“Don’t Sweat the Small Stuff” vs. “It’s the Little Things that Matter Most”

Dr. Stuart Wright, Senior Scientist, EDAX

A few weeks ago, my colleague at EDAX, Shawn Wallace, posed a question that has stayed with me, and so I thought my turn at the EDAX blog would be a good place to address it. Shawn was building an EBSD structure file for a new phase and encountered the following dialog for adding an atom to the unit cell.

Dialog box for building a new structure file for a new phase.

Shawn asked how important it was to get the Ion Type correct for the structure he was working with. I realized I had implemented this capability several years ago for kinematical calculations of structure factors but had never really explored it’s impact on the calculations. I guessed that it would not have much of an impact, but I wasn’t entirely sure that was the case. The choice of ion type affects the atomic scattering factor used in structure factor calculations. I looked through our phase structure database for a binary compound containing Fe and decided to use a simple Al-Fe structure to check out the effect of the ion type selection on the structure factor calculation.

Structure factors for the Fe and Fe+3 selections in the dialog box.

I calculated structure factors for the Fe and Fe+3 selections in the dialog box above. As shown, the difference in the structure calculation results is imperceptible in the kinematically simulated patterns. The maximum difference between the two patterns is only a 1.6% difference in the relative intensity of the {100} bands.

Kinematically simulated patterns for Fe and Fe+3.

Here is a table showing that the structure factors are quite similar, confirming my initial guess. I haven’t tried any other structures, so it is not a complete study, but I suspect other structures will follow the trend shown by the simple Al-Fe structure. Thus, my conclusion is, Don’t Sweat the Small Stuff.

(hkl) FFe FFe+3
110 4.773 4.826
100 1.291 1.738
200 3.256 3.252
211 2.566 2.562
111 1.098 1.104
220 2.189 2.186
222 1.633 1.632
210 0.913 0.908
310 0.858 1.856
321 1.462 1.461

With that little study wrapped up, I turned my attention to choosing the Debye-Waller factor used in the dynamical simulation. In the dialog above, it says the default Debye-Waller factor for iron is “0.003106 for bcc, 0.533 for fcc”. Does the choice of Debye-Waller factor matter? Here are dynamically simulated patterns for these values.

Dynamically simulated patterns using the Debye Wall factor.

The two patterns are quite different. To correctly use the new simulation tools, I need to expend some effort to learn more about Debye-Waller factors. Clearly, It’s the Little Things that Matter Most.

Want a Free Set of Microanalysis Standards?

Dr. Shangshang Mu, Applications Engineer, EDAX

Modern EDS systems are capable of quantitative analysis with or without standards. Unlike standard-less analysis, the k-ratio is either calculated in the software or based on internal standards. For analysis with standards, it is measured from a reference sample with known composition under the same conditions as the unknown sample. As an applications engineer, sometimes users ask me where to order these standards. Usually, I point them to the vendors that manufacture and distribute reference standards where you can order either off-the-shelf or customized standard blocks. In addition to these commercial mounts, I always tell them that they can request a set of mineral, glass, and rare earth element phosphate standards from the National Museum of Natural History free of charge! These are very useful standards that I’ve seen widely used in not only the geoscience world but also in various manufacturing industries. These free standards are also great for those graduate students with limited budgets and ideal for practicing sample preparation (yes, I was one of them).

This set of standards is officially called the Smithsonian Microbeam Standards and includes 29 minerals, 12 types of glass, and 16 REE phosphates. You can find out more information about these standards and submit a request form by clicking on the link below:
https://naturalhistory.si.edu/research/mineral-sciences/collections-overview/reference-materials/smithsonian-microbeam-standards

I mentioned sample preparation earlier. Yes, you read that right. These standards come in pill capsules containing from many tiny grains to a few larger ones and you need to mount them on your own (Figure 1).

Grains in a pill capsule.

Figure 1. Grains in a pill capsule.

Since you can get the information such as the composition, locality, and references for each standard from the website, what I want to discuss in this blog post is how to prepare them properly for X-ray analysis. The first tricky thing is to get them out of the capsules. The grains in Figure 1 are almost the largest in this set and you won’t get too many of this size. Some of the grains are even too tiny to be seen at first glance. For the majority that are really tiny, you need to tap the capsule a couple of times to release the grains that get stick to the capsule wall, then you can open the capsule very carefully and let the grains slide out with a little tapping.

For mounting, the easiest way is to mount the standards in epoxy using a mounting cup and let it cure. I did this in a fancy way to make it look like a commercial mount (Figure 2). I ordered a 30 mm diameter circular retainer with 37 holes used by commercial mount manufacturers (Figure 3) and filled the holes with standards on my own. I must admit that the retainer is not cheap, but you can machine the mount by yourself or have a machine shop do it for you. In addition to looking pretty, the retainer ensures a good layout so you can quickly locate the standards you need during microanalysis, and you can mount the same type of standards on one block and get rid of the hassle of frequently venting and pumping the SEM chamber to switch standard blocks.

Examples of commercial mounts.

Figure 2. Examples of commercial mounts.

 

30 mm diameter circular retainer with 37 holes.

Figure 3. 30 mm diameter circular retainer with 37 holes.

To prevent the tiny grains from moving and floating up when pouring the epoxy mix, I placed the retainer upside down and pressed it onto a piece of sticky tape (Figure 4a) and positioned the grains on the sticky surface of the tape within the holes. When tapping the capsule to let the grains slide out and fall into the hole, the other holes were covered to prevent contamination (Figure 4b). These holes are small in diameter and pouring the epoxy mix directly will trap air bubbles in the hole to separate the grains from the epoxy mix. To overcome this problem, I filled up the hole by letting the epoxy mix drip down very slowly along the inner surface of the hole.

Positioning grains within the holes of the retainer.

Figure 4. Positioning grains within the holes of the retainer.

For general grinding, I start with wet 240 grit SiC sandpaper with subsequent use of 320, 400, 600, 800, and 1,200 grit wet SiC sandpapers. But coarser grits can grind off tiny grains in this case, so I would recommend starting with a relatively fine grit based on the sizes of the grains you receive and always use a light microscope or magnifier to check the grinding. For polishing abrasive, I used 1 micron and 0.3 micron alumina suspensions on a polishing cloth. For the grains used as standards or quantification in general, the surface needs to be perfectly flat. However, the napped polishing cloth tends to abrade epoxy and the grains at different rates, creating surface relief and edge rounding, especially on tiny grains. To mitigate this effect, the polishing should be checked under a light microscope constantly and stopped as soon as the scratches are removed. A vibratory final polishing with colloidal silica is optional. Followed by ultrasonic cleaning and carbon coating, the standard mount is ready to use.

Note that commercial mount manufacturers may prepare standards individually (especially for metal standards) and insert them into the holes from the back of the retainer and fasten them with retaining rings (Figure 5a). A benefit of this approach is that the standards on the mount are changeable, so you can load all the standards you need on one mount before microanalysis. I used to make several individual mounted standards that can fit into the retainer (Figure 5b) but this process is very time consuming and much trickier to keep the small surface flat during grinding and polishing.

a) The back of a commercial metal standard mount. b) A tiny cylindrical mount that can fit into the retainer holes.

Figure 5. a) The back of a commercial metal standard mount. b) A tiny cylindrical mount that can fit into the retainer holes.

This is definitely a good set of standards to keep in your lab. With EDAX EDS software, in addition to quantification with these standards, you can also use them to create a library and explore the Spectrum Matching feature. The next time you want to quickly determine the specific type of a mineral, you can simply collect a quick spectrum and click the “Match” button, and the software will compare the unknowns to the library you just created.

Between the Lines

Dr. René de Kloe, Applications Specialist, EDAX

While I am testing new hardware and software versions, I use it as an opportunity to collect some data on unique materials. Testing detector speed or general software functionality is easiest on a simple material like an undeformed Ni or Fe alloy. But, I think it is a shame to perform longer duration tests on materials I have already seen many times before. For such occasions, I look through my collection of materials for something nice to map. During testing of the upcoming APEX™ 2.0 EBSD software, I collected a few larger scans on fossils that I had found during geological fieldwork and family holidays. This included large single-field scans and a Montage map, where we combine beam scans with stage movements for a large mosaic map.

Cross-section through a fossil crinoid stem and IPF on PRIAS™ center map of the fossil crinoid stem sample collected from the indicated area.

Figure 1. a) Cross-section through a fossil crinoid stem. b) IPF on PRIAS™ center map of the fossil crinoid stem sample collected from the indicated area.

For example, Figure 1a shows a cross-section through a fossil crinoid stem. At the edge, the lighter areas represent the structure of the organism, while the darker areas are later sedimentary infill.

This is beautifully visible in the 2.1 x 1.7 mm IPF on PRIAS™ center map, where the biomineral structure appears smooth and fine-grained. In contrast, the infill is more equiaxed and shows topography due to compositional differences (Figure 1b).

Another beautiful scan was collected while I was trying out the new APEX™ 2.0 EBSD Montage map wizard. This wizard allows easy pre-imaging of the entire scan field to set the actual scan area. With the wizard, setting up such a large, 18 million point, 30-field Montage map over a 1.3 x 7 mm area can be done in a few minutes.

Calcite rock sample with fossils and EBSD Montage map of one of the nummulite fossils.

Figure 2. a) Calcite rock sample with fossils. b) EBSD Montage map of one of the nummulite fossils.

We collected these two scans on calcite rocks for which you can simply load the appropriate crystal structure. But collecting data is not always that easy, especially if you are not sure what phase(s) you have in your sample. And ultimately, EBSD data collection is based on pattern analysis and then matching the detected bands against a lookup table. In most cases, you can just search the included EDAX structure file database that contains close to 500 phases and covers most commonly studied materials, such as the calcite used for the scans above.

But where do these files come from? Partly, they are a result of our combined legacy. Over the years, we have seen many materials and often painstakingly identified which bands to select to get reliable indexing results. Nowadays, you can create phase files directly using atomic and crystallographic information. However, you can continue to extract the majority of “new” phase files from XRD databases, such as the AMCS, ICSD, or ICDD PDF databases. These databases contain 10’s to sometimes 100’s of thousands of phase descriptions that are based on XRD measurements. The XRD data shows which lattice planes are effective X-ray diffractors, and are also useful to construct a structure file for electron diffraction patterns.

Indexed olivine EBSD pattern.

Figure 3. Indexed olivine EBSD pattern.

And there the fun starts. Often there are multiple possibilities for phases or minerals (e.g., solid solution series) available in the database. Which one to select? And in many cases, there is no single-phase file that matches the pattern exactly. There are always bands that do not get labeled or are shown in the overlay that are not visible in the real pattern. This is due to the differences between X-ray and electron diffraction intensities or simply incomplete database entries. Time for some human intervention. The APEX™ EBSD software contains advanced tools to modify and optimize the reflector tables of imported or calculated structure files. First, the color-coding itself. All bands are labeled with a color that corresponds to the IPF color triangle, so equivalent lattice planes get identical colors. This allows a visual inspection if bands that are designated with the same color also appear identical.

IPF color triangle.

Figure 4. IPF color triangle.

Then there is a band ID tool to help identify non-labeled bands in the diffraction patterns. When a pattern appears correctly indexed, but a number of bands are not labeled, the user can draw a line on the missing band. The software then shows which lattice plane corresponds to that band and also indicates all crystallographic equivalent planes. When it is still difficult to identify the correct indexing solution, it can be beneficial to bypass the Hough band detection and instead manually draw the bands for indexing. A good trick for low symmetry crystals is only to select the thinnest bands. These correspond to the lattice planes with the largest d-spacings and should be the important low-index crystallographic planes. By excluding the (often) large number of bands with similar bandwidths, you reduce the number of options and more quickly land at the best matching orientation or phase.

Manual Band Selection tool.

Figure 5. Manual Band Selection tool.

When a solution is found that matches the thin bands, you can start drawing in the other ones. When drawing a band, the software automatically shows where all the crystallographic equivalent planes should be. If a line is drawn where no band is present, you have the wrong candidate, and you need to look further. If all the indicated bands match in appearance and width, you can enable the reflector. This way, it is easy to interactively generate a matching phase file. Just keep in mind that when you have optimized a structure file to a pattern, it is a good idea to find some more patterns from that phase (if necessary, just rotate the sample to get a different orientation) and verify that all the bands in the other patterns are also properly identified. This is especially important for low symmetry materials where few lattice planes are equivalent.

Band optimization sequence on an EBSD pattern from W2C. The initial reflector table (a) misses a number of strong bands. Manually selecting a band (b) shows which reflector this is and where the crystallographic equivalent bands should be. This can be repeated (c) until all clear bands have been labeled.

Figure 6. Band optimization sequence on an EBSD pattern from W2C. The initial reflector table (a) misses a number of strong bands. Manually selecting a band (b) shows which reflector this is and where the crystallographic equivalent bands should be. This can be repeated (c) until all clear bands have been labeled.

Although it can be rewarding to identify a new phase and optimize the structure file to allow for EBSD mapping of a new and interesting material, I would like to end with a word of warning. When you are working with a good pattern and successfully identify the phase and orientation, it is very tempting to keep looking for bands and completely fill the pattern with everything you can see. But that is often a bad idea, as the weaker bands will typically not get selected by the Hough transformation on the poorer patterns that are used during indexing. Enjoy playing with the materials and structure files, but don’t overdo it.

Diffraction pattern with all visible bands enabled for indexing.

Figure 7. Diffraction pattern with all visible bands enabled for indexing.

How Many Electrons Do You Need For An EBSD Pattern?

Matt Nowell, EBSD Product Manager, EDAX

I always liked the commercial that asked,” How many licks does it take to get to the center of a Tootsie Pop?”. I like contests where you estimate the number of M&Ms in a jar. Taking the concept away from delicious treats and moving towards something more technical, I’ve also enjoyed looking at the number of grains we need to measure with EBSD to get a good idea of the texture of a material.

Recently I’ve been working with our new Clarity™ Direct Electron Detector for EBSD. It’s the first commercial EBSD direct detector and will be launching soon. Traditionally, EBSD patterns are captured when the diffracted electrons strike a phosphor screen, where energy is converted into light photons, which are focused through a lens onto an imaging sensor, where the light photons are then converted back to electrons. However, a direct electron detector is just that, it captures the diffracted electrons directly. This allows us to count the electrons in an EBSD pattern directly.

EBSD pattern collected with Clarity™ with an average of 5,000 electrons per pixel.

Figure 1. EBSD pattern collected with Clarity™ with an average of 5,000 electrons per pixel.

Take the EBSD pattern collected from a nickel superalloy using the Clarity™ shown in Figure 1. For an EBSD pattern like this, remember that it has been background corrected to flat-field the image and improve the contrast. This is because the actual live EBSD pattern does not have a uniform intensity across the sensor, as shown in Figure 2. In this example, a background collected while imaging many grains was collected and subtracted from the live signal to produce the image in Figure 1. The background image has the spatial information for a specific orientation removed, while retaining the overall intensity gradient that is a function of the material of interest and the sample geometry. Note that the Clarity™ uses four direct electron detectors that are coupled together. The cross-hair image visible in Figure 2 shows the location of the seams between the detectors. These can be masked out of the image if desired but are quickly minimized with this background correction.

EBSD pattern from Figure 1 prior to background correction.

Figure 2. EBSD pattern from Figure 1 prior to background correction.

For Figure 1, a pixel at the center of the signal intensity contained approximately 10,000 electrons, and the average counts for all pixels was approximately 5,000 electrons. After background subtraction, I drew a line across the image, and the intensity profile across this line is shown in Figure 3. This profile shows that the final processed EBSD pattern has a dynamic range of about 1,700 electrons.

Line profile across the EBSD pattern in Figure 1 showing the dynamic range of the EBSD signal.

Figure 3. Line profile across the EBSD pattern in Figure 1 showing the dynamic range of the EBSD signal.

EBSD pattern with an average of 10 electrons per pixel.

Figure 4. EBSD pattern with an average of 10 electrons per pixel.

Now seeing that I could count the number of electrons in an EBSD pattern, I wanted to know how many I needed to get a usable EBSD pattern. I could decrease the exposure time, decrease the beam current, or do both. In this case, I continually decreased the exposure time to find where the EBSD pattern indexing started to fail. Figure 4 shows an EBSD pattern where the maximum number of electrons is 20 and the average number of electrons is 10. Even with this small amount of a signal, I was still able to index it with a confidence index of 0.92 and a fit of 0.6°, which indicates a good orientation solution. Talk about doing a lot with a little. This performance is enabled by the single electron sensitivity and zero readout noise of the detector, which makes this camera very exciting for low beam dose applications for beam-sensitive materials. I look forward to sharing more later.

Indexing solution for the pattern in Figure 4 with a confidence index of 0.92.

Figure 5. Indexing solution for the pattern in Figure 4 with a confidence index of 0.92.

How to Get a Good Answer in a Timely Manner

Shawn Wallace, Applications Engineer, EDAX

One of the joys of my job is troubleshooting issues and ensuring you acquire the best results to advance your research. Sometimes, it requires additional education to help users understand a concept. Other times, it requires an exchange of numerous emails. At the end of the day, our goal is not just to help you, but to ensure you get the right information in a timely manner.

For any sort of EDS related question, we almost always want to look at a spectrum file. Why? There is so much information hidden in the spectrum that we can quickly point out any possible issues. With a single spectrum, we can quickly see if something was charging, tilted, or shadowed (Figure 1). We can even see weird things like beam deceleration caused by a certain imaging mode (Figure 2). With most of these kinds of issues, it is common to run into major quant related problems. Any quant problems should always start with a spectrum.

Figure 1. The teal spectrum shows a strange background versus what a normal spectrum (red) should look like for a material.

Figure 1. The teal spectrum shows a strange background versus what a normal spectrum (red) should look like for a material.

This background information tells us that the sample was most likely shadowed and that rotating the sample to face towards the detector may give better results.

Figure 2. Many microscopes can decelerate the beam to help with imaging. This deceleration is great for imaging but can cause EDS quant issues. Therefore, we recommend reviewing the spectrum up front to reduce the number of emails to troubleshoot this issue.

Figure 2. Many microscopes can decelerate the beam to help with imaging. This deceleration is great for imaging but can cause EDS quant issues. Therefore, we recommend reviewing the spectrum up front to reduce the number of emails to troubleshoot this issue.

To save the spectrum, right-click in the spectrum window, then click on Save (Figure 3). From there, save the file with a descriptive name, and send it off to the applications group. These spectrum files also include other metadata, such as amp time, working distance, and parameters that give us so many clues to get to the bottom of possible issues.

Figure 3. Saving a spectrum in APEX™ is intuitive. Right-click in the area and a pop-up menu will allow you to save the spectrum wherever you want quickly.

Figure 3. Saving a spectrum in APEX™ is intuitive. Right-click in the area and a pop-up menu will allow you to save the spectrum wherever you want quickly.

For information on EDS backgrounds and the information they hold, I suggest watching Dr. Jens Rafaelsen’s Background Modeling and Non-Ideal Sample Analysis webinar.

The actual image file can also help us confirm most of the above.

Troubleshooting EBSD can be tricky since the issue could be from sample prep, indexing, or other issues. To begin, it’s important to rule out any variances associated with sample preparation. Useful information to share includes a description of the sample, as well as the step-by-step instructions used to prepare the sample. This includes things like the length of time, pressure, cloth material, polishing compound material, and even the direction of travel. The more details, the better!

Now, how do I know it is a sample prep problem? If the pattern quality is low at long exposure times (Figure 4) or the sample looks very rough, it is probably related to sample preparation (Figure 4). That being said, there could be non-sample prep related issues too.

Figure 4. This pattern is probably not indexable on its own. Better preparation of the sample surface is necessary to index and map this sample correctly.

Figure 4. This pattern is probably not indexable on its own. Better preparation of the sample surface is necessary to index and map this sample correctly.

For general sample prep guidelines, I would highly suggest Matt Nowell’s Learn How I Prepare Samples for EBSD Analysis webinar.

Indexing problems can be challenging to troubleshoot without a full data set. How do I know my main issues could be related to indexing? If indexing is the source, a map often appears to be very speckled or just black due to no indexing results. For this kind of issue, full data sets are the way to go. By full, I mean patterns and OSC files. These files can be exported out of TEAM™/APEX™. They are often quite large, but there are ways available to move the data quickly.

For the basics of indexing knowledge, I suggest checking out my latest webinar, Understanding and Troubleshooting the EDAX Indexing Routine and the Hough Parameters. During this webinar, we highlight attributes that indicate there is an issue with the data set, then dive into the best practices for troubleshooting them.

As for camera set up, this is a dance between the microscope settings, operator’s requirements, and the camera settings. In general, more electrons (higher current) allow the experiment to go faster and cover more area. With older CCD based cameras, understanding this interaction was key to good results. With the newer Velocity™ cameras based on CMOS technology, the dance is much simpler. If you are having difficulty while trying to optimize an older camera, the Understanding and Optimizing EBSD Camera Settings webinar can help.

So how do you get your questions answered fast? Bury us with information. More information lets us dive deeper into the data to find the root cause in the first email, and avoids a lengthy back and forth exchange of emails. If possible, educate yourself using the resources we have made available, be it webinars or training courses. And always, feel free to reach out to my colleagues and me at edax.applications@ametek.com!

Shelf Life

Dr. Bruce Scruggs, XRF Product Manager, EDAX

Recently, we had a customer request to see a demonstration on the Orbis micro-XRF system. As we talked about what they would like to see, he mentioned that he had made some test XRF measurements on table salt, and he couldn’t measure the iodine content. I agreed to measure the iodine content in table salt. Initially, I thought this would be a very straightforward exercise, as table salt is just NaCl with some iodine added, but this was anything but straightforward.

The iodization of salt in the United States began about a century ago. Iodine is an important micronutrient for thyroid gland health. Certain portions of the American population had diets deficient in iodine and the iodization of table salt was chosen as a method to increase the level of iodine in the average American diet. The salt iodization process was inexpensive; salt does not spoil and estimates of table salt consumption were available.

Some weeks before the customer demo, I bought some iodized table salt from the local grocery store. The ingredients list showed iodine in the form of potassium iodide at about 45 ppm iodine. This concentration was consistent with my web searches. I pressed a pile of salt grains onto a piece of carbon tape and measured it with the Orbis system using a 2 mm spot size (the system was equipped to measure down to a 30 μm spot size, small enough for individual grains, but I wanted to avoid any potential issues with grain to grain variations). It was easy enough and I could measure the I(L) lines with I(Lα) at 3.937 keV (Figure 1).

(A): Salt spectrum with peak deconvolution, not including I(L) series; Fig 1(B): The same salt spectrum as in (A) with peak deconvolution including I(L) series.

Figure 1. (A) Salt spectrum with peak deconvolution, not including I(L) series. (B) The same salt spectrum as in (A) with peak deconvolution including I(L) series.

Some weeks later, during the actual customer demonstration, we measured a variety of customer supplied samples and the customer asked to measure table salt near the end of the demo. I put my table salt sample into the Orbis and was astonished to find that the iodine signal disappeared (Figure 2). Peak fitting and quantification results showed no detectable iodine. After a discussion with the customer, I began to suspect that the salt iodization level was not stable, given that solid I2 is known to undergo sublimation at room temperature. I spoke to the customer again and in his previous attempts, he measured table salt (from shakers) in the company cafeteria. I often wonder how long that salt has been in the shaker!

The same salt sample, as Figure 1, measured on the Orbis a few weeks later without the presence of iodine.

Figure 2. The same salt sample, as Figure 1, measured on the Orbis a few weeks later without the presence of iodine.

Further web searches indicated that indeed, the iodization level of salt has a certain shelf life depending on many factors, including temperature, humidity, impurities in the salt, the chemical form of the iodine bearing additives, and product packaging. For example, potassium iodide is oxidized by contact with oxygen and atmospheric moisture and the resulting iodine then undergoes sublimation. In various regions of the world, iodized table salt is formulated to improve its shelf life with regard to iodine retention based on the characteristics of the table salt and the general environment, e.g., desert, tropical. Based on this loss mechanism, I suspect that there must also be a significant loss of iodine during cooking depending on whether salt is added while cooking or directly applied before consuming.

In my case, the iodine level had dropped below detectable limits in about three weeks of being left out on the table. The grains of salt ranged in size from about 100 – 500 μm in characteristic dimensions, and I was curious to what characteristic depth XRF was measuring. Was there possibly any iodine left in the largest crystals? This depth can be estimated based on the fluorescent signal energy as the exciting X-ray energy always has to be greater than the fluoresced photons (The physics are a bit different for electron excitation where the answer is determined by electron penetration depth into the sample).

XRF measurement depth can be estimated from the Beer-Lambert equation for the absorption and transmission of light:

Equation 1

Equation 1.

The mass absorption coefficient (MAC) describes how readily the I(Lα) signal line at 3.937 keV will be absorbed by the NaCl matrix. It can be described as follows:

Equation 2

Equation 2.

For NaCl, we have two MACs describing how Na and Cl each absorb the 3.937 keV photon. The easiest way to get the full matrix MAC is to back-calculate it from the Beer-Lambert equation and any web-based calculator describing X-ray absorption/transmission characteristics modeling the fluoresced photon traversing the sample matrix to the detector. I prefer the website, http://henke.lbl.gov/optical_constants/filter2.html. By inputting the sample matrix formula (including trace elements if desired), and an arbitrary path length, one can get the calculated result for I/Io and then rearrange Equation 1 to solve for the NaCl matrix MAC by inputting the previously used path length and the known density of table salt. The result is: μNaCl(3.937 keV) ~ 540 cm2/g.

Rearranging Equation 1, one can solve for the signal path length through the sample traversed by the fluoresced photon to the detector as a function of I/Io:

Equation 3

Equation 3.

The XRF Emission Depth, D, would typically be defined as normal to the sample surface, and you should also consider the take-off angle (TOA) of the detector defined from the sample surface, as shown in Equation 4.

Equation 4

Equation 4.

Table 1 shows the XRF Emission Depth as a function I/Io with a nominal detector TOA of 50ᵒ.

I/Io [%] Path Length, x [μm] Emission Depth, D [μm]
10 20 15
1 39 30
0.1 59 45

Table 1. XRF Emission Depth as a function of the signal transmission ratio, I/Io.

The definition of the characteristic XRF path length and emission depth is somewhat arbitrary, as it depends on the value assigned to the signal transmission ratio, I/Io. Typically, the characteristic path length is defined as the length over which 99% of the signal is absorbed. Hence:

Equation 5

Equation 5.

It is interesting to note from Table 1, that at 50% of the critical emission depth, the XRF signal is undergoing 90% absorption.

Coming back to the original analysis, it is possible that iodine was still present at the core of the larger 500 μm grains of salt. Further analyses could be done on cross-sectioned grains or pulverized grains to make that determination. It would be possible to measure cross-sectioned grains of NaCl using the 30 μm spot size on the Orbis to study how readily iodine is lost as a function of depth into the NaCl grain, but that is a study for another day.

Colorful Language

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

As some of you may know, I dabble in woodworking. Over the years, I’ve built several things for our home. I’m embarrassed to admit that when things don’t go right on these projects, I’ve also been known to use some colorful language to express my frustration. I’m a little prouder that color maps have been the language of EBSD since the inception of the automated systems. Figure 1 shows one of the first color maps I created with the help of Karsten Kunze, who was a Post-Doc at Yale University while I was working on my PhD. The colors are associated with prominent peaks in the ODF. Namely the ideal copper orientation {112}〈111〉 in blue and its statistically symmetric variant (i.e. arising from the processing symmetry – rolled sheet in this case) in red, the ideal brass orientation {011}〈211〉 in green and its statistical variant in orange. The ability to illuminate the crystallographic orientation aspects of the microstructure using such color maps took off quickly. I’ve always thought such maps have an aesthetic beauty to them. As the New Mexico based artist, Georgia O’Keeffe said “I found I could say things with color… that I couldn’t say any other way.”

Orientation distribution function (ODF) plotted in Euler space and an orientation map from rolled aluminum (November 1991). Colors correspond to the copper and brass texture α-fiber components for rolled FCC materials.

Figure 1. Orientation distribution function (ODF) plotted in Euler space and an orientation map from rolled aluminum (November 1991). Colors correspond to the copper and brass texture α-fiber components for rolled FCC materials.

I remember a conversation in the lab with Karsten and my advisor, Professor Brent Adams, arguing whether we could see some patterns in the arrangement of the different “colored” orientations. We found each of us were predisposed to seeing patterns for certain colors over other colors. This led to one more chapter in my PhD thesis focused on orientation correlation. In this chapter, I tried to confirm the presence of patterns in the arrangement of orientations within the microstructure with some statistical rigor. In the end, there didn’t appear to be much correlation for any of the colors.

There has been some recent work on improving color mapping. There are two parts to this, to try and get (1) more “perceptually uniform” color maps1 and (2) better color maps for showing crystallographic orientation particularly for low symmetry materials2,3. I’ve implemented these ideas into OIM Analysis™ v8.5. This version will hopefully be ready early this year – we are currently in the testing/bug fixing phase. The reason for “perceptually uniform” color maps (PUCM) is that we can see variations within some colors but not others. For example, Figure 2 shows a GROD map for a steel sample after 10% tensile strain. The mapping has been done using our standard “rainbow” color gradients and with a PUCM version of the rainbow gradient.

Grain Reference Orientation Deviation (GROD) maps for steel tensile specimen deformed in-situ. The first row is displayed using the standard OIM “rainbow” color gradient and the second using a perceptually uniform color map (PUCM). The first column of maps are GROD-angle maps, the second column of maps are GROD-angle maps overlaid on gray scale image quality (IQ) maps.

Figure 2. Grain Reference Orientation Deviation (GROD) maps for steel tensile specimen deformed in-situ. The first row is displayed using the standard OIM “rainbow” color gradient and the second using a perceptually uniform color map (PUCM). The first column of maps are GROD-angle maps, the second column of maps are GROD-angle maps overlaid on gray scale image quality (IQ) maps.

You will notice in the color gradient that it is difficult to see subtle color variations for blue, green and red in the standard rainbow gradient, whereas the PUCM color gradient shows a more consistent variation in the colors across the full range of colors. The variations at the top of the color scales are 10%. From a purely aesthetic point of view, I like the vibrancy of the colors in our standard rainbow color mapping. However, I can also see that the standard color gradient can be somewhat misleading as to the degree of color variation. I noted in my thesis, “This visualization of the microstructure is a useful technique for coupling the morphological and orientation aspects of microstructure into a discrete picture. … Orientation correlation calculations can be made to statistically quantify this apparent structure. This is discussed in the following chapter.” In other words, the colors are nice and helpful. They can serve as a guide for further quantitative analysis, but without that subsequent chapter on the “further quantitative analysis”, our microstructure characterization report will end up as a picture book for the coffee table.

Here is another way to look at the benefits of perceptually uniform color mapping. Figure 3 shows a series of IPF maps where I have rotated the data to show the large grain at the center in several ideal orientations. The top row uses our standard color triangle, whereas the bottom row uses the PUCM color triangle. Note that the subtle color variations in the large grain are more evident in some colors than in other colors.

Inverse Pole Figure (IPF) maps for the same grain in seven different orientations displayed using the standard OIM color mapping scheme (top row) and the PUCM scheme (bottom row).

Figure 3. Inverse Pole Figure (IPF) maps for the same grain in seven different orientations displayed using the standard OIM color mapping scheme (top row) and the PUCM scheme (bottom row).

It is interesting to note that, in general, the PUCM maps show less color variation than our standard IPF maps. Also note that the degree of color variation is more consistent across the full range of colors. Consistency is good, but the results are not as dramatic as I had originally anticipated. PUCM does not alleviate the need to extend our analysis beyond pretty pictures to a thorough quantitative examination of the orientation data.

For the lower symmetries, the PUCM color mapping schemes are quite different from our standard color mapping schemes and include both white and black to extend the available color palette. As can be seen in Figure 4, for rhodochrosite, the extended PUCM color palette works well for standalone IPF maps, but does not work as well when the IPF maps are overlaid on a gray scale map, such as an IQ map. So once again, the PUCM maps have some advantages and disadvantages over the standard maps. Certainly, having more views of the same data is helpful. But another chapter on quantitative analysis is needed.

IPF maps for a mineral specimen containing rhodochrosite, barite, quartz and pyrite. The left column of maps are displayed using the standard OIM color mapping scheme and the right column using the PUCM scheme. The top row of maps are standalone IPF maps, the second row of maps are IPF maps overlaid on gray scale IQ maps.

Figure 4. IPF maps for a mineral specimen containing rhodochrosite, barite, quartz and pyrite. The left column of maps are displayed using the standard OIM color mapping scheme and the right column using the PUCM scheme. The top row of maps are standalone IPF maps, the second row of maps are IPF maps overlaid on gray scale IQ maps.

A phase map for the rhodochrosite mineral specimen.

Figure 5. A phase map for the rhodochrosite mineral specimen.

OK, probably a few too many references about the need to go beyond pictures to quantitative analysis, so let me end on a fun anecdote about color from the “Microscale Texture of Materials” Symposium at the joint ASM/TMS Meeting in Cincinnati, OH in October 1991. At this meeting, I gave our first presentation on results obtained using fully automate EBSD. My presentation was short leaving time for an energetic discussion. One point of discussion was the bit depth needed for image processing to detect the bands in the patterns. My advisor, Professor Adams, said something along the lines of “the human eye can only differentiate 32 colors” (he meant 32 gray levels). I remember Professor Dr. Robert Schwarzer chimed in after Brent with “I don’t know about the American eye, but the European eye can certainly see more than 32 colors!” Of course, we all got a big laugh 😊. Perhaps my European friends can get by with the color maps and can skip the extra “chapter” on quantitative analysis!

1 Peterkovesi.com/projects/colourmaps/index.html
2 G. Nolze and R. Hielscher (2016) “Orientations–perfectly colored” Journal of Applied Crystallography, 49: 1786-1802.
3 William C. Lenthe & Marc De Graef (2018), “Perceptually Uniform Color Maps for the Disk, Sphere, and Ball”, preprint

What a Difference a Year Makes

Jonathan McMenamin, Marketing Communications Coordinator, EDAX

EDAX is considered one of the leaders in the world of microscopy and microanalysis. After concentrating on advancements to our Energy Dispersive Spectroscopy (EDS) systems for the Scanning Electron Microscope (SEM) over the past few years, EDAX turned its attention to advances in Electron Backscatter Diffraction (EBSD) and EDS for the Transmission Electron Microscope (TEM) in 2019.

After the introduction of the Velocity™ Plus EBSD camera in June 2018, which produces indexing speeds greater that 3,000 indexed points per second, EDAX raised the bar further in 2019. In March, the company announced the arrival of the fastest EBSD camera in the world, the Velocity™ Super, which can go 50% faster at 4,500 indexed points per second. This was truly a great accomplishment!

EBSD orientation map from additively manufactured Inconel 718 collected at 4,500 indexed points per second at 25 nA beam current.

EBSD orientation map from additively manufactured Inconel 718 collected at 4,500 indexed points per second at 25 nA beam current.

Less than three months later, EDAX added a new detector to its TEM product portfolio. The Elite T Ultra is a 160 mm2 detector that offers a unique geometry and powerful quantification routines for comprehensive analysis solutions for all TEM applications. The windowless detector’s geometric design gives it the best possible solid angle to increase the X-ray count rates for optimal results.

EDAX Elite T Ultra EDS System for the TEM

EDAX Elite T Ultra EDS System for the TEM.

Just before the annual Microscopy & Microanalysis conference, EDAX launched the OIM Matrix™ software module for OIM Analysis™. This new tool gives users the ability to perform dynamic diffraction-based EBSD pattern simulations and dictionary indexing. Users can now simulate EBSD patterns based on the physics of dynamical diffraction of electrons. These simulated patterns can then be compared to experimentally collected EBSD patterns. Dictionary indexing helps improve indexing success rates over standard Hough-based indexing approaches. You can watch Dr. Stuart Wright’s <a href=”https://youtu.be/Jri181evpiA&#8221; target=”_blank”>presentation from M&M</a> for more information.

Dictionary indexing flow chart and conventional indexing results compared with dictionary indexing results for a nickel sample with patterns collected in a high-gain/noisy condition.

Dictionary indexing flow chart and conventional indexing results compared with dictionary indexing results for a nickel sample with patterns collected in a high-gain/noisy condition.

EDAX has several exciting product announcements on the way in early 2020. We have teased a two of these releases, APEX™ Software for EBSD and the Clarity™ Direct Electron Detector. APEX™ EBSD will give users the ability to characterize both compositional and structural characteristics of their samples on the APEX™ Platform. It gives them the ability to collect and index EBSD patterns and EBSD maps, as well as allow for simultaneous EDS-EBSD collection. You can learn more about APEX™ EBSD in the September issue of the Insight newsletter and in our “APEX™ EBSD – Making EBSD Data Collection How You Want It” webinar.

EBSD of a Gibeon Meteorite sample covering a 7.5 mm x 6.5 mm area using ComboScan for large area analysis.

EBSD of a Gibeon Meteorite sample covering a 7.5 mm x 6.5 mm area using ComboScan for large area analysis.

The Clarity™ is the world’s first commercial direct electron detector (DeD) for EBSD. It provides patterns of the highest quality and sensitivity with no detector read noise and no distortion for optimal performance. The Clarity™ does not require a phosphor screen or light transfer system. The DeD camera is so sensitive that individual electrons can be detected, giving users unprecedented performance for EBSD pattern collection. It is ideal for analysis of beam sensitive samples and potential strain applications. We recently had a webinar “Direct Electron Detection with Clarity™ – Viewing EBSD Patterns in a New Light” previewing the Clarity™. You can also get a better understanding of the system in the December issue of the Insight newsletter or the .

EBSD pattern from Silicon using the Clarity™ detector.

EBSD pattern from Silicon
using the Clarity™ detector.

All this happened in one year! 2020 looks to be another great year for EDAX with further improvements and product releases to offer the best possible tools for you to solve your materials characterization problems.

Those People and Things

Dr. Sophie Yan, Applications Engineer, EDAX

Click here to read the post in Chinese.

The end of the year is my conference season. I have been to various conferences since October and I have seen many new faces. Recently, I realized that several young people I trained have stepped into the electron microscopy and microanalysis world. Their reasons seemed to be similar: want a life like Sophie’s. I felt deeply honored but also frightened. Did I give the young people a good example or fantasy?

A couple of us who used to study and/or work at Shanghai Institute of Ceramics have organized an annual meetup at the Chinese Electron Microscopy Society Conference. This year the number of participants reached 19, indicating more and more people have joined this field. As time passes, I have been able to recognize some of the big names in microscopy, and I am overwhelmed at how quickly young scientists have become those big names. Indeed, when more and more new faces have become major players in this field, it indicates the prosperity of this field. I am very fortunate to be a witness of this booming industry.

Once at SEMICON, a participant from Taiwan couldn’t believe my decision to step out after he/she realized that I was no longer in the semiconductor industry. At that time, I didn’t care about his/her words, but right now I figured out why he/she felt so sorry for me. It is very fortunate to love a job you choose. The semiconductor industry was a little down at the time I left, but it has been developing incredibly fast afterwards and I have found the job I love. It is so good to see that my breakup with semiconductor made both of us happy.

Mr. Yang from The University of Science and Technology Beijing (the author of the first Chinese EBSD book you’re supposed to read in China) used to tell me that “I felt you have been attending a lot of conferences and got much more resources than other people.” So I really got lucky.

My EBSD mentor, European applications specialist, René de Kloe, has traveled all around the world. He is very knowledgeable and humble but shows his expertise when questioned. He always promptly and fully replies to my emails and is always ready to help. Although we meet often, every time I am impressed by his expertise and like him more.

Dr. Sophie Yan and Dr. Stuart Wright

Dr. Sophie Yan and Dr. Stuart Wright

EDAX EBSD experts at a meeting in Draper, UT.

EDAX EBSD experts at a meeting in Draper, UT.

And Dr. Stuart Wright, he is a legend in the EBSD world. His name appears in textbooks and references to all kinds of EBSD papers. He took René and I to the west coast of the United States the first time I met him. René said that his toes finally touched the water of the Pacific Ocean again and for the first time in 3 years. He said that his feet high fived each other from the last time he dipped his feet in Tokyo Bay. In 2017, ICOTOM was held in the small city where Stuart lives. As a conference organizer, he took care of everything by himself. That was the most successful conference that considered both academic atmosphere and hospitality. (Well, I must attend the next ICOTOM in Osaka in September 2020).

With lots of luck, I have been to many places and gotten in touch with big names in this field. The cost is I travel more than 50%, on mainly domestic trips with more than 100,000 kilometers every year. I have seen everything there is to see in the Beijing and Shanghai airports. In contrast, the streets of every city look common to me. A kind of common that you can’t figure out their meanings at a glance.

Beijing Daxing International Airport

The new Beijing Daxing International Airport opened in September 2019.

But when people ask what exactly is EDAX’s direct electron detection? I can finally calm down and keep the conversation going, although I just know a little about it. René and Stuart patiently explained it to me when I knew nothing about it, and now it is my turn to spread the word. This is a brand-new field, and EDAX is the first player. What can we do with direct electron detection? Just wait and see. For a sneak preview, take a look at René’s recent webinar, “Direct Electron Detection with Clarity™ – Viewing EBSD Patterns in a New Light”.

Look Closer

Dr. René de Kloe, Applications Specialist, EDAX

All our senses are aimed at observation. We feel, see, hear, smell and taste things to experience the world around us. We are relying on our senses to make many of our day-to-day decisions and choices. And especially in the upcoming holiday season, shops and companies in the business of selling things cleverly use shiny advertisements, brochures, fragrances, and unbeatable product descriptions to entice us to select their wares. All the time hoping that we will succumb to our senses that focus on the superficial appearance of products before thinking things through.

We must be very careful not to let this very successful marketing strategy subconsciously guide us when analyzing materials as well. During our work as microscopists we are continuously selecting samples, cutting and preparing them to expose a feature of interest, and then choosing the analytical tool and actual analysis area. How sure can we be that we really get representative and objective information?

Dr. René de Kloe's PhD thesis

Dr. René de Kloe’s PhD thesis.

As a geologist, I was taught to take your distance from a rock outcrop and look it over before going into any detail, knowing that the context of your observations is crucial in your interpretation. Then I would go in close to look, feel, and yes sometimes actually taste the rock in order to try to identify what I was actually looking at and how the overall structure fit in the geological setting of the area.

Observing this distance is crucial for your understanding of structures, but in some cases, you cannot get out far enough to see the bigger picture and then you must make do with what you can see.

Perhaps an extreme example is what I did for my PhD research. I have studied the occurrence and distribution of nm-scale films of amorphous material along grain boundaries in experimentally deformed rocks that originate deep inside the Earth. In total I may have characterized a few cubic microns of material but based on that I tried to draw conclusions on the effects of these melt layers on the movements of entire continents!

In microanalysis, we are suffering from the same problem. Microscopy inherently means that you cannot look at the wider picture and when you are looking at extremely small-scale features, their size combined with a practical image resolution may limit the observable surface even further. And one of the most difficult questions you then must ask yourself before starting an analysis is, if the analysis area is representative. And that can be a really tricky question. How objective are we all when browsing the sample surface to find a spot to collect the data? Don’t we all tend to preferentially pick an area that looks promising? I am not so sure that that would always be the most representative region.

It is not that long ago that the acquisition limits in EDS and EBSD were caused by the detector technology. For EDS mapping, we were quite happy if you could collect your data with 50,000 input counts per second and a 50% dead time. This meant that when you were collecting a 512 x 400 pixel map where you wanted to have, say 1000 X-ray counts per pixel, it would take you a few hours. And after that time someone else would be hovering behind you, eager to use the microscope. This seriously limited the sample area that could be analyzed and as a researcher you needed to think carefully about your analysis strategy to get representative information.

Single field EDS map of FeSi sample with REE phases

Single field EDS map of FeSi sample with REE phases.

The area that can be analyzed has changed dramatically with the introduction of the latest EDS detector technology. These detectors are capable of processing more than two million input counts and get maximum throughputs of 850,000 counts per second. You can now get the same area analysis in a matter of minutes, which allows you to analyse more samples or simply more areas on your sample. Alternatively, you can choose to get a wider view and collect large area mosaic maps to minimise the risk of unintended preferential area selection and get more representative data.

120 field multi-field EDS map of an igneous rock showing merged ROI maps of Si (red), Fe (yellow), and O (green) on a backscatter SEM image. Total image resolution 6144 x 4000 points ~ 5.4 x 3.5 mm

120 multi-field EDS map of an igneous rock showing merged ROI maps of Si (red), Fe (yellow), and O (green) on a backscatter SEM image. Total image resolution 6144 x 4000 points ~ 5.4 x 3.5 mm.

A similar dramatic improvement has occurred in EBSD technology. When I started as EBSD application specialist at EDAX in 2001, my first EBSD demo system could collect at least two points per second when it was not raining and the moon was in the right quarter (or perhaps more realistically, if I was really lucky to have a good sample with strong patterns). The map below was one of my first maps that I collected when getting to know the system and I still use it today as an example to show different typical EBSD mapping features, such as grain boundaries, subgrain boundaries, twins, and slip planes. This map contains “only” 124,405 points but took an 8.5-hour overnight scan to collect.

EBSD IPF on IQ map of Ni alloy

EBSD IPF on IQ map of Ni alloy.

 

49 field EBSD comboscan IPF on PRIAS™ center map of an Fe alloy

49 multi-field EBSD comboscan IPF on PRIAS™ center map of an Fe alloy.

The same map today would take less than half a minute to collect with a Velocity™ EBSD detector. Or when you would like to take a little wider view you can combine beam and stage movements to collect a 2.5 million point scan of an entire sample in about 15 minutes.

These technological improvements allow you to be more efficient with your time and collect the same data much faster. But alternatively, it can effectively open our eyes and allow us to investigate much larger areas to see the bigger picture. Just be careful when you look at things from a bit further away, sometimes at the end of the day it may seem that these things start looking back at you!

Large area EDS map of FeSi sample with REE phases – look who’s watching!

Large area EDS map of FeSi sample with REE phases – look who’s watching!