Author: edaxblog


Dr. René de Kloe, Applications Specialist, EDAX

After finishing my Ph.D. in structural geology at Utrecht University, I joined EDAX in 2001 to become an EBSD Application Specialist. At the time, I expected that my experience with investigating microstructures of rocks would be equally applicable to metals and ceramics. After all, grains are grains, and deformation structures in rocks and ceramics are not all that different. At least, that was my impression when I visited the International Microscopy Conference (IMC14) in Cancun, Mexico, in 1998. My poster on melt and deformation microstructures in partially molten olivine-orthopyroxene rocks was the only geological contribution in a metallurgical section. At first glance, nobody even noticed that my TEM images were not on metal, and I had many interesting discussions on potential deformation mechanisms and how metallurgists and geologists could work together and learn from each other.

Quickly after joining EDAX, I recognized that there is a fundamental difference between the work of a material scientist and that of a geologist. To generalize, a material scientist works on developing materials like metal alloys or ceramics for a specific application and typically has a pretty good idea of what kind of processing has been applied to a material. As a geologist working with natural rocks, you have to work with the material and try to unpick its formation history that may span millions of years by observations alone. This turned out to be a helpful skill in looking at customer samples that have come my way over the years. In many cases, I only have very limited information on the background of a sample. Then, I have to rely on my observations to unravel the sequence of events and select the analytical options for the successful characterization of the microstructure. Observing materials without assuming that you know what happened allows you to catch unexpected events in production processes, and that makes a very useful connection between characterizing materials in geology and materials science.

For example, during a recent training course, we worked on a stud welded sample. This was a new technique for me, and a first-practice EBSD map showed a very interesting microstructure. We used that map during the training, but since then, I have taken the time to do a complete characterization to see if I could identify what really happens during these short milliseconds that it takes to weld a stud to a substrate.

Online, I found a general description of the welding technique (Figure 1). In the stud welding process, an electrical arc is generated between the tip of the stud and the substrate so that a small melt pool forms at the contact. The stud is then plunged into the melt pool, which solidifies within milliseconds, creating a strong bond. Can we use EBSD to tell us what really happens?

Figure 1. Stud welding procedure, 1) The stud is placed close to the substrate. 2) An electric arc is created to melt the contact area on both sides. 3) The stud is pushed into the melt pool. 4) After a few ms of cooling time, the weld is complete.

And I start just like I would when looking at a rock outcrop. First, take a step back and look at the entire structure. Once we have seen that, we can zoom in on key areas to investigate what has happened.

The SEM image (Figure 2) does not clearly show how large the area affected by the welding process is in the cross-section. The expected microstructural changes can be illustrated using an EBSD Image Quality (IQ) map. An IQ map shows the contrast of the bands in the diffraction patterns where recrystallized areas with good quality patterns are bright. In contrast, deformed areas producing poor quality patterns appear dark. Therefore, any changes in the crystalline microstructure are likely to be clearly visible in the EBSD results.

Figure 2. a) The polished sample in 3 cm resin mount. b) An SEM secondary electron montage image of the stud weld contact. (1806 fields, 15.7 x 10.7 mm)

Before we look at the weld structure itself, we need to check the starting microstructure of the base materials to recognize what changes during welding. The threaded stud on top consists of deformed austenitic steel. At the top of the IQ map (Figure 3a), individual grains can be seen along the stud’s center axis. With increasing deformation towards the threads, the grains become smaller and EBSD patterns degrade, resulting in very dark IQ values at the stud surface.

Figure 3. a) An IQ map and b) a phase map of a montage EBSD scan with 221 fields, 45 million points @ 1.5 µm steps. Red is ferrite and green is austenite. c) An IPF map on IQ showing the crystal directions parallel to the sample normal direction. The scalebar is 4 mm.

This structure is related to the wire drawing of the stud and the shaping of the threads.
The ferrite substrate appears homogeneous with an equiaxed grain structure and consistently good quality patterns. However, the change in color from green to purple in the IPF map (Figure 3c) shows a gradient in the dominant grain orientation distribution towards the interface. This does not appear to be related to the welding process and is probably introduced during the production of the ferritic steel sheet. The welding process dramatically changes the IQ appearance of both the stud and the substrate (Figure 3a). In the welding zone, the austenitic material becomes bright while the ferrite goes very dark. Between these two areas, a band of material is present that has been melted and then quickly solidified. In this solidified material, there are swirls of darker IQ values that appear indicative of the melt’s movement during the plunge phase when the stud gets pushed into the melt pool. The EBSD phase map in Figure 3b indicates that these darker bands in the melted zone consist of ferrite grains. The main components of the weld zone are shown in Figure 4.

Figure 4. A weld structure IQ map with the main structures indicated.

To see what causes these changes in the IQ map, we have to look more closely at the EBSD results. The bright IQ band in the austenite stud consists of small equiaxed grains that are fully recrystallized and produce good quality patterns (Figure 5, 6a).

Figure 5. An EBSD montage map of the entire weld zone, IPF on IQ map, 133 fields and 91M points @ 750 nm steps. The scalebar is 4 mm.

Figure 6. a) A detailed IPF on PRIAS center map of the recrystallized austenite layer. b) An IPF on PRIAS center map showing the columnar austenite grains crystallized from the former melt pool.

This structure suggests that the temperature in this band has been high enough to allow recrystallization of the deformed structure. Still, the metal remained solid and cooled down too fast to enable significant grain growth.

The fine-grained austenite microstructure is in stark contrast to the area that has been melted. Atomic movements in the melt were so fast that upon cooling, very large columnar grains could form in two bands with a seam in the middle (Figure 6b). This double band structure indicates solidification that started on both sides of the melt pool on the solid metal surfaces until the grains met in the middle.

The structure on the ferrite side is more complex. In the middle of the weld contact, a triangular area of apparent columnar grains has formed with a void at the bottom tip (Figure 7). Underneath these larger grains is a fine-grained band over the full width of the weld. In both zones, the EBSD patterns have deteriorated compared to the original ferrite matrix structure.

Figure 7. An IPF on PRIAS center map showing only the ferrite phase.

The substrate’s ferrite structure has a complicating factor when compared to the austenitic steel stud. When ferrite is heated close to the melting temperature, the crystal structure changes into that of austenite. This means that the columnar grains in the ferrite’s center area actually crystallized from the melt pool as large austenite grains, similar to those shown in Figure 6b. However, in this case, the austenitic crystal structure is not stable at room temperature, and upon cooling, these grains changed back into ferrite. This back transformation does not simply change the structure of the entire grain into the ferrite structure. Instead, it creates organized clusters of ferrite grains with different “child” orientations inside each original austenite grain. At low magnifications, these clusters give the impression of columnar grains with a single orientation. In reality, each column may contain many small grains with up to 12 different orientations (Figure 8).

Figure 8. A close up of the columnar grains in the ferrite area.

Below the melt pool, the original small ferrite grains also transform into austenite during the welding process, but the temperature does not get high enough to enable significant grain growth while the metal remains solid (Figure 9). When these small austenite grains transform back into ferrite during cooling, the original grains also get split up into many small ferrite children, which are a little deformed. That is what causes the dark appearance of this zone in the IQ map in Figure 3a.

Figure 9. An IPF on PRIAS center map of the fine-grained ferrite zone below the melt pool.

The latest version of OIM Analysis™ contains a new tool to investigate this phase transformation from high-temperature austenite into the low-temperature ferrite phase. The clusters of child ferrite grains can now be fit together into the original high-temperature austenite structure that was present during (weld) processing. This is extremely helpful in understanding the contact between the ferrite substrate and the crystallized melt pool.

The EBSD map in Figure 10a shows the compound columnar grains in the ferrite substrate with the two bands of columnar grains that were formed from the melt on top. There is no obvious microstructural correlation between the ferrite and the first layer of columnar grains from the melt in this image. However, when the high-temperature austenite microstructure is reconstructed, the columns in the ferrite substrate coalesce into single austenite grains that match the bottom layer’s orientation perfectly.

Figure 10. An IPF map of the ferrite-melt contact zone. a) The microstructure as measured and b) the microstructure after reconstruction of the high-temperature austenitic microstructure. The scalebar is 1 mm.

This means that these columnar grains grew continuously from the melt at high temperature when the weld was formed then separated into the fragmented ferrite structure and intact austenite structure upon cooling. The exact location of this boundary between the final ferrite and austenite phases is determined by the chemical composition, especially the Ni content (Figure 11). The ferrite substrate has little Ni, which causes the austenite to transform back into ferrite upon cooling.

Figure 11. A simultaneously collected EDS map for Ni. The scalebar is 1 mm.

Figure 12. An IQ detail map of the left melt pool, 2.5M points @ 1.5 µm steps. The scalebar is 900 µm.

Finally, the Ni distribution in the melt pool also explains the occurrence of the dark bands or swirls in the IQ map in Figure 12. When the stud was pushed into the melt pool, the melt fractions from the stud and ferrite substrate mixed, but not thoroughly. On the left side, an area that is somewhat depleted in Ni can be recognized in the melt pool, and where the Ni content drops below a threshold, these portions of the austenite grains that grew from the melt are not stable and transform into ferrite (Figure 13).

Figure 13. a) A phase map of the left melt pool area. Green is austenite and red is ferrite. b) An IPF on PRIAS center map of the left melt pool as measured. The scalebar is 900 µm.

The parent grain reconstruction (Figure 14) confirms that all the small ferrite grains exhibit orientations derived from the coarse austenite grain structure.

Figure 14. An IPF on PRIAS center map after parent grain reconstruction.

Just like the investigation of natural rocks, a detailed analysis of the microstructure of a metallurgical sample like this weld contact can provide a comprehensive image of the active microstructural processes with only minimal prior knowledge. Starting with an overview of the entire sample, followed by targeted scans of distinct parts of the microstructure using EBSD and EDS, a timeline of events can be reconstructed that produced a strong bond between two materials.

And what does it matter if the creation of a structure takes milliseconds or perhaps many millions of years? In the end, everything is connected.

Rubber Bands and Dynamical Diffraction Simulations

Dr. Jordan Moering, Mid-Atlantic Sales Manager, EDAX

Like any nerdy high schooler growing up in the suburbs of North Carolina, I had a lot of weird hobbies growing up. Some of these turned into scientific interests that brought me to grad school, and others just turned into party tricks (Rubik’s cube, anyone?). I’ve recently been thinking about one of these hobbies, especially after hearing our recent webinar on OIM Matrix and Forward Modeling.

When I was 15, I thought that rubber band balls were really cool. I’m not sure why, but I really enjoyed the twists and turns of the multiple layers of bands on top of each other. The aspect ratio of the band thickness to the curvature and angle of the bands was something I found really fascinating. Fast forward a few decades and what used to be a softball-sized rubber band ball is now a 12 kg behemoth that I pass every day while walking to my office. I’ve been giving a lot of thought to this ball recently because of how it mimics the dynamical diffraction simulation used in dictionary indexing.

Figure 1. (left) My 12 kg ball of rubber bands. (right) Simulated Kikuchi patterns for EBSD analysis.

I suppose what really fascinates me the most about these projections is how accurate they are. The fundamentals of diffraction can be so simply described in Bragg’s law, but the implications for these phenomena are profound. Because different crystallographic indices diffract incoming electrons at different intensities, the collected image shows the orientation of the crystal wherever the electron beam was parked. The resulting bands (called Kikuchi lines) are a direct representation of the material’s crystal structure.

Now, I’m not an expert on diffraction, but I find all this to be fascinating. What’s cool to me is that recent developments in computing and modeling have enabled new types of indexing. This includes Dictionary Indexing which utilizes an entirely synthesized library of diffraction patterns to correlate the likely orientation of every collected pattern when obtaining EBSD scans. What’s fascinating to me is that these simulations historically struggle to account for artifacts in the Kikuchi patterns like lens blurring, phosphor illumination, etc. With the advent of direct detection cameras however, there is no need to account for these as individual electrons create the image on the sensor. New techniques like forward model-based indexing are only accelerating the adoption of this new technique. And at the core of these new modeling techniques are simulations – simulations of Kikuchi patterns.

So yeah, I see my rubber band ball every day and think about simulated diffraction patterns. I suspect that it is a very low symmetry system based on the geometry.

EDAX and Gatan Bring You Lithium

Dave Durham, Sales Manager – U.S. Western, EDAX

It has been interesting to recently witness EDAX and Gatan working together to combine the technologies in our portfolios. Although technically, Gatan was acquired by AMETEK back in late 2019, it seems like 2021 has been a year where the integration of our two companies has really begun to hit its stride.

For example, I’ve seen how Gatan’s ion polishing instruments can dramatically improve indexing success for EDAX’s Electron Backscatter Diffraction (EBSD) users compared to the conventional methods for sample preparation. And I’ve seen EDAX’s Elite T Energy Dispersive Spectroscopy (EDS) System undergo a tremendous workflow improvement and ease-of-use overhaul with the implementation of Gatan’s Microscopy Suite user interface. It has been great stuff!

However, the most recent integration between our two companies is truly groundbreaking, and I’m thrilled to see what it will do to enhance the research being done in its field.

Hopefully, you’ve already seen the news mentioned on our website. For the first time, we’ve been able to perform quantitative mapping of lithium in the Scanning Electron Microscope (SEM) by combining the power of EDAX and Gatan detectors and software! These breakthrough results will enable a new level of lithium research that was previously never possible with the SEM.

Figure 1. EDAX and Gatan bring you lithium.

So who cares about lithium? Everyone should. Lithium compounds and alloys are very important materials with significant commercial value. The compounds are being implemented into lightweight structural alloys in the aerospace and automotive industries. They’re also utilized in lithium-ion batteries for small electronic devices and vehicles. Many governments worldwide have proposed plans to reduce dependence on legacy energy sources, which makes further development of lithium-based technologies critical to the adoption of these plans. This means significant investments are currently being made in R&D, failure analysis, and quality control of these materials.

Figure 2. (left) Lithium-ion battery cross-section prepared by Ilion II broad beam argon milling system. (right) EBSD IQ + orientation map revealing the microstructure of the heat-affected zone in a lightweight structural alloy.

So what are the issues with lithium? While electron microscopy and EDS are already essential characterization tools in this industry, there is a distinct inability to map lithium distribution in the SEM. This has presented a significant obstacle, holding back research on these tools. EDS is typically a valuable technique for material characterization, with high sensitivity and spatial resolution to allow for quantitative analysis on a wide range of sample types. But it is not possible to identify lithium in commercially important materials by EDS because:

  1. There is no guarantee that lithium X-rays will be produced from the sample. The X-ray energy and the number of photons produced from the specimen depend on the lithium bonding state. So, even if you have lithium in your sample, it does not mean that lithium X-rays will be generated.
  2. Even if a sample does generate lithium X-rays, they are easily absorbed back into the sample itself, contamination or oxidation, or by the EDS detector window before they can even reach the EDS detector.

Indeed, specialized windowless EDS detectors can detect lithium, but these have drawbacks that impede their practicality and largescale adoption. Even on samples that have a high lithium fluorescence, these special detectors have a limit of detection of about 20 wt %. This is equivalent to about half of the atoms in the sample being lithium, which restricts analysis to only metallic lithium or simple lithium compounds that may not be relevant to advanced lithium research or applications.

And having a specialized windowless EDS system potentially introduces a slew of operational issues/limitations with the detector that aren’t present with a “standard” windowed EDS system. It also restricts the detector’s utility on non- lithium -research-based applications in the lab.

So what have EDAX and Gatan done? We have solved these issues by using a patent-pending technique called the Composition by Difference Method. In this method, we quantify the backscattered electron signal to determine the mean atomic mass for all elements in a particular area of a sample. And from the same region, we collect the EDS signal to quantify the non-lithium elements. From that information, we have two data points that tell us the actual mean atomic mass from the region and a calculated value based on the EDS results — when they don’t agree with one another, it tells us we are missing something in the EDS data. That something we’re missing is lithium.

Figure 3. Data from the OnPoint and the Octane Elite Super are combined and analyzed to quantify lithium.

By using this method, and specifically by combining the EDAX Octane Elite Super EDS Detector and the Gatan OnPoint Backscattered Electron Detector to collect these two signals, we can now generate lithium maps quantitatively with single-digit mass percentages of lithium with sub-micron spatial resolution. This accuracy has been verified to ~1 wt. % lithium by an external accredited laboratory using Glow-discharge Optical Emission Spectroscopy (GDOES).

Figure 4. Secondary electron image and elemental metal fraction maps (by wt. %) of the same region of the MgLiAl alloy; white pixels are regions excluded from the analysis due to the influence of topography (identified by arrows in the secondary electron image) shown here for clarity.

This is a cutting-edge capability in the SEM, and it is a huge opportunity for anyone wanting to discover where lithium exists in their specimens. Just to reiterate, this method does not use a specially designed EDS system for lithium detection! It uses EDAX’s standard (windowed) Octane Elite Super and Gatan’s OnPoint BSE detector, along with EDAX and Gatan software. Simply amazing!

Now that EDAX and Gatan have introduced the ability to provide quantitative lithium analysis, that is:

  • A substantial improvement in limits of lithium detection
  • Insensitive to the lithium bonding state
  • More tolerant to contamination and oxidation
  • Not limited to metallic materials or simple lithium compounds
  • Free from windowless detector-related limitations on the SEM

It seems that we have helped open an avenue for our customers to expand their lithium research beyond anything previously possible. We are truly beginning a very exciting new stage in lithium analysis, and I can’t wait to see how this new capability is used and what comes next!

You can find more information on this new development in our experiment brief.

Learning From Customers

Matt Nowell, EBSD Product Manager, EDAX

As EBSD Product Manager, one of the things I have missed the most in the last 18 months during the COVID pandemic is visiting customers. Generally, in a year, I will attend a few meetings. Some are reoccurring: M&M for microscopy topics, TMS for materials science, and an annual EBSD meeting (either the RMS or MAS version, depending on the year) to keep up with the latest and greatest in these fields. Additionally, I will attend a new show to learn about potential markets and applications. It’s always enjoyable to meet both users and prospects to learn more about their applications and how EDAX tools can help their characterization needs.

In place of these shows, I’ve been turning towards social media to keep track of trends for EBSD. Twitter is one tool I use, where there is a strong scientific group that shares their thoughts on a range of subjects and offers support to each other in this networked community. Recently, my Twitter feed showed a beautiful EBSD map on the cover of Science. Professor Andrew Minor’s group out of UC Berkeley had used EDAX EBSD to analyze twinning in cryoforged titanium. I feel connected to this work, as I’ve looked at twinning in titanium on other samples (Bringing OIM Analysis Closer to Home blog). Seeing different posts about various applications helps me understand where EBSD is used is very exciting and rewarding.

Figure 1. September 17, 2021 issue of Science magazine featuring an EBSD orientation map of cryoforged titanium.

LinkedIn is another social media tool I use. One of my favorite things about this platform is seeing how the careers of different people I know have developed over the years. I turn 50 in a couple of weeks, and I’ve been involved in EBSD for over half of these years. With that experience, I’ve seen the generational development of scientists and engineers in my field. The post-docs who first adopted EBSD when I started are now department chairs and running their own research groups. The students who came to a training course now advise the new users at their companies on EBSD. Recent students are graduating and now asking about EBSD for their new positions. It’s easy to get a sense of how the EBSD knowledge I’ve shared with people has percolated out into the greater world.

While I expect to see some EBSD on Twitter and LinkedIn, this year, I also had a pleasant surprise finding some wonderful EBSD in Gizmodo ( I’ve had a strong interest in additive manufacturing since visiting NASA 15 years ago. Seeing this technology develop and how EBSD can help understand the microstructures produced is very satisfying to me. I reached out to Jake Benzing, who was the driver behind this post. This led to his group at NIST being featured in our latest EDAX Insight newsletter. It also helped me connect with a user and be better positioned to get feedback on using our products to drive development and improvement.

Figure 2. Ti-6Al-4V created by a form of AM called electron-beam melting powder-bed fusion. This map of grain orientations reveals an anisotropic microstructure, with respect to the build direction (Z). In this case, the internal porosity was sealed by a standard hot isostatic pressing treatment.

Research Must Go On

Rudolf Krentik, Sales Manager – Central and Eastern Europe, EDAX

It has been some time since I started working at EDAX as an Area Sales Manager for Central and Eastern Europe. When I think about it, Russia is by far the largest region compared to all the others. If sales grew linearly with the size of the area, I would probably be a millionaire. Unfortunately, it is not the case. The primary purpose of my work is to take care of our distributors and business partners in individual countries. I give them support in business cases, provide up-to-date information about our products, and sometimes I am also an intermediary for the serious requirements of our end customers. The work is very interesting, especially because I meet interesting people. EDAX’s customers are primarily scientists and engineers studying materials, solving complex problems, and dealing with development and innovation. Such meetings are often very fun, inspiring, and rewarding.

Figure 1. My new office.

The market situation has changed dramatically since 2015, when I started. COVID-19 has completely changed the way we work. Instead of meeting customers at scientific conferences, we all locked ourselves in our homes for a long time. After three months, I couldn’t stand it and rented a small office so that I wouldn’t go crazy at my home office with my wife and two small children, who were also schooling and working from home. So I was moving from my home office to an actual office, doing just the opposite of what others were doing during the pandemic.

Moving from real life to the online world was probably frustrating for many of us. Still, we had to adapt and start selling and communicating over the phone and especially over the internet. Online presentations and meetings are still the order of the day. This way of communication will be maintained in the future, that is quite certain. Unfortunately, this does not replace personal contact, which is essential for creating a relationship with customers. It can already be seen that interest in virtual conferences is declining. People are inherently interactive and need to share their needs and feelings with each other. This is not possible in the world of the internet. Therefore, we all hope that everything will return to normal soon. Our service technicians have been traveling to places where it is safe for quite a long time, and we salespeople are also starting to plan our first trips abroad. I’m actually partly writing this blog in Turkey on my first trip in 18 months.

Although it does not seem so, COVID has not yet caused significant losses or loss of orders in terms of business results. Our business is still in good condition. One of the factors that affects this is the life cycle of a business case. This can take months or even years. If we do not soon return to the life we are used to; it will have very negative consequences for our field. I mention this because we are currently at the stage where we want to launch several exciting products. You probably know that Gatan also belongs to our AMETEK family. The company is known for its leading technology in detection systems in TEM and SEM and other devices, e.g., for sample preparation. The acquisition of Gatan is a great benefit not only for AMETEK but also for EDAX. The combination of know-how, development, and experience in the electron microscopy field creates space for innovation and synergies that would not be possible.

Several novelties were introduced three weeks ago at M&M 2021. It is worth mentioning the EDAX EDS Powered by Gatan, in which EDAX hardware is now integrated into the software from Gatan. This brings many benefits, such as a unified GUI for all the TEM techniques available from Gatan. EDS analysis with Elite T can now be performed seamlessly with Gatan EELS, 4D STEM (STEMx), or other techniques. This makes it all much easier and faster. And as we know, time is money, and this is doubly true for time spent at the TEM.

Another interesting novelty is the cooperation of EDS and CL detectors. Thanks to an EDS-compatible cathodoluminescence (CL) mirror that enables line of sight from the sample to the EDS detector while still collecting the CL signal, we can obtain information about the material’s structure that was previously difficult to achieve.

When it comes to EBSD, EDAX has been the leading provider of this technique since the 90s. But for reliable analysis, one needs a high-quality sample preparation tool. Again, with the Gatan PECS II, we can offer a complete workflow from getting the sample ready to post-processing of acquired data. The latest news is also the hottest news. With the help of the highly sensitive OnPoint BSE and Octane Elite EDS Detectors, it is possible to detect lithium for the first time and quantify it. Unique technology, the accuracy of which is verified by another method, is now available and we are very anxious to introduce this product to our customers.

That is why we need to get the COVID-19 pandemic under control. Without the opportunity to travel and meet our customers, our work will be inefficient and not as much fun. However, the newly introduced devices and the ongoing development of the EDAX-Gatan collaboration gives us a strong hope that everything is on track and that our efforts are worthwhile.

Home Sweet Home

Dr. René de Kloe, Applications Specialist, EDAX

This last year has been different in many ways, both personally and at work. For me, it meant being in the office or working from home instead of being out and about and meeting customers and performing operator schools in person. This does not exactly mean that things are quieter, though! At home, I got confronted with lots of little maintenance things in and around my house that otherwise somehow manage to escape my attention. At work, lots of things vying for my attention have managed to land on my desk.

The upside is that with almost everything now being done through remote connections. I get to sit more at the microscope in the lab to work on customer samples, collect example datasets, perform system tests, and also practice collecting data on difficult samples so that I can support our customers better. To do that, I have the privilege of being able to choose which EBSD detector I want to mount, from the fast Velocity to the familiar Hikari to the sensitive Clarity Direct Electron System. But how do I decide what samples to use for such practice sessions?

Figure 1. A common garden snail (Cornu aspersum) and an empty shell used for the analysis.

In the past, I wrote about my habit of occasionally going “dumpster diving” to collect interesting materials (well, to be honest, I try to catch the things just before they land in the dumpster). That way, I have built up a nice collection of interesting alloys, rocks, and ceramics to keep me busy. But this time, I wanted to do something different, and an opportunity presented itself when I was working on a fun DIY project, a saddle stool for my daughter. On one of the days that I was shaping wood in my garden for the saddle-support, I noticed some garden snails moving about leisurely. These were the lucky chaps (Figure 1). While we occasionally feel the need to redecorate our walls to get a change of view, the snail’s home remains the same and follows him wherever he goes; sounds great! No need to do any decoration or maintenance, and always happy at home!

But all kidding aside, I have long been interested in the structure of these snail shells and have wanted to do microstructural analysis on one. So, when I found an empty shell nearby belonging to one of its cousins that had perished, I decided to try to do some Scanning Electron Microscope (SEM) imaging and collect Electron Backscatter Diffraction (EBSD) data to figure out how the shell was constructed. The fragility of the shell and especially the presence of organic material in between the carbonate crystals that make up the shell makes them challenging for EBSD, so I decided to mount my Clarity Detector and give it a very gentle try.

The outer layer that contains the shell’s color was already flaking off, so I had nice access to the shell’s outer surface without the need to clean or polish it. And with the Gatan PECS II Ion Mill that I have available, I prepared a cross-section of a small fragment. I was expecting a carbonate structure like you see in seashells and probably all made of calcite, which is the stable crystal form of CaCO3 at ambient temperatures. What I found was quite a bit more exotic and beautiful.

In the cross-section, the shell was made up of multiple layers (Figure 2). First, on the inside, a strong foundation made of diagonally placed crossed bars, then two layers of well-organized small grains, was topped by an organic layer containing the color markings.

Figure 2. A PECS II milled cross-section view of the shell with different layers. The dark skin on the top is the colored outer layer.

At the edge of the PECS II prepared cross-section, a part of the outer shell surface remained standing, providing a plan view of the structure just below the surface looking from the inside-out. In the image (Figure 3), a network of separated flat areas can be recognized with a feather-like structure on the top, which is the colored outer surface of the shell. An EDS map collected at the edge suggests that the smooth areas are made up of Ca-rich grains, which you would expect from a carbonate structure. Still, the deeper “trenches” contain an organic material with a higher C and O content, explaining why the shell is so beam-sensitive.

Figure 3. A plan view SEM image of the structure directly below the colored surface together with EDS maps showing the C (purple), O (green), and Ca (blue) distribution.

The EBSD data was collected from the outer surface, where I could peel off the colored organic layer. This left a clean but rough surface that allowed successful EBSD mapping without further polishing.

My first surprise here was the phase. All the patterns that I saw were not of calcite but aragonite (Figure 4). This form of calcium carbonate is stable at higher temperatures and forms nacre and pearls in shells in marine and freshwater environments. I was not expecting to see that in a land animal.

Figure 4. An aragonite EBSD pattern and orientation determination.

The second surprise was that the smooth areas that you can see in Figure 3 are not large single crystals but consist of a very fine-grained structure with an average grain size of only 700 nm (Figure 5). The organic bands are clearly visible by the absence of diffraction patterns – the irregular outline is caused by projection due to the surface topography.

Figure 5. Image Quality (IQ) and aragonite IPF maps of the outer surface of the shell. The uniform red color and (001) pole figure indicate a very strong preferred crystal orientation.

After this surface map, I wanted to try something more challenging and see if I could get some information on the crossbar area underneath. At the edge of the fractured bit of the shell, I could see the transition between the two layers with the crossbars on the left, which were then covered by the fine-grained outer surface (Figure 6).

Figure 6. An IQ map of the fracture surface. The lower left area shows the crossbar structure, then a thin strip with the fine-grained structure, and at the top right some organic material remains.

Because the fractured sample surface is very rough, EBSD patterns could not be collected everywhere. Nevertheless, a good indication of the microstructure could be obtained. The IPF map (Figure 7) shows the same color as the previous map, with all grains sharing the same crystal direction pointing out of the shell.

Figure 7. An IPF map showing the crystal direction perpendicular to the shell surface. All grains share the same color indicating that the [001] axes are aligned.

But looking at the in-plane directions showed a very different picture (Figure 8). Although the sample normal direction is close to [001] for all grains, the crystals in the crossbar structure are rotated by 90° and share a well-aligned [100] axis with the two main directions rotated by ~30° around it.

Figure 8. An IPF map along Axis 2 showing the in-plane crystal directions with corresponding color-coded pole figures.

Figure 9. Detail of the IPF map of the crossbar area with superimposed crystal orientations.

I often have a pretty good idea of what to expect regarding phases and microstructure in manufactured materials. Still, I am often surprised by the intricate structures in the smallest things in natural materials like these snail shells.

These maps indicate a fantastic level of biogenic crystallographic control in the snail shell formation. First, a well-organized interlocked fibrous layer with a fixed orientation relationship is then covered by a smooth layer of aragonite islands, bound together by an organic structure, and then topped by a flexible, colored protective layer. With such a house, no redecoration is necessary. Home sweet home indeed!


Dr. Stuart Wright, Senior Scientist, EDAX

Of all the papers I’ve written, my favorite title I’ve managed to sneak past the editors and reviewers is “Random thoughts on non-random misorientation distributions.” The paper is a write-up of a presentation I gave at a celebration of Professor David Dingley’s contributions to EBSD, which was held as a special version of the annual Royal Microscopy Society EBSD meeting at New Lanark in Scotland. It was a fun meeting as several of David’s former Ph.D. students shared some great stories and pictures of David, and the talks were a little less formal than usual, which led to some interesting discussions.

There are many terms used to describe the difference in crystallographic orientation between two crystal lattices: misorientation, disorientation, orientation difference, misorientation angle, minimum misorientation angle, grain boundary character, intercrystalline interface. One can get a bit “disoriented” trying to sort out all these different terms. Unfortunately, I am at fault for some of the confusion as I have tended to use the different terms loosely in my presentations and papers. But I am not the only one; I have seen some wandering in the definition of some of these terms as different researchers have followed up on the work of others. I will not pretend to be rigorous in this blog, but let me see if I can help sort through the different terms.

My first exposure to the idea of misorientation was from Bunge’s classic book Texture Analysis in Materials Science from 1969. I was first introduced to the book when I joined Professor Brent Adam’s Lab in 1985. We called it the “Red Bible,” as we had a very well-worn copy in the lab. We were even lucky enough to have Peter Morris with us at the time, who translated the book from German to English (a herculean task for a non-German speaker without modern tools like Google Translate). On page 44 of this book, you will find the following:

If two adjacent grains in a grain boundary have orientations g1 and g2, the orientation difference is thus given by:

g = g2 g1-1                                                                   (3.12)

This looks like a relatively simple expression, and we have generally calculated it using orientations described as matrices, and thus the result ∆g would also be a matrix. But the most common description of this orientation difference given in the literature would be an axis-angle pair. Any two crystals have at least one axis in common. A rotation about that axis will bring the two crystal lattices into coincidence.

Figure 1. Axis-angle description of misorientation.

While the equation above seems simple, we need to remember that, due to crystal symmetry, there are multiple symmetrically equivalent descriptions of the orientations g1 and g2. We can term the symmetry operators Li. These are the elements of the crystallographic point group symmetry for the crystals in question. For example, for a cubic crystal, there will be 24 symmetry elements. Since there are 24 symmetric equivalents for g1 and 24 for g2 that means there will be 576 symmetric equivalents for ∆g. In the expression below, the apostrophe denotes symmetrically equivalent.

g’12 = Lig2∙(Lig1)-1

As an example, here is a list for a random axis angle pair assuming cubic crystal symmetry: 12° @ 〈456〉. Note that the notation 〈uvw〉 denotes the family of crystal directions and [uvw] denotes a single crystal direction. Once again, for cubic symmetry, there are (in general) 24 [uvw] directions in the 〈uvw〉 family of directions (note in general there are 24 directions in the family, i.e. [123], [132], [-123], [-132], …. but this can be reduced for families where multiplicity plays a role, such as 〈00w〉 or 〈uuw〉…).

12.00(4 5 6)124.26(139 132 170)
82.16(2 18 155)125.80(118 121 148)
83.62(20 4 157)131.85(44 43 45)
85.06(4 45 325)169.37(2 161 177)
95.94(33 3 262)170.34(235 6 265)
97.23(4 20 177)171.30(2 149 172)
98.51(62 7 617)171.80(10 8 167)
108.17(39 38 40)173.17(8 12 167)
114.78(137 173 177)174.54(12 10 167)
116.39(130 103 136)178.07(25 196 221)
117.99(137 177 181)179.03(188 26 207)
122.71(149 153 192)179.03(155 18 179)

So, this is a list of symmetrical misorientations given as axis-angle pairs. The minimum rotation angle in this set is the disorientation. But, you will also see the disorientation called the orientation distance (Bunge equation 2.123), rotation angle and misorientation angle (OIM), minimum misorientation angle, as well as simply the misorientation, orientation difference, grain boundary angle, . For a little comic relief at intense EBSD workshops, I have often said that I prefer the term misorientation because disorientation is what we tend to feel at the end of the day of lectures. I give Professor Marc De Graef credit for helping me finally get these terms straight. So, now I can retire that joke that probably never really translated very well into different languages anyway.

One more note on terminology. A grain boundary is a five-parameter entity: three for the misorientation and two to describe the orientation of the boundary plane.

Figure 2. 5D Grain Boundary Character.

This five-dimensional entity is now often referred to as the Grain Boundary Character (Rohrer) but has also been termed the Intercrystalline Interface Structure (Adams). In the past and in OIM Analysis, the Grain Boundary Character Distribution or GBCD refers to the distribution of grain boundaries across three classifications, low-angle random boundaries, high-angle random boundaries, and “special” (generally CSL) boundaries. As a side note, Grain Boundary Character has been called a “full” or “complete” description of a grain boundary, but this is a bit of an overreach. There are still other parameters associated with a grain boundary that may be just as important as these five, for example, curvature, faceting, chemical composition.

It should be noted that we can calculate the misorientation between two crystals of different symmetry and get a nice, neat axis-angle pair.

However, the concept of coincidence is not as clear as for two crystals of the same symmetry, as illustrated in the schematic shown in Figure 3. Nonetheless, this terminology (and its corresponding mathematical methods) can be helpful when analyzing the orientation relationships associated with phase transformations.

Figure 3. Misorientation between a hexagonal and cubic crystal.

I hope this brief discussion has helped “orient” you in the right direction. I know I am now trying to be more careful in using these terms, which will probably result in a few changes in our user interface for a future version of OIM to reflect this.


Wright, SI (2006) Random thoughts on non-random misorientation distributions. Materials Science and Technology 22: 1287-1296.

Bunge, HJ (1969) Mathematische Methoden der Texturanalyse. Akademie-Verlag: Berlin.

Beladi H, Nuhfer NT, and Rohrer GS (2014) The five-parameter grain boundary character and energy distributions of a fully austenitic high-manganese steel using three dimensional data. Acta Materialia 70:281-289

Zhao J, Koontz JS, and Adams BL, 1988. Intercrystalline structure distribution in alloy 304 stainless steel. Metallurgical Transactions A, 19:1179-1185.

Late Night(s) with EBSD

Matt Nowell, EBSD Product Manager, EDAX

While it is easy to imagine late nights running EBSD scans, and I have pulled a few all-nighters over the years for special projects, the improvements in acquisition speeds and software have mostly eliminated the need for this for me. The title of this blog references back to my time in college when one of my favorite shows was Late Night with David Letterman. One of my favorite parts of this program was the famous and funny top ten lists, which I was thinking about while working on a more recent late-night project.

As a product manager, I am always trying to understand the EBSD market, as well as predict and prepare for what is coming next. One method I use is to analyze the keywords within EBSD publications. There has been an exponential increase in the number of EBSD papers each year. Figure 1 shows the distribution using EBSD as a keyword for searching ScienceDirect.

Figure 1. EBSD publications by year.

Using my modest programming skills, I extracted the keywords from the abstracts from these papers. I then performed a frequency analysis of these terms and classified them into material, application, and topic categories. I also had to identify synonyms. For example, additive manufacturing and 3D printing should be counted together. When completed, I was able to compile the top 10 lists for my different categories. In some cases, the results were what I expected, but there were some surprises. Figure 2 shows a word cloud, where the size of the word is proportional to the frequency of occurrence.

Figure 2. Word cloud based on the frequency of EBSD keywords.

During this process, I found many interesting papers I wanted to read. With 4,500+ publications in 2020, I know I cannot read them all. Even with the ones I did read, I found myself missing presentations. With the shift towards virtual conferences due to the pandemic, I can listen to talks at the recent ICOTOM and TMS meetings. When I read a paper, I bring my perspective, but when I hear a presentation, I understand the presenter’s perspective, and they know more about this material than I do. EDAX hosts several webinars each year, and recently we invited customers to present their research and results. A couple of weeks ago, Dr. Eric Payton from the Air Force Research Laboratory gave a webinar that tied making a toaster to President Herbert Hoover. then to robotic vacuum cleaners, and finally to artisanal alloys. It was a very interesting and engaging presentation and can be viewed on-demand from the EDAX website. I also look forward to the next webinar on May 27th when Dr. David Rowenhorst from the Naval Research Lab will present on 3D EBSD to investigate the microstructures of additively manufactured materials.

I also use social media to track trends for EBSD. Twitter has an active scientific community, and I follow many scientists who share their research online. This has led to opportunities to meet many of these people in person over the years. Coming from the commercial side, I will admit that I sometimes feel like an outsider, and I am a little hesitant at times to chime in on a discussion. I often find interesting work and share tweets with my colleagues. I recently found an article featuring EBSD maps from additively manufactured materials (yes, additive is pretty large in the word cloud) on Gizmodo ( I shared this on social media platforms, both professionally and personally. I even reached out to Jake Benzing at NIST to compliment the wonderful results. I really enjoy being able to connect with our users, and see what they do with our tools to further their work.

Minimum Detection Limit and Silicon Nitride Window

Dr. Shangshang Mu, Applications Engineer, EDAX

A couple of weeks ago, a question regarding the minimum detection limits (MDL) of our Energy Dispersive Spectroscopy (EDS) quantitative analysis was forwarded to me from a potential customer. This is a frequently asked question I get from customers during EDS training. We understand researchers are looking for a simple answer; however, they don’t get a straightforward answer from us most of the time. This is not because we don’t want to tell the customer the configurations of our systems, but detection limits depend on various factors, including detector window, geometry, detector resolution, collection time, count rate, and sample composition. The detection limit for a given amount of an element in different sample matrixes is not the same. For example, calcium in indium has a much higher detection limit than it has in carbon because calcium energy lines are heavily absorbed by indium, but not by carbon. The limit also changes if you have a bit more of a given compound in the sample. The limits are lower if the data collection time is doubled. So, it is impossible to provide a general MDL for an EDS system or even a given element, but we can calculate the MDL for a given spectrum.

This function is available in APEX™ Software for EDS version 2.0 or later. For each element identified in the spectrum, the MDL is given in the quantification table and flagged if it is below the detection limit (Figure 1). To determine the MDL for a given spectrum, one must look at the statistical significance of the signal above the background. We generally use the single-channel definition for peak and background counts.

Figure 1. Quantification table with MDL.

Figure 2. Illustration of background and peak counts.

For a given element to be above the significance level, it requires that the total number of counts on the peak NP be above background counts NB by a predetermined confidence, see Figure 2. For significance, we use 1.7 standard deviations (SD) in a one-tail test since we are only concerned about having counts above the threshold (Figure 3). A SD of 1.7 corresponds to about 95% confidence for a single-tail.

Figure 3. Single-tail normal distribution. NB is the background mean level.

The significance level can be calculated as:

NS=NB+1.7σB= NB+1.7√(NB)

This means that the requirement for an element to be considered significant is:

NP≥ NB+1.7√(NB)

For the MDL calculation, we are considering the net counts on the peak (NP-NB). Analog to the significance level, it is required that the counts are above the background plus a significance level, but we are now considering net counts instead of gross counts.


To calculate the error, we consider the error of the peak and the background. If an element is close to the detection limit, the number of counts are comparable to the background counts, and we can approximate the total error:


Using a 2s/95% confidence level, we can write the count detection limit as:


With the count-based detection limit and assuming the counts are linear with concentration, the concentration MDL can be calculated from the concentration C of a given element in a spectrum:


As I mentioned earlier, the detector window is one of the most important factors determining the MDL. With the introduction of Silicon Drift Detectors (SDD) and the development of fast and low-noise pulse processors, EDS analysis has seen remarkable increases in throughput and reliability in the last decade. But one often overlooked aspect of the detection technology is the detector window. A variety of window technologies are available, including beryllium, polymer films, and the most recent addition by EDAX, silicon nitride. Due to the polymer window’s composition and thickness, a significant part of the low-energy X-rays is absorbed before reaching the X-ray detector. This absorption effect is vastly reduced in the range below 2 keV for the silicon nitride windows, as shown in Figure 4.

Figure 4. Transmission curves for silicon nitride and polymer windows measured using synchrotron radiation.

The MDL for spectra acquired from the same samples with different window configurations can be calculated by employing the derived equation above. This study was led by Dr. Jens Rafaelsen at EDAX using five different standards. To eliminate the detector resolution and response as a variable in the experiment, the window was removed from a standard detector, and exchangeable caps with silicon nitride and polymer windows were mounted in front of the electron trap. Figures 5 and 6 show the relative improvement in MDL for the window-less and silicon nitride window configurations compared to the polymer window. Figure 6 documents the silicon nitride’s superiority over the polymer window in the low energy range with improvements of over 10% for the MDL of oxygen. While Figure 5 shows that further improvements can be gained in the window-less configurations, the silicon nitride window still allows for the use of variable pressure mode and spectrum collection from samples exhibiting cathodoluminescence (CL).

On a side note, our friends at Gatan recently captured fantastic EDS and CL data simultaneously from a meteorite thin-section using an EDAX Octane Elite EDS Detector and a Gatan Monarc CL Detector mounted on the same SEM. Check out the blog post written by Dr. Jonathan Lee to see how combined EDS and CL analysis can provide a glimpse into the history of our solar system’s evolution.

Figure 5. Relative gain in MDL for window-less configuration compared to polymer window.

Figure 6. Relative gain in MDL for silicon nitride configuration compared to polymer window.

Microanalysis That’s Out of This World!

Dr. Jonathan Lee, Application Scientist, Gatan

Working as a cathodoluminescence (CL) application scientist at Gatan, I observe a great variety of interesting specimens from semiconductor devices, plastics, and geological samples to novel nanoscale optical devices demonstrating the capabilities of the Monarc® Pro CL detector. In case you don’t know, CL is the visible, ultraviolet, and infrared light emitted by many specimens in the scanning electron microscope (SEM). Recently, I was contacted regarding a meteorite sample and asked what analysis I could demonstrate using CL. As a physicist and amateur astronomer, I was naturally very excited at the rare opportunity to analyze something that literally came from out of this world! You might say I was… over the moon 🌙!

The sample is a thin-section from a meteorite collected from Antarctica – Miller Range 090010, you can read more about the classification here: Meteoritical Bulletin: Entry for Miller Range 090010 ( Likely to have been a constituent of the asteroid belt, our specimen had a trajectory that eventually led it to fall to Earth. The study of these meteorites allows us to understand more about the age and history of our solar system. Given the origins and unusual conditions experienced by meteorites, the microstructure can be incredibly complex, but often, chondritic meteorites like this one contain calcium aluminum inclusions (CAIs) and corundum grains which are among the first solids to condense from the solar nebula! Now, before I get wrapped up with the Cosmic Calendar, let’s take a look at our specimen!

Figure 1. Image overlay from a CAI region of meteorite specimen (gray) secondary electron and (green) unfiltered CL.

CL revealed so much new information, and this was an exciting first result! For geological specimens, unfiltered CL images can be very useful to reveal mineral texture, but the real nitty-gritty information is found in the spectrum. So many of the grains showed such strong luminescence that I was eager to learn more.

Our friends at EDAX recently installed an Octane Elite Energy Dispersive Spectroscopy (EDS) Detector on the same SEM as the Monarc. EDS and CL are fantastically complementary techniques for sample analysis. EDS is great for elemental quantification but falls short when trying to identify trace elements, crystallographic phases, or grain boundaries – where CL shines! Equipped with these powerful tools, I collected my first multi-hyperspectral data, capturing CL and EDS signals simultaneously. Take a look at some of the results:

Figure 2. (left) True color representation of the CL spectrum image (color) overlaid with SE image (gray), and (right) extracted CL spectra from points 1 (aqua fill), 2 (red), and 3 (green).

Figure 3. (left) Elemental quantity maps extracted from the EDS spectrum image corresponding to aluminum (blue), calcium (green), and magnesium (red); and (right) extracted EDS spectra from points 1 (aqua fill), 2 (red), and 3 (green). Points 1, 2, and 3 are the same locations as in Figure 2.

Both techniques were very revealing. In addition to Mg, Ca, and Al, the EDS spectrum image (hyperspectral image) detected other elements, some in high abundance like O and Si, and others which were less abundant, including Fe, C, Ti, and Na. We discovered geological materials like hibonite, corundum, and apatite but could not discern which mineral complexes they were involved in. At first glance, the CL and EDS maps looked very similar, but the more I looked, the more I realized there were significant differences, and so I decided to dig a little deeper with the CL spectrum image. The CL spectrum shown in Figure 2 indicates the presence of several trace elements. By looking at the difference of intensities at the smaller sharp peaks in contrast with the surrounding intensities, I was able to differentiate two maps from the CL data, which likely correspond to the presence of trace elements, one with an emission peak at 460 nm (Fe in corundum) and the other at 605 nm (Sm in apatite).

Figure 4. Extraction of CL trace elements (Fe in corundum) found at 460 nm (red) and (Sm in apatite) 605 nm (green).

Figure 5. (left) Bandpass CL image displaying 580 ± 20 nm and (right) colorized EDS map for Al (blue), Ca (green), and Mg (red).

Figure 6. EDS and CL composite image including EDS elemental maps for aluminum(blue) and magnesium (yellow); and trace elements iron in corundum (green) and samarium in apatite (red) as revealed by CL.

The data gathered from this sample may give a glimpse into the history of our solar system’s evolution. It also demonstrates the need for complementary techniques when analyzing complex samples. I want to thank NASA for generously providing the sample used in this study, Gatan and EDAX for providing me the opportunity to work with it, and the nature of the universe for generating this message in a bottle and letting it find its way to our lab!