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?

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

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.

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

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.

A plan view SEM image of the structure directly below the colored surface together with EDS maps showing the C, O, and Ca distribution.

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.

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.

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.

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).

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.

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.

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.

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.

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

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

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

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!

Disoriented

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.

Axis-angle description of misorientation.

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〉…).

AngleAxisAngleAxis
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.

5D Grain Boundary Character.

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.

Picture4

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.

Misorientation between a hexagonal and cubic crystal.

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.

References

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.

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.

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 (https://gizmodo.com/these-microscopic-maps-of-3d-printed-metals-look-like-a-1846669930). 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 1. Quantification table with MDL.

Figure 2. Illustration of background and peak counts.

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.

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.

NDL=NB+∆(NP-NB)

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:

∆(NP-NB)=√(NP+NB)≈√(2NB)

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

NDL=NB+2√(2NB)~2.8√(NB)

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:

MDL=2.8√(NB)*C/NP

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.

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 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.

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 (usra.edu). 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!

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

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:

(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 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).

(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.

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).

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

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

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

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

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.

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!

Boxes

Dr. Stuart Wright, Senior Scientist, EDAX

It has been a tough year for all of us – at times, I get cabin fever and feel boxed-in. The recent holiday break was a pleasant diversion. Even though we weren’t able to gather like we usually do, we did get to spend some time with a couple of our grandkids. As we opened gifts, per the usual stereotype, our youngest grandson had more fun playing with the boxes than the toys in them! Since today’s blog is on boxes, Figure 1 shows a picture of our granddaughter atop an old toy box. Yes, she is more than willing to pose for the camera.

My granddaughter atop a toy box I built many years ago.

Figure 1. My granddaughter atop a toy box I built many years ago.

So why the picture of a toy box? That toy box is 32 years old and has a tie-in to the development of EBSD (since I am getting older, I’m allowed to be a bit nostalgic.)

I joined Professor Brent Adams’ group as an undergrad at BYU in 1985. Brent was working on the orientation coherence function (OCF) at the time, which is a statistical description of crystallographic orientation arrangement within a polycrystalline microstructure. One of the Ph.D. students, T. T. Wang, went off to what was then the Alcoa Technical Center to make orientation measurements using selected area diffraction – a painstakingly slow process. He returned with a large set of Euler angles and a box of micrographs with numbered spots to indicate where the orientation measurements were from. My assignment was to digitize those micrographs – both to manually point-and-click each grain vertex and to write software to use those vertices to reconstruct and visualize the digital microstructure. Figure 2 shows one example from the set of 9 section planes. The entire set contained 5,439 grains and 21,221 boundaries. It was a lot of tedious work.

Digitized microstructure from aluminum tubing for work reported in B. L. Adams, P. R. Morris, T. T. Wang, K. S. Willden and S. I. Wright (1987). “Description of orientation coherence in polycrystalline materials.” Acta Metallurgica 35: 2935-2946.

Figure 2. Digitized microstructure from aluminum tubing for work reported in B. L. Adams, P. R. Morris, T. T. Wang, K. S. Willden and S. I. Wright (1987). “Description of orientation coherence in polycrystalline materials.” Acta Metallurgica 35: 2935-2946.

When Brent saw David Dingley’s presentation on EBSD at ICOTOM in 1987, he got very excited as he realized how much it could help with our group’s data collection needs. We got the first EBSD system in the US shortly after ICOTOM. It was installed on an old SEM in the botany department. The system was all computer-controlled, but it still required a user to manually (with the mouse) identify zone axes in each EBSD pattern to be indexed. It was a huge step forward for our research group. Brent quickly envisioned a fully automated system for site-specific orientation measurements. In 1988, Professor Adams moved to Yale University. I was fortunate to be invited to be a member of the research team that accompanied him. My wife and I boxed up our belongings and moved our small family of four from Utah to Connecticut for our new adventure.

The first few weeks at Yale were spent cleaning out an old laboratory space (some items even went to the Yale museum) in preparation for receiving our new CamScan SEM and the next generation EBSD system from David Dingley. When the SEM boxes arrived at the lab, we were very excited to see the microscope uncrated and installed. It was great to have our own microscope to work with, and we waited in eager anticipation for David’s arrival to install the new EBSD system. Unfortunately, I don’t have many photos from the Yale lab, but Figure 3 shows one with three of my colleagues in front of the SEM.

Brent Adams, John Hack, and Karsten Kunze in the SEM lab at Yale.

Figure 3. Brent Adams, John Hack, and Karsten Kunze in the SEM lab at Yale.

After everything was installed, there were a lot of wooden boards left over from all the crates in which the equipment was shipped. Being the stereotypical poor-starving student, I saw the wood as an opportunity. I diverted the bigger pieces of wood to my car instead of the dumpster and took them to our apartment. It was enough wood to build a toy box for each of our two kids (Figure 4).

Building a toy box with my kids while at Yale.

Figure 4. Building a toy box with my kids while at Yale.

A picture of the SEM at BYU (Brent returned to BYU after I graduated in 1992 and brought the system with him back to BYU) can be seen in Figure 5. Note all the boxes surrounding the instrument. In the very first system, instead of controlling the SEM beam, we moved the sample under a stationary beam using piezoelectric stages. In this photo, the camera was fixed so that it was always inserted into the microscope chamber, so there wasn’t a box to control the slide yet. Eventually, the stages were replaced with beam control, the SEM image could be viewed live on the workstation monitor, the camera was controlled through the computer, the image processing was done in the computer, the camera slide was controlled in software until we reached the modern, streamlined systems we are accustomed to today.

Photo of the first fully automated EBSD system in a lab at BYU (originally at Yale but later moved to BYU).

Figure 5. Photo of the first fully automated EBSD system in a lab at BYU (originally at Yale but later moved to BYU).

The old SEM was scrapped several years ago, but the two toy boxes are still in use and filled with “stuffies” as my granddaughter likes to say. So, just like the presents under the Christmas tree, the SEM boxes are still providing entertainment long after the toys they once held have been recycled into new ones 😊.

Improve the indexing rate – EDAX’s optimized EBSD solution

Dr. Sophie Yan, Applications Engineer, EDAX

As an applications specialist, I have encountered various problems over the years. There is always a common goal among EBSD users—to improve the EBSD indexing rate. Even a user who mainly tests relatively easy steel samples may run into deformed samples and intergranular precipitates that are difficult to calibrate, so they still need to improve the indexing rate. Ideally, we want to get a beautiful EBSD IPF map like Figure 1; however, the reality is that we often fail to get a map with such a high indexing rate.

IPF map with a very high indexing rate.

Figure 1. IPF map with a very high indexing rate.

Recently, I received a phone call from a customer asking for help. She had tricky ceramic samples with low crystallinity and fine grains, which are hard to index. The indexing success rate was only 5.48% from the area she tried to analyze (Figure 2). She wanted to see if we could improve it.

Ceramic sample with an indexing success rate of 5.48%.

Figure 2. Ceramic sample with an indexing success rate of 5.48%.

Of course, we can.

EDAX has a set of solutions to improve the indexing rate, as shown in Figure 3. If I had a direct detector like the Clarity™ EBSD Analysis System, I would obviously get better results. However, I only have a CMOS-based Velocity™ Super in my lab.

During the data collection process, users can optimize different parameters, such as background processing, or Hough parameters, to fit real-world samples. Combined with a unique hexagonal grid sampling and triplet indexing solution, EDAX gets a better indexing rate, which is very important for challenging samples.

If the data is not ideal, we can process the result using NPAR™. With NPAR, it averages the patterns to improve the indexing rate of challenging samples considerably. Also, in OIM Analysis™ v8.0 or higher, a module is available that can perform background processing again on the saved patterns to improve the indexing rate further.

EDAX’s optimized EBSD solution.

Figure 3. EDAX’s optimized EBSD solution.

I analyzed the sample and saved the patterns. Then I used OIM Analysis to post-process the patterns, as shown in Figure 4. The original pattern is quite fuzzy, and the bands were not clear. After NPAR processing, it improves the signal-to-noise ratio of the pattern, and the bands became clearer after further background processing.

(a) The raw pattern, (b)NPAR, (c) NPAR+dataset background, and  (d) NPAR+dataset background+dynamic background.

Figure 4. (a) The raw pattern, (b)NPAR, (c) NPAR+dataset background, and (d) NPAR+dataset background+dynamic background.

Of course, the processed patterns have indexing success rates. Figure 5 shows the IPF map of the data after a series of post-processing steps were taken, as described in Figure 4. The indexing success rate improved to 24.1%.

An IPF map with an indexing success rate of 24.1%.

Figure 5. An IPF map with an indexing success rate of 24.1%.

For this user’s case, the indexing success rate was greatly improved and was within an acceptable range. But to achieve our goal of improving the indexing rate of challenging samples, there is much more that needs to be done.

The above indexing success rates were achieved after CI >0.1 filtering. For those points with a CI <0.1 (the black areas in the IPF map), we can further process them. EDAX recently added OIM Matrix™, which includes dictionary indexing as a supplementary solution. As we all know, the result of dictionary indexing is usually better. I would expect a higher indexing success rate on the customer sample if I could use dictionary indexing to process it further.

If we push the limit, we can use the Clarity Direct Electron System to test this sample. In fact, the super-sensitive, low-beam current requirement is ideal for testing this type of sample. Maybe we can expect a better result with Clarity?

Will the result improve with Clarity?

Figure 6. Will the result improve with Clarity?

The goal of improving the indexing rate can be summed up in one sentence from a Chinese poem published in roughly 300 BC: The journey is long, but I will search up and down.

提高标定率——EDAX的EBSD解决方案

Dr. Sophie Yan, Applications Engineer, EDAX

作为应用,这些年,林林总总,我碰到各种问题。但是不能否认,对于EBSD用户来说,有一个共同的追求:提高EBSD的标定率。很少有用户没有这类困扰,即便用户主要测试的是相对容易的钢铁样品,可能也会有变形试样以及难以标定的晶间析出相;理想情况,当然是得到图1那样漂亮的EBSD图;但是受限于现实情况,我们往往不能如愿。

IPF map with a very high indexing rate.

图1:IPF图,标定率极高

最近我接到一个客户的求助电话,她有些很难的陶瓷样品,结晶程度不高,晶粒小,标定率很低。她尝试了一个区域,标定成功率只有5.48%;如下图2。她想看看有没有办法提高。

Ceramic sample with an indexing success rate of 5.48%.

图2:陶瓷样品,标定成功率5.48%

当然我们是有办法的。

EDAX对于提高标定率有一整套解决方案,如图3所示。从硬件选择开始,比如选择最新款直接电子检测相机Clarity,那这个样品明显会有更好的结果出现; 不过我的实验室只有CMOS相机Velocity Super……

采集过程,EDAX尽可能的开放了采集过程中的参数设置,如背底处理及Hough的参数,结合特有的六方步进处理及三条带组算法,使我们在实际操作过程中,参数更加贴近实际的样品情况,对于有挑战性的样品非常重要。

如果采集的数据结果不理想,我们可以对结果进行预处理。最有代表性的当属NPAR处理,由于对花样进行平均,能极大的提高挑战性样品的标定率。此外,在OIM8.0之后,OIM Ananlysis加入了新的模块,对保存后的花样能再次进行背底处理,进一步提升标定率。

EDAX的优化EBSD解决方案

图3:EDAX的优化EBSD解决方案

我对这个样品进行了EBSD分析,保存了花样。然后用OIMA对花样进行了预处理,见下图4.原始花样相当模糊,条带不太清楚。NPAR处理之后花样信噪比提升,条带变得较为清晰。再进一步进行背底处理,条带清晰程度进一步提升。

(a) The raw pattern, (b)NPAR, (c) NPAR+dataset background, and (d) NPAR+dataset background+dynamic background.

图4(a)原始花样 (b)NPAR (c) NPAR+dataset bkg (d) NPAR+dataset bkg+dyn bkg

使用这样处理过的花样进行标定,结果当然会有所不同。下图5是我进行图4中一系列后处理后标定的IPF图。标定成功率24.1%。

An IPF map with an indexing success rate of 24.1%.

图5,IPF图,标定成功率24.1%

对这个用户,整件事情告一段落:标定成功率有了大的提升,这个结果已经在可接受的范围。但是对于我们致力于提升挑战性样品标定率的目标,其实这件事还大有可为。

以上的标定成功率都是CI>0.1过滤之后的数值。对于CI<0.1的那些点,亦即IPF图中那些黑色的区域,其实我们可以对它进一步处理:EDAX近期引入了词典算法辅助标定,作为标定算法的一部分。众所周知,词典算法标定率极高,如果能用词典算法进一步标定,相信会有更好的结果。我期待着词典算法能给这个样品更高的标定率。

如果将手段用到极致——如果有可能的话,用EDAX全新发布的探测器Clarity测试这个样品,这个超灵敏,低束流的要求其实很适合测试这一类样品——可能结果也非常值得期待?

这样看来,提升标定率这一目标,也好像适用于那一句话: 路漫漫其修远兮,吾将上下而求索?

Will the result improve with Clarity?

图6.清晰度会改善结果吗?

Thanksgiving

Dave Durham, Western Region Sales Manager, EDAX/Gatan

We are quickly approaching that special season where we are encouraged to momentarily put aside our busy schedules and take an inventory of the things in our lives that we may not have had a chance to appreciate throughout the year. Considering the pandemic we’ve all been experiencing during the majority of 2020, I think it is especially important to stay optimistic and find the positive things that have materialized during this challenging and weird year.

Professionally, as a salesperson for the company, I am undoubtedly very thankful for the fact that the team at EDAX has had the resolve to release several new and compelling products this year. Amazing! Even considering the challenges of 2020, there has been a steady stream of recent upgrades and technology that have allowed us to provide our customers with groundbreaking tools to make their work and research even more successful. All this during a period where, I would have thought, very little innovation would be introduced in the field.

First was the release of APEX™ 2.0 for Energy Dispersive Spectroscopy (EDS) and Electron Backscatter Diffraction (EBSD). This was a substantial upgrade to the APEX Software interface, integrating it with our EBSD product line, allowing our customers to analyze their sample’s compositional and structural characteristics, and implementing a handful of other critical improvements to the capabilities and functionality of the platform.

APEX Software user interface.

Figure 1. APEX Software user interface.

Then we announced the launch of the new Lambda™ WDS product line. These spectrometers utilize a proprietary X-ray optical module to give them much better sensitivity at low energies and extend the energy limit beyond 15 keV, giving them superior performance in compositional analysis within WDS applications.

Lambda WDS Analysis System.

Figure 2. Lambda WDS Analysis System.

We followed that up with another huge announcement – the release of our Clarity™ EBSD Analysis System. The Clarity is the world’s first commercially available direct detector of its type designed for EBSD, ideal for operating at low currents and low voltages, where typical phosphor-based EBSD technology is unable to collect usable EBSD patterns. This detector truly opens a new window into sample types and applications that have never been possible with EBSD analysis. Very impressive!

Clarity EBSD Analysis System.

Figure 3. Clarity EBSD Analysis System.

Lastly, we released OIM Analysis™ v8.5, an improved version of our renowned post-processing analysis software for EBSD. This new revision added compatibility with APEX 2.0 and support for OIM Matrix™, for dynamic pattern simulation and dictionary indexing, as well as a few significant upgrades to user functionality and ease-of-use.

Schematic of the dictionary indexing processes in OIM Matrix using a library of simulated patterns.

Figure 4. Schematic of the dictionary indexing processes in OIM Matrix using a library of simulated patterns.

I want to give my sincere thanks to all the folks at EDAX who played a part in bringing each of these products to fruition in 2020. I appreciate the hard work you put in this year, in addition to the multiple years it takes to bring new products to market. I’m thankful that you’ve made my job easier as a salesperson, helping me keep customers excited and engaged with new products. And you’ve also played a significant part in advancing our customer’s research and productivity.

On a side note, I’d be remiss if I didn’t also say that I was thankful for the new sample preparation instruments, the Ilion II and PECS II, added to our product portfolio the AMETEK acquisition of Gatan this year. While the instruments themselves were not released in 2020, they are “new” to me, and I am very excited to introduce them to our customers moving forward. I believe they will allow the EBSD community to spend significantly less time preparing their samples for analysis while providing substantially better patterns than what they’re used to seeing through typical sample preparation techniques. We recently released an experiment brief on the subject.

(left) Gatan Ilion II System and (right) Gatan PECS II System.

Figure 5. (left) Gatan Ilion II System and (right) Gatan PECS II System.

Finally, I’m thankful for my health – I’ve lost about 15 pounds this year and feel like I’m in the best shape I’ve been in two decades. I’m also very thankful for my family, kids, and friends, whom I love and have loved me and supported me through all of 2020’s ups and downs. When I think of everything that has been going on in the world and how there are still so many good things going on in my life, considering all of the things that could have taken a turn for the worst, I’m thankful for that too. And all of that makes me enthusiastic and hopeful for a better year in 2021.

What are you thankful for?

Dave Durham and his three children.

Figure 6. Dave Durham and his three children.

Do Vintage Toy Cars Contain Lead?

Dr. Shangshang Mu, Applications Engineer, EDAX

Collecting die-cast toy cars is a childhood hobby that I picked up again twelve years ago. As kids play with Hot Wheels in the United States, you are sure to remember Matchbox toy cars if you were a kid in the 1980s and 1990s in China, like me. The brand originated in the United Kingdom and was given its name because the original die-cast toy cars were sold in boxes similar to those in which matches came in. I stepped into this mini world at the age of four when my father bought me my first Matchbox toy car. During my adolescence, I enjoyed exploring my gradually growing collection. Many years later, when I was in graduate school, these toy cars captured my attention again while I was shopping for groceries. I ran into a small section with some Hot Wheels and Matchbox cars hanging on the pegs. I was so excited to see that my favorite childhood toy brand was still alive and immediately reconnected with my old hobby.

Besides collecting toy cars released in the current year, I started to search on the internet to re-collect the same un-opened models that became worn and even destroyed in my childhood. Soon, I expanded my collection to include toy cars made in the 1970s and even 1960s and started to collect detailed scale model cars that are about the same size. Although collecting Matchbox or Hot Wheels cars is a hobby that attracts a lot of adult fans around the world, these cars are toys that do not have small parts, and all the vehicle types are about three inches in length, regardless if it is a passenger car or a truck (Figure 1). On the other hand, matchbox-sized detailed model cars are classified as 1/64 the size of the actual automobile, with many small parts that are only suitable for ages fourteen and up. 1/64 scale models bring back memories in another way because I am collecting models of classic cars and trucks from the era in which I grew up. Figure 2 shows some impressive cars from my childhood and a fire engine from my neighborhood in Boston.

A vintage railway playset from 1979 that my daughter likes to play with, and some toy cars ranging from the 1970s to 2010s.

Figure 1. A vintage railway playset from 1979 that my daughter likes to play with, and some toy cars ranging from the 1970s to 2010s.

Some matchbox-sized detailed models (1/64 scale) of the cars and trucks that I grew up with.

Figure 2. Some matchbox-sized detailed models (1/64 scale) of the cars and trucks that I grew up with.

Sometimes my five-year-old daughter rolls my toy cars on racetracks to figure out which one is the fastest. She also likes playing with my vintage railway playset. As a parent, my daughter’s interest made me a little concerned about lead paint since some of the toy cars she plays with were manufactured decades ago. For example, the railway playset dates back to 1979. Safety standards have been changed and revised over time, so I decided to figure out if these toys are lead-free. As an Applications Engineer at EDAX, I had more than one choice of material characterization technique. The Orbis Micro-XRF Analyzer can do non-destructive elemental analysis with the flexibility to work across a wide range of sample types and shapes, meaning I could put the toy cars directly into the analyzer to get the results. At that time, I was in the middle of testing new features in our new APEX™ 2.0 Software for EDS, so I decided to go with Energy Dispersive Spectroscopy (EDS) to give the new Batch Mode feature a try. With the benefits of EDS analysis and the Batch Mode feature in the APEX 2.0 Software, I was able to load all the paint samples into the SEM chamber and run them all at once using an Octane Elite Silicon Drift Detector. I scratched a tiny paint chip from each toy car and stuck it on a 25 mm adhesive carbon tab. Overall, I got 28 samples to analyze, ranging from the 1960s to the 2010s. They were mostly Matchbox, including the cars my daughter plays with, but some were also from other major toy car brands sold in the United States (Figure 3).

A 25 mm adhesive carbon tab with paint samples from my toy cars

Figure 3. A 25 mm adhesive carbon tab with paint samples from my toy cars

The Batch Mode operation allows you to collect data sets at different stage positions as a batch operation. Since the paint samples were hand stuck on the tab, the distance between adjacent samples was relatively large, and a single field of view was only able to show one sample. The Batch Mode feature’s automated stage movement was extremely useful in covering the paint samples all over the carbon tab in one operation batch. I was able to store all the paint samples in a batch list, set up collection parameters (Figure 4), and click on the Collect button to wait for all the samples’ results. Fortunately, the results show that all the samples I analyzed do not contain lead. The identified characteristic peaks were correlated to the paint samples’ colors; titanium dioxide and zinc oxide were white, carbon was black, and sulfur-containing sodium silicate was blue (Figure 5).

The growing batch list of the paint samples.

Figure 4. The growing batch list of the paint samples.

Selected SEM images and spectra overlay of the paint samples. The arrow indicates that no Pb L peak (10.55 keV) is present.

Figure 5. Selected SEM images and spectra overlay of the paint samples. The arrow indicates that no Pb L peak (10.55 keV) is present.

On a side note, it was relatively easy to identify a single element from a bunch of spectra that the energy region around the lead peak was pretty clean without any overlapping peaks. I simply had to overlay all the spectra together and see if the lead peak stuck up from the background. If you need to identify multiple compounds of contaminants from various samples, examining every spectrum or doing quantification analysis and comparing how close these numbers are over and over again is very time-consuming. An easy solution is to use the Spectrum Matching feature provided by the APEX 2.0 Software. You can collect spectra from those contaminants to build a library for them first, and then you can run Spectrum Matching to compare the unknown samples to the library. If Spectrum Matching finds more than three matches for an unknown sample, it will display the top three matches with numerical values of fit% for each unknown sample. This feature provides a remarkable benefit in improving the efficiency of your experimental work.

Now, I can stop worrying about the toxic component and let my daughter play with the vintage toy cars as she likes. My only concern is that some are hard to find now, so be careful and don’t break my vintage toy cars!