 3D printing school projects. I’m glad they are both interested in materials and how they can affect daily living. I’ve also been a little sentimental and nostalgic thinking about how we have tried to learn more about materials in our household.

One activity they have always enjoyed is collecting pressed coins. These machines squeeze a coin between two rollers, one of which has an engraving on its surface that is then imprinted onto the stretched and flattened surface of the deformed coin. We have collected these coins from around the world. One example is shown in Figure 1, which is a pressed coin from Universal Studios. This was the most recent addition to the collection. I decided to press a second coin that we could prepare and characterize with electron backscatter diffraction (EBSD) to see the microstructural developments that occur during the pressing process.

Figure 1. A pressed coin from Universal Studios.

The pressed coin was mechanically polished down to 0.02 µm colloidal silica and then analyzed using the new EDAX Velocity Ultra EBSD detector. This new detector allowed for high-speed data collection at acquisition rates of 6,500 indexed patterns per second. Figure 2 shows the inverse pole figure (IPF) orientation map collected from a 134 µm x 104 µm area with a 100 nm step size, with the coloring relative to the orientations aligned with the sample’s surface normal direction. At these speeds, the acquisition time was less than five minutes. A copper blank was used instead of the traditional penny for this sample. This was noticeable when indexing the EBSD patterns. Since 1982, pennies have been made of zinc coated with copper. Zinc has a hexagonal crystal structure, while the EBSD patterns from this coin were face-centered cubic (FCC). EDS analysis confirmed that the material was copper.

Figure 2. An IPF orientation map collect from a 134 µm x 104 µm area of the pressed coin with a 100 nm step size. The coloring is relative to the orientations aligned with the sample’s surface normal direction.

The IPF map shows a significant amount of deformation. This can be seen in the IPF maps with the color variation within each grain. This is, of course, expected, as the elongation and thinning of the coin are easily observed while watching the machine. EBSD is an ideal tool for characterizing this deformation within the material. While there are several different map types to visualize local misorientations and deformation, Figure 3 shows one of my favorites, the grain reference orientation deviation (GROD) map. In this map, the grains are first calculated by grouping measurements of similar orientation using a 5° tolerance angle. Next, the average orientation of each grain is calculated. Finally, each pixel within a grain is colored according to its misorientation from the average orientation of its grain. The microstructure’s largest GROD angular value is 61.9°, indicating a large spread of orientations. This map also shows the grain boundaries as black lines to indicate the original grain boundary positions.

Figure 3. A GROD map of the pressed coin.

Figure 4 shows a fascinating view of how the material is deformed within a selected grain. This chart was created by drawing a line within a grain and plotting the point-to-point and point-to-origin misorientations along this line. The point-to-point distribution shows that each step is typically a small misorientation value below the grain tolerance angle. The point-to-origin distribution shows an accumulation of misorientations within this grain, with the overall misorientation changing more than 30° over the 25 µm distance within the grain. This type of result always gets me thinking about what a grain really is in a deformed material.

Figure 4. A view of how the material is deformed within a selected grain. This chart was created by drawing a line within a grain and plotting the point-to-point and point-to-origin misorientations along this line.

Figure 5. The (001), (111), and (110) pole figures calculated from the measured orientations.

Figure 5 shows the (001), (111), and (110) pole figures calculated from the measured orientations. These pole figures are incomplete and resemble what is expected for a rolled FCC material. This is due to the small number of grains sampled in this area. A second map was collected over a 1,148 µm x 895 µm area with a 2 µm step size in under a minute to get a better sampling of the entire microstructure. The pole figures for this data are shown in Figure 6. Comparing Figures 5 and 6 shows that the additional sampling within the second scan adds more symmetry to the pole figures.

Figure 6. The pole figures for the second map that was collected over a 1,148 µm x 895 µm area with a 2 µm step size.

This was a fun example to show the different data types that can be derived from EBSD measurements. In materials science, understanding the relationship between materials processing and the resulting microstructure is critical to understanding the material’s final properties. It’s clear that pressing a coin causes significant deformation within the material, which can then be measured and quantified with EBSD. Maybe the next time we go to the zoo, we will vary the speed at which we roll the coins and see what effect that has on the data.

Being more precise

Dr. Stuart Wright, Senior Scientist, Gatan/EDAX

The precision and accuracy of orientation measurements by electron backscatter diffraction (EBSD) have been of interest since the advent of EBSD [1, 2]. In contrast, reliability (in terms of correctly identifying the orientation at least within 5°) was of greater concern when indexing was first automated (there is a section of my thesis [3] devoted to precision, as well as Krieger Lassen’s thesis [4]). I’ve written a few papers on the subject [5 – 7], and there have been several more by other authors [8 – 11]. High-resolution EBSD (HREBSD) has shown success in markedly improving precision [12]. Now that dictionary indexing (DI) has become more common; there has been a resurgence in papers on the precision that can be achieved using DI [13 – 15]. I know that is a lot of references for a blog post, but I wanted to give you an idea of how many different research groups have studied angular precision in EBSD measurements – the references given are only a sampling; there are certainly more.

Will Lenthe and I have been working hard to improve the dictionary indexing capabilities in the EDAX OIM Matrix™ add-on module to EDAX OIM Analysis™. In addition, Will has added the ability to perform spherical indexing within OIM Matrix [16 – 17] (see Will’s “New Tools for EBSD Data Collection and Analysis” webinar for more information). These new capabilities will be available soon in OIM Analysis 9. I’m excited about the progress we’ve made. You will find OIM Matrix much easier to use and more robust. In addition, we’ve sped up many aspects of OIM Analysis, which will help with the big datasets routinely obtained with the EDAX Velocity™ cameras.

The precision of indexing via spherical indexing has recently been explored [18]. Using OIM Analysis 9, we’ve been exploring what we can achieve in terms of orientation precision with orientation refinement [19 – 21] applied to initial indexing results obtained by Hough transform-based indexing, dictionary indexing, and spherical indexing. We haven’t quantified our results yet. Still, the KAM maps (which indicate the orientation precision) we’ve obtained are so promising that I want to show our preliminary results. Our refinement method is essentially a hybrid of that proposed by Singh, Ram, and De Graef [19] and Pang, Larsen, and Schuh [21]. But for the spherical indexing, we also have implemented an additional refinement in the harmonic frequency space. Figure 1 shows some results I am excited to share.

Figure 1. KAM maps from nickel [22]. (Top row) As-indexed, (middle row) with NPAR for Hough-based indexing and refinement in the spherical harmonics for spherical indexing, and (bottom row) after real-space refinement. The first column is for Hough-based indexing, columns 2 – 4 are for dictionary indexing with different dictionary target disorientations, and columns 5 – 6 are for SI with different harmonic bandwidths.

It is pretty interesting that the KAM maps after refinement are all nearly the same, no matter which type of indexing was used to obtain the initial orientation measurements. We do not expect much plastic strain or permanent deformation in these samples, so the reduced KAM values are more of what we expect for the sample.

Here is another set of results for a silicon single crystal. The scan is approximately 1 x 1 mm with a 30 m step size. You can see the dramatic improvement in these results. Unfortunately, the two points with the largest KAM values are due to some dust particles on the sample’s surface.

Figure 2. KAM maps were constructed using Hough-based indexing, SI, and SI followed by refinement.

We are very excited to get these advancements into your hands and are putting in extra hours to get the software ready for release. We hope you are as precisely excited as we are to apply it to your samples!

[1] Harland CJ, Akhter P, Venables JA (1981) Accurate microcrystallography at high spatial resolution using electron backscattering patterns in a field emission gun scanning electron microscope. Journal of Physics E 14:175-182
[2] Dingley DJ (1981) A Comparison of Diffraction Techniques for the SEM. Scanning Electron Microscopy IV: 273-286
[3] Wright SI (1992) Individual Lattice Orientation Measurements Development and Applications of a Fully Automatic Technique. Ph.D. Thesis., Yale University.
[4] Krieger Lassen NC (1994) Automated Determination of Crystal Orientations from Electron Backscattering Patterns. Ph.D. Thesis, Danmarks Tekniske Universitet.
[5] Wright S, Nowell M (2008) High-Speed EBSD. Advanced Materials and Processes 66: 29-31
[6] Wright SI, Basinger JA, Nowell MM (2012) Angular precision of automated electron backscatter diffraction measurements. Materials Science Forum 702: 548-553
[7] Wright SI, Nowell MM, de Kloe R, Chan L (2014) Orientation Precision of Electron Backscatter Diffraction Measurements Near Grain Boundaries. Microscopy and Microanalysis 20:852-863
[8] Humphreys FJ, Huang Y, Brough I, Harris C (1999) Electron backscatter diffraction of grain and subgrain structures – resolution considerations. Journal of Microscopy – Oxford 195:212-216.
[9] Demirel MC, El-Dasher BS, Adams BL, Rollett AD (2000) Studies on the Accuracy of Electron Backscatter Diffraction Measurements. In: Schwartz AJ, Kumar M, Adams BL (eds) Electron Backscatter Diffraction in Materials Science. Kluwer Academic/Plenum Publishers, New York, pp 65-74.
[10] Godfrey A, Wu GL, Liu Q (2002) Characterisation of Orientation Noise during EBSP Investigation of Deformed Samples. In: Lee DN (ed) ICOTOM 13, Seoul, Korea, Textures of Materials. Trans Tech Publications Inc., pp 221-226.
[11] Ram F, Zaefferer S, Jäpel T, Raabe D (2015) Error analysis of the crystal orientations and disorientations obtained by the classical electron backscatter diffraction technique. Journal of Applied Crystallography 48: 797-813
[12] Wilkinson AJ, Britton TB (2012) Strains, planes, and EBSD in materials science. Materials Today 15: 366-376
[13] Ram F, Singh S, Wright SI, De Graef M (2017) Error Analysis of Crystal Orientations Obtained by the Dictionary Approach to EBSD Indexing. Ultramicroscopy 181:17-26.
[14] Nolze G, Jürgens M, Olbricht J, Winkelmann A (2018) Improving the precision of orientation measurements from technical materials via EBSD pattern matching. Acta Materialia 159:408-415
[15] Shi Q, Loisnard D, Dan C, Zhang F, Zhong H, Li H, Li Y, Chen Z, Wang H, Roux S (2021) Calibration of crystal orientation and pattern center of EBSD using integrated digital image correlation. Materials Characterization 178:111206
[16] Lenthe W, Singh S, De Graef M (2019) A spherical harmonic transform approach to the indexing of electron backscattered diffraction patterns. Ultramicroscopy 207:112841
[17] Hielscher R, Bartel F, Britton TB (2019) Gazing at crystal balls: Electron backscatter diffraction pattern analysis and cross-correlation on the sphere. Ultramicroscopy 207:112836
[18] Sparks G, Shade PA, Uchic MD, Niezgoda SR, Mills MJ, Obstalecki M (2021) High-precision orientation mapping from spherical harmonic transform indexing of electron backscatter diffraction patterns. Ultramicroscopy 222:113187
[19] Singh S, Ram F, De Graef M (2017) Application of forward models to crystal orientation refinement. Journal of Applied Crystallography 50:1664-1676.
[20] Winkelmann A, Jablon BM, Tong V, Trager‐Cowan C, Mingard K (2020) Improving EBSD precision by orientation refinement with full pattern matching. Journal of Microscopy 277:79-92
[21] Pang EL, Larsen PM, Schuh CA (2020) Global optimization for accurate determination of EBSD pattern centers. Ultramicroscopy 209:112876
[22] Wright SI, Nowell MM, Lindeman SP, Camus PP, De Graef M, Jackson MA (2015) Introduction and comparison of new EBSD post-processing methodologies. Ultramicroscopy 159:81-94

Grain Analysis in OIM Analysis

Dr. Sophie Yan, Applications Engineer, EDAX

Recently, we held a webinar on Grain Analysis in OIM Analysis™. After the webinar, many users mentioned that the basic operation overview was very helpful. Since there was a very enthusiastic response, I want to take this opportunity to share these fundamental tips and tricks with the greater electron backscatter diffraction (EBSD) community.

Perhaps the most popular EBSD application is grain analysis, as it’s fundamental to characterizing many materials. Because the results of grain analysis are sometimes consistent or inconsistent with other tests, it’s great to start with a basic understanding of a grain with respect to EBSD and how grain analysis works.

The definition of a grain in OIM Analysis differs from the strict academic definition, which refers to the collection of pixels within a certain orientation range. This orientation range, namely grain tolerance angle, can be changed in OIM Analysis, which is generally set to 5° by default. You can also vary the number of pixels in a grain (the default is 2). These parameters affect the result of grain size, so we should pay attention to them in the analysis. The prerequisite of grain analysis is that the data is statistically valuable. Sometimes this requires a lot of tests to achieve the goal, repetitive studies to diminish errors, or the data should be filtered or processed before the analysis (per relevant standards, accordingly).

Figure 1. A typical grain map.

A standard display for grain size analysis is the Grain Size (diameter) chart. First, the grain is fit to a circle, and then the software calculates the diameter. The data distribution range and average grain size are on the chart’s right side. The most frequent question users ask is, “What is the formula to calculate the average grain size?”. In fact, two results of the average grain size, which are calculated by two different methods, are shown. The ‘number’ method calculates the average area of each grain first (the sum area is divided by grain number values first) before it determines the diameter. In contrast, it considers different weights due to different areas for the ‘area’ method. Since large grains have larger weight percentages, it first calculates the average grain area using different weight percentages, then calculates the average grain size.

In addition to the average grain size, OIM Analysis offers a variety of charts and plots to characterize grain shape. The most popular one is the grain shape aspect ratio, an essential parameter to display the columnar grain property (grains are fit as an ellipse). In addition to the shape aspect ratio, the Grain Shape Orientation in OIM Analysis shows the angle between the long axis and the horizontal direction, which is suitable for grains with a specific growth direction.

OIM Analysis offers numerous functions. Concerning grain analysis, there are six different charts for grain size and eight for grain shapes. Some charts are not common, but they have corresponding application scenarios. If you do not know the meaning of those charts, you can query the OIM Analysis Help file to get relative information.

Grain analysis is a very common function of EBSD applications. As a webinar speaker, I enjoyed digging up some less familiar details so users could gain a deeper understanding of software operations. I look forward to continually introducing webinar topics to meet the EBSD community’s needs and make greater progress in the new year.


Dr. Sophie Yan, Applications Engineer, EDAX

最近我们办了一期OIM Analysis如何进行晶粒分析的直播,效果颇出我意料。大家对于基本操作的热情令我始料未及,在播出之后联系我的人中,也往往会提到这一场直播对他们有所帮助。我本以为,这一类关于基本操作的直播并不太吸引人,充其量也不过是成为大家在日常操作中备查的工具视频而已;但,果然是我灯下黑,事实并不是如此。我也借此机会,给大家分享一些基本的原则或窍门。


OIM中的晶粒不同于学术上严格的定义,是指在一定取向范围内的像素点的集合。这个取向范围,即容差角,是可以设置的,一般默认为5度。像素点的个数,也同样可以设置,默认个数为2; 这些参数的设置,其实都会影响我们统计晶粒粒径的结果,因此需要在粒径分析中予以注意。当然,进行粒径分析的前提是数据具有统计意义,有时需要进行大量的重复性的测试来减少误差,也需要在测试之前对数据进行筛选或处理(可参照相关标准进行操作)。


最常见的粒度分析的结果是将晶粒拟合成圆,计算其平均直径,即常见的Grain Size(diameter)曲线。曲线(或柱状图)的右边是数据部分,有不同粒度分布的占比,也列出了平均粒径。这是我被客户问得最多的部分,即,我们的平均粒径的结果是怎么计算的:这个平均粒径其实列出了两种结果,由两种方法分别计算。“number”方法是指按数数目的方法,先计算每个颗粒平均的面积(总面积除以晶粒数),然后再计算直径;”area”则要考虑因面积不同而带来的不同权重,大颗粒占权重较大,按权重计算出颗粒的平均面积,再计算平均粒径。

除平均粒径外,OIM Analysis还提供了多种表征颗粒形状的图表。最常见的是短长比(grain shape aspect ratio),是描述非等轴晶粒径的重要参数(晶粒被拟合成椭圆)。当然,除了短边长边的比值,有些颗粒有形状还有明显的择优,按特定的方向生长,针对这一点,Grain Shape Orientation表征长边与水平方向的角度,可以描述这一特性。

OIM Analysis提供非常丰富的功能,晶粒粒径有6种不同图表,晶粒形状有8种。有些图表可能不太常用,但都有对应的应用场景。对于这些相对冷门的图表,一般用户,如不了解其功能,可以在OIM Analysis提供的帮助文件中查询,可以得到关于其功能或定义的相关信息。


Reaching Out

Dr. René de Kloe, Applications Scientist, EDAX

2022 was a year of changes. In the beginning, I set up a desk in the scanning electron microscope (SEM) lab where, without truly reaching out, I only needed to turn in my chair to switch from emails and virtual customers on my laptop to the live energy dispersive spectroscopy (EDS) and electron backscatter diffraction (EBSD) system and real data on the microscope. As travel restrictions gradually eased worldwide, we were all able to start meeting “real” people again. After almost two years of being grounded, I finally met people face to face again, discussing their analysis needs, and answering questions do not compare to online meetings. We restarted in-person training courses, and I participated in many external courses, exhibitions, and conferences, reaching out to microscopists all over Europe.

And as always, I try to correlate real life with some nice application examples. And what is similar to reaching out to people in the microanalysis world? Reaching out to things! So, what came to mind are remote thermal sensors, which most of us will have at home in the kitchen: a thermostat in an oven and a wired thermometer that you can use to measure food temperatures. And I just happened to have a broken one that was ready to be cut up and analyzed.

Figure 1. a) A food thermometer and b) an oven thermostat sensor.

On the outside, these two sensors looked very similar; both were thin metal tubes connected to a control unit. Because of this similarity, I was also expecting more or less the same measuring method, like using a thermocouple in both thermometers. But to my surprise, that was not quite the case.

The long tube of the food thermometer was mostly empty. Right at the tip, I found this little sensor about 1 mm across connected to copper wires that led to the control unit. After mounting and careful sectioning, I could collect EDS maps showing that the sensor consisted of a central block of Mn-Co-Fe-oxide material sandwiched between silver electrodes soldered to the copper-plated Ni wires.

Note that in the image, you only see one of the wires, the other is still below the surface, and I did not want to polish it any deeper.

Figure 2. The temperature sensor taken out of the tube of the food thermometer.

Figure 3. A forward scatter SEM image of the polished cross-section showing the central MnCoFe-oxide material and one of the connecting wires.

This was no thermocouple.

Figure 4. The element distribution in the sensor.

Figure 5. The EDS spectrum of the central CoMnFe-oxide area.

Instead, the principle of this sensor is based on measuring the changing resistivity with temperature. The EBSD map of the central Co-Mn-De oxide area shows a coarse-grained structure without any preferred orientation to make the resistivity uniform in all directions.

Figure 6. An EBSD IPF on Image Quality map of the sensor in the food thermometer.

Figure 7. (001) pole figure of the MnCoFe oxide phase, showing a random orientation distribution.

And where the tube of the food thermometer was mostly empty, the tube of the oven thermostat sensor was completely empty. There were not even electrical connections. The sensor was simply a thin hollow metal tube that contained a gas that expands when heated. This expansion would move a small disk with a measurement gauge that was then correlated with a temperature readout. Although this sounded very simple, some clever engineering was needed to prevent the tube from pinching shut when bending and moving it during installation.

I cut and polished the tube, and an EBSD map of the entire cross-section is shown below.

Figure 8. a) EBSD IQ and b) IPF maps of a cross-section through the entire tube of the oven thermostat sensor.

The tube is constructed out of three layers of a Fe-Cr-Ni alloy with fine-grained multiphase chromium phosphide layers in between. This microstructure is what provides corrosion protection, and it also adds flexibility to the tube. And this, in turn, is crucial to prevent cracks from forming that would cause the leaking of the contained gas, which is critical in getting a good temperature reading.

The detailed map below shows a section of the phosphide layer. There are two chromium phosphide phases, and in between, there are dendritic Ni grains that link everything together.

Figure 9. EDS maps showing the composition of one of the phosphide layers.

Figure 10. EBSD IPF maps of the different phases. a) All phases on a PRIAS center image, b) CrP, c) Fe matrix, and d) Ni dendrites, Cr3P.

When you look at the microstructure of both sensors in detail, it is possible to determine how they work, and you can appreciate why they have been designed as they are. The two devices are efficient and tailored to their intended use. The oven thermostat is designed to be mounted in a fixed position to be secure so that it can be used for a very long time. The food thermometer is very flexible and can easily be moved around.

In that respect, I feel there is another similarity between these sensors and the different kinds of meetings between people we have experienced over the past year. It does not matter how you do it; you can always reach out and feel some warmth.

I wish everybody a very happy and peaceful 2023.