geology

Do not try this at home: Microwave-Rubies

M. Sc. Julia Mausz, Application Scientist, Gatan/EDAX

Synthetic gemstone quality rubies are commonly manufactured with the Verneuil process, which got its name from its ”father ” Dr. A.V.L. Verneuil. This process was designed to produce single crystalline synthetic rubies and can now be used to melt a variety of high melting point oxides. The details of this flame fusion process were already published in 1902-1904 [1]. As I have neither a ruby mine nor a flame fusion device handy, I aimed to manufacture rubies using a different approach. However, I was unsure if it was possible to form single crystals or even large grains with this technique.

Like in the Verneuil process, the starting point of my synthetic rubies was Al2O3 and Cr2O3 powder. Those were homogeneously mixed, aiming at 1 – 2 at. % chromium content. Considering the melting point of Al2O3 (2,038 °C) [2] and Cr2O3(2,435 °C) [3], the maximum local temperature required to melt a powder mixture of both is 2,435 °C.

A microwave-induced plasma will supply the heat. With an operational frequency of about 2.450 GHz, kitchen microwaves can create high temperature plasmas, even at atmospheric pressure [4]. While bulk metals undergo little heating from microwaves due to the reflection of the waves, it is possible to heat fine-grained metal particles with dielectric heating. However, there is a more effective phenomenon to heat metal with microwaves. Electric discharge can occur due to changes in the distribution of charges when a conductive material with a sharp edge or tip is exposed to microwaves in that frequency regime. The heat resulting from the discharge dissipates very locally into the conductive material, resulting in temperature hot spots able to melt metals and metal oxides in direct contact with the metal, as shown later [5] [6] [7].

The main gases relevant for the plasma will be nitrogen (approx. 78%) and oxygen (approx. 21%) from the surrounding air. The electron source to ignite the plasma will be fine, sharp aluminum edges. Therefore, the powder mixture was placed in a glass crucible and covered with a network of fine aluminum stripes. The crucible was shallow and closed with a glass lid to prevent the hot gas from rising away from the powder. Then, the microwave was operated at 900 W and could sustain the plasma for 60 s. Then, the fused parts were collected from the powder, cleaned, and mounted onto an aluminum stub for observation in the SEM. The resulting fused particles were in the order of 0.5 – 2 mm and already showed the expected pink to purple color, which can be seen in Figures 1a and 1b. The fluorescence yield of rubies can be seen under black light. Without blacklights available, I needed to rely on the 8 kV argon ion beam from the Gatan PECS™ II, and the resulting fluorescence is shown in Figure 1c.

a) Various rubies mounted on a carbon tape. b) Detailed view of the rubies under an optical microscope. c) Fluorescing ruby in an argon ion beam in the PECS II using stationary single beam from one side.

Figure 1. a) Various rubies mounted on a carbon tape. b) Detailed view of the rubies under an optical microscope. c) Fluorescing ruby in an argon ion beam in the PECS II using stationary single beam from one side.

The Zeiss Sigma 500 VP SEM was set to 12 kV acceleration voltage, 120 μm aperture, and 3 Pa low vacuum to prevent charging. The microstructure was then analyzed on the unpolished surface using the EDAX Velocity Super EBSD detector. After fusion of the powder, the resulting ruby has a smooth surface with the crystal structure extending all the way to the surface. Therefore, the ruby could be indexed without any polishing step. It is fascinating with how much ease and speed an unpolished, charging material could be analyzed.

Hough indexing already achieved high indexing rates, considering the dirt and the shadowing on the sample. To bring back even more shadowed points and to refine the grain boundaries, I reprocessed the dataset using Neighbor Pattern Averaging & Reindexing (NPAR™) [8] and spherical indexing [9]. For spherical indexing, a dynamic simulation of trigonal Al2O3 was used. For each, the image quality (IQ) map [10] and confidence index (CI) map, an overlay of the orientation map is shown in Figure 2.

Figure 2. Ruby surface. a) IQ map, b) IQ map + IPF map with CI > 0.2 filter and CIS, c) CI map, and d) CI map + IPF map with CI >0.2 and CIS.

The dataset clearly shows a polycrystalline structure. Note that although the grains can be easily recognized, the shape and size of the grains are distorted due to the variation in surface topography.

In contrast to the grain shape, misorientation and texture analyses are unaffected. The detected bands in the EBSD patterns are direct projections of the lattice planes. As the active lattice planes are independent of the surface structure, the measured crystal orientation is not affected by the surface orientation.

The orientation map is displayed in Figure 3a after applying the confidence index standardization (CIS) procedure and a CI filter of 0.2. Figure 3b shows the overlay of this orientation map with its corresponding CI map and the grain boundaries with a minimum misorientation angle of 5° marked in black.

Figure 3. Ruby surface. a) IPF map with CI >0.2 and CIS and b) overlay of IPF map with CI >0.2 and CIS with grain boundary (>5°) in black and CI Map after CIS.

Interestingly, the as-fused state of the ruby showed a clear spike in the misorientation angle of 60°, as shown in Figure 4a. The twin boundaries of 60° with a tolerance angle of 2° are marked in black on top of the detail orientation map in Figure 4b. The crystal wire figure is schematically shown on both sides of the twin boundary, showing a 60° rotation along the c-axis.

Figure 4. Ruby surface. a) Misorientation chart with black highlighting and b) orientation map with black twin boundaries and crystal visualization of both sides.

In Figure 5, the (0001) texture pole figure reveals a weak texture. The orientation maximum is shifted somewhat towards the top-right, corresponding to the surface’s slanting in the same direction. This suggests that there is a weak preferred orientation of the (0001) planes parallel to the surface of the ruby aggregate particle.

Figure 5. Ruby surface. Texture Pole Figure.

It is possible to form synthetic rubies using microwave-induced plasma in a commercial microwave oven. However, the resulting rubies are small, of unpredictable shape, and due to their polycrystalline nature, not of high clarity. While ruby production in the microwave did not qualify to open a gemstone side business, it is a reliable source for making interesting EBSD samples, and we might see some more gemstone blogs in the future.

References

  1. NASSAU, K. Dr. AVL Verneuil: The man and the method. Journal of Crystal Growth, 1972, 13. Jg., S. 12-18.
  2. SCHNEIDER, Samuel J.; MCDANIEL, C. L. Effect of environment upon the melting point of Al2O3. Journal of Research of the National Bureau of Standards. Section A, Physics and Chemistry, 1967, 71. Jg., Nr. 4, S. 317.
  3. GIBOT, Pierre; VIDAL, Loïc. Original synthesis of chromium (III) oxide nanoparticles. Journal of the European Ceramic Society, 2010, 30. Jg., Nr. 4, S. 911-915.
  4. KOCH, Helmut; WINTER, Michael; BEYER, Julian. Optical Diagnostics on Equilibrium and Non-equilibrium Low Power Plasmas. In: 48th AIAA Plasmadynamics and Lasers Conference. 2017. S. 4158.
  5. SUN, Jing, et al. Review on microwave–metal discharges and their applications in energy and industrial processes. Applied Energy, 2016, 175. Jg., S. 141-157.
  6. LIU, Wensheng; MA, Yunzhu; ZHANG, Jiajia. Properties and microstructural evolution of W-Ni-Fe alloy via microwave sintering. International Journal of Refractory Metals and Hard Materials, 2012, 35. Jg., S. 138-142.
  7. ZHOU, Chengshang, et al. Effect of heating rate on the microwave sintered W–Ni–Fe heavy alloys. Journal of Alloys and Compounds, 2009, 482. Jg., Nr. 1-2, S. L6-L8.
  8. WRIGHT, Stuart I., et al. Improved EBSD Map Fidelity through Re-indexing of Neighbor Averaged Patterns. Microscopy and Microanalysis, 2015, 21. Jg., Nr. S3, S. 2373-2374.
  9. LENTHE, W. C., et al. Spherical indexing of overlap EBSD patterns for orientation-related phases–Application to titanium. Acta Materialia, 2020, 188. Jg., S. 579-590.
  10. WRIGHT, Stuart I.; NOWELL, Matthew M. EBSD image quality mapping. Microscopy and Microanalysis, 2006, 12. Jg., Nr. 1, S. 72-84.

Stimulating Simulations

Dr. Stuart Wright, Senior Scientist, EDAX

It has been an interesting experience to build our OIM Matrix™ software package. As you may know, OIM Matrix is partially a front-end user interface to the EMsoft package developed by Professor Marc De Graef’s group at Carnegie Mellon University to make it convenient to use within the framework of OIM Analysis™. I have learned a lot in the process and am grateful for Marc’s patience with my many questions. Will Lenthe recently joined the EBSD group at EDAX. Will worked as a Post-Doc in Marc’s group, and his additional insights have been invaluable as we are striving to build the second generation of OIM Matrix. It will be easier to use, more robust, and provide some significant speed gains.

While our initial focus for OIM Matrix was on helping users improve the indexing of EBSD patterns from difficult-to-index materials, I’ve been surprised by how useful it has been for testing our software. It has also helped us in developing some of our new features. Having well-simulated patterns for known orientations and EBSD/SEM geometries is very helpful.

I used OIM Matrix for a study on feldspars. According to Wikipedia:

“Feldspars are a group of rock-forming aluminum tectosilicate minerals containing sodium, calcium, potassium, or barium. The most common members of the feldspar group are the plagioclase (sodium-calcium) feldspars and the alkali (potassium-sodium) feldspars. Feldspars make up about 60% of the Earth’s crust and 41% of the Earth’s continental crust by weight.”

Given that feldspars are relatively common, we are frequently asked to help index them. They are difficult, as a poster at the 2019 Quantitative Microanalysis (QMA) conference detailed [1]. I thought it might be interesting to see what we could learn about the limits of EBSD in characterizing these materials. I won’t give you all that we learned in that little study, but what I thought was an interesting snapshot. Figure 1 shows a phase diagram for the feldspar group of minerals.

Figure 1. Phase diagram for the feldspar group.

To start, I looked in the American Mineralogist Crystal Structure Database (AMCSD) for all the relevant entries I could find. There are a lot of variants. Here is a table:

Table 1. Number of entries in AMCSD for each feldspar.

I enjoy seeing pattern simulation results, but producing 149 master patterns [2] would take more patience than I have (each master pattern calculation can take several hours for these low-symmetry materials). So, I selected one entry for each mineral type. I tried to find one that seemed most representative of all the other entries in the set. After calculating the eight master patterns, I simulated one individual pattern at the same orientation for each mineral, as shown in Figure 2. Note that they are all similar, with the most deviation coming from the anorthite and sanidine end members of the series.

Figure 2. Patterns were simulated at Euler angles of (30°, 30°, 30°) for each feldspar.

To quantify the differences, I calculated the normalized dot-products [3] for all pattern pairs to get the following table. A value of “1” indicates the patterns are identical. As expected by the initial observation, the biggest difference is the sanidine to albite pair of patterns.

Table 2. Normalized dot products.

Of course, the next step would be to see how this holds up to experimental patterns and dictionary indexing [4]. I hope to eventually do this with samples Professor Rudy Wenk of Stanford University kindly gave me. Rudy has been one of the major contributors to the entries in the AMCSD for feldspars.

There was one more virtual experiment I thought would be interesting. I wanted to ascertain how much the chemical species in the feldspar series influenced the patterns. To do this, I created an average structure instead of using the lattice parameters for each feldspar. I then populated these structures with atoms to maintain the chemical composition ratios specified for each series. A master pattern for each ideal structure was calculated. Three hundred forty patterns were simulated uniformly, covering orientation space with a spacing of approximately 30° between orientations. The average normalized dot products were calculated for each pattern against the albite pattern at the same orientation. Figure 3 shows the results.

Figure 3. The normalized dot product of simulated patterns for idealized structures against the albite simulated patterns.

Clearly, the dot products are all very near 1, indicating that the differences in the simulated patterns due to chemical composition are small for these chemical species. This suggests that coupling EBSD with EDS is critical when trying to differentiate the different feldspar minerals. While this small study has not changed the world of feldspar indexing, it has, at least, been a stimulating study of simulating for me.

[1] B Schneider, and J Fournelle (2019) “Using Quantitative and Qualitative Analysis to Confirm Phase Identities for Large Area EBSD Mapping of Geological Thin Sections” Poster at Microanalysis Society Topical Conference: Quantitative Microanalysis, University of Minnesota, Minneapolis MN, June 2019.

[2] PG Callahan, and M De Graef (2013) “Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations” Microscopy and Microanalysis, 19, 1255-1265.

[3] S Singh, and M De Graef (2016) “Orientation sampling for dictionary-based diffraction pattern indexing methods” Modelling and Simulation in Materials Science and Engineering, 24, 085013.

[4] K Marquardt, M De Graef, S Singh, H Marquardt, A Rosenthal, and S Koizuimi (2019) “Quantitative electron backscatter diffraction (EBSD) data analyses using the dictionary indexing (DI) approach: Overcoming indexing difficulties on geological materials” American Mineralogist: Journal of Earth and Planetary Materials, 102, 1843-1855.

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!

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

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

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

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

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

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

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

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

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

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

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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!

Teaching is learning

Dr. René de Kloe, Applications Specialist, EDAX

Figure 1. Participants of my first EBSD training course in Grenoble in 2001.

Everybody is learning all the time. You start as a child at home and later in school and that never ends. In your professional career you will learn on the job and sometimes you will get the opportunity to get a dedicated training on some aspect of your work. I am fortunate that my job at EDAX involves a bit of this type of training for our customers interested in EBSD. Somehow, I have already found myself teaching for a long time without really aiming for it. Already as a teenager when I worked at a small local television station in The Netherlands I used to teach the technical things related to making television programs like handling cameras, lighting, editing – basically everything just as long as it was out of the spotlight. Then during my geology study, I assisted in teaching students a variety of subjects ranging from palaeontology to physics and geological fieldwork in the Spanish Pyrenees. So, unsurprisingly, shortly after joining EDAX in 2001 when I was supposed to simply participate in an introductory EBSD course (fig 1) taught by Dr. Stuart Wright in Grenoble, France, I quickly found myself explaining things to the other participants instead of just listening.

Teaching about EBSD often begins when I do a presentation or demonstration for someone new to the technique. And the capabilities of EBSD are such that just listing the technical specifications of an EBSD system to a new customer does not do it justice. Later when a system has been installed I meet the customers again for the dedicated training courses and workshops that we organise and participate in all over the world.

Figure 2. EBSD IPF map of Al kitchen foil collected without any additional specimen preparation. The colour-coding illustrates the extreme deformation by rolling.

In such presentations, of course we talk about the basics of the method and the characteristics of the EDAX systems, but then it always moves on to how it can help understand the materials and processes that the customer is working with. There, teaching starts working the other way as well. With every customer visit I learn something more about the physical world around us. Sometimes this is about a fundamental understanding of a physical process that I have never even heard of.

At other times it is about ordinary items that we see or use in our daily lives such as aluminium kitchen foil, glass panes with special coatings, or the structure of biological materials like eggs, bone, or shells. Aluminium foil is a beautiful material that is readily available in most labs and I use it occasionally to show EBSD grain and texture analysis when I do not have a suitable polished sample with me (fig 2) and at some point, a customer explained to me in detail how it was produced in a double layer back to back to get one shiny and one matte side. And that explained why it produces EBSD patterns without any additional preparation. Something new learned again.

Figure 3. IPF map of austenitic steel microstructure prepared by additive manufacturing.

A relatively new development is additive manufacturing or 3D printing where a precursor powdered material is melted into place by a laser to create complex components/shapes as a single piece. This method produces fantastically intricate structures (fig 3) that need to be studied to optimise the processing.

With every new application my mind starts turning to identify specific functions in the software that would be especially relevant to its understanding. In some cases, this then turns into a collaborative effort to produce scientific publications on a wide variety of subjects e.g. on zeolite pore structures (1, fig (4)), poly-GeSi films (2, fig (5)), or directional solidification by biomineralization of mollusc shells (3).

Figure 4. Figure taken from ref.1 showing EBSD analysis of zeolite crystals.

Figure 5. Figure taken from ref.2 showing laser crystallised GeSi layer on substrate.

Such collaborations continuously spark my curiosity and it is because of these kinds of discussions that after 17 years I am still fascinated with the EBSD technique and its applications.

This fascination also shows during the EBSD operator schools that I teach. The teaching materials that I use slowly evolve with time as the systems change, but still the courses are not simply repetitions. Each time customers bring their own materials and experiences that we use to show the applications and discuss best practices. I feel that it is true that you only really learn how to do something when you teach it.

This variation in applications often enables me to fully show the extent of the analytical capabilities in the OIM Analysis™ software and that is something that often gets lost in the years after a system has been installed. I have seen many times that when a new system is installed, the users invest a lot of time and effort in getting familiar with the system in order to get the most out of it. However, with time the staff that has been originally trained on the equipment moves on and new people are introduced to electron microscopy and all that comes with it. The original users then train their successor in the use of the system and inevitably something is lost at this point.

When you are highly familiar with performing your own analysis, you tend to focus on the bits of the software and settings that you need to perform your analysis. The bits that you do not use fade away and are not taught to the new user. This is something that I see regularly during the training course that I teach. Of course, there are the new functions that have been implemented in the software that users have not seen before, but people who have been using the system for years and are very familiar with the general operation always find new ways of doing things and discover new functions that could have helped them with past projects during the training courses. During the latest EBSD course in Germany in September a participant from a site where they have had EBSD for many years remarked that he was going to recommend coming to a course to his colleagues who have been using the system for a long time as he had found that the system could do much more than he had imagined.

You learn something new every day.

1) J Am Chem Soc. 2008 Oct 15;130(41):13516-7. doi: 10.1021/ja8048767. Epub 2008 Sep 19.
2) ECS Journal of Solid State Science and Technology, 1 (6) P263-P268 (2012)
3) Adv Mater. 2018 Sep 21:e1803855. doi: 10.1002/adma.201803855. [Epub ahead of print]

Endless Summer

Matt Nowell, EBSD Product Manager, EDAX

My family and I love the beach. We love to swim in the water, ride the waves, and play in the sand. Each summer we typically spend time at Sunset Beach, North Carolina. After years of seeing the cool stuff in the SEM, materials science and microscopy are always topics of discussion. This year, after enjoying the musical Hamilton, my wife was inspired to start working on a periodic table of elements rap song. My 13-year-old learned more about metalworking watching the History Channel show, Forged in Fire, where participants are challenged to make different weapons from assorted metallic sources. My favorite part was watching them evaluate different parts of a bicycle for heat-treatable steel to recycle. One of my favorite moments though was unpacking my beach shoes on the first day.

Generally, when we visit a beach, we try to bring home a shell or a piece of driftwood. However, when I was putting on my shoes for the first time, I noticed some sand was still present. My last beach trip had been to the Cayman Islands. I immediately noticed that this sand looked much different than the sand at Sunset Beach. I decided to save a little bit of each for some microscopy and microanalysis when I got back home.

When I looked at them both more closely, I saw that the sand from Sunset Beach (SB) on the left was much darker with black flecks, while the sand from Grand Cayman (GC) was much lighter. Thinking about the possible composition of the sand got me thinking about the bladesmithing competition held at the TMS annual meetings. One year, the team from UC Berkeley created a sword using magnetite found at local beaches using magnets. I thought it would be interesting to examine both of these sands with my SEM, EDS, and EBSD tools.


Sand grains from Sunset Beach.

Sand grains from Grand Cayman.

 

Initially I placed a bit of sand on an aluminum stub for SEM and EDS analysis. To reduce charging effects, I used the Low Vacuum capability of our FEI Teneo FEG-SEM, running at 0.1 mbar pressure. Images were collected using the Annular BackScatter (ABS) detector for atomic number contrast imaging. The sand grains from Sunset Beach were generally a little smaller than the Grand Cayman sand, as expected from visual inspection. Both sands exhibited cracking and weathering, which isn’t surprising in hindsight either. Many grains show flat surfaces, with internal structure visible with ABS imaging contrast.

I followed the imaging work with compositional analysis using EDS. The Sunset Beach sand was primarily composed of silicon and oxygen grains, which I suspect is quartz. The single brighter grain in Figure 3 was composed of an iron-titanium oxide. The Grand Cayman sand was primarily a calcium carbonate (Ca-C-O) material. The more needle shaped grains were primarily sodium and chlorine, which I assume is then salt that has solidified during the evaporation of the water. All this leads me to believe I really didn’t do a good job of cleaning my shoes after Grand Cayman.

While quartz being present in sand wasn’t surprising to me, the observation of calcium carbonate did remind me of some geological homework I did on the island. The water in Grand Cayman was very clear, which made it great for snorkeling. We swam around and saw a coral reef, a sunken ship, lots of fish, and stingrays. To understand why the water was so clear, I read that it was the lack of topsoil, and the erosion and runoff of topsail to the water that was responsible for the clarity. Looking again at this reference, it mentions that the top layer of the island is primarily composed of carbonates. The erosion of this material would explain the primary composition of the beach sand in my shoes.

Of course, the next step now is analyzing these sands with EBSD to determine the crystal structure of the materials. I’ve started the process. I’ve mounted some of the sand in epoxy, and hand polished to get some flat surfaces for analysis. I’m able to get EBSD patterns, but getting a good background is going to be tricky. I think the next step will be to watch my colleague Shawn Wallace’s webinar on Optimizing Backgrounds on MultiPhase samples to be presented on September 27th. You can also register for this here.

In the meantime, I’ll keep the sand samples on my desk to remind me of summer as the colder Utah winters will be approaching. It will be a good reason to stay inside and write the next chapter of this analysis for another blog post.

Vacationing Between a Rock and a Hard Place?

Shawn Wallace, Applications Engineer, EDAX.

One of the perks of both my degree (Geology) and my current job is that I have travelled extensively. In all those travels, I had been to 47 of the 48 contiguous US States, with Maine being the missing one. This year, I decided to be selfish and dragged the family to Maine on vacation, so I that could tick off the final one.

Being a member of the Wallace family means vacation is a time for strenuous hikes and beating on rocks to unlock their inner goodies, to add to our ever growing rock and mineral collection. This vacation was no different. Maine is home to some of the best studied and known Pegmatites, and they quickly became our goal. Pegmatites are neat for a several reasons, the main two being that they tend to form giant crystals (a 19 foot long Beryl found in Maine) and weird minerals in general tend to form in them.

I was able to track down some publicly accessible sites, found a lovely home base to rent for the week, and we set off for a week long rockhounding adventure. Ok not all week. We took a couple days off to go swimming, as it got up over 90F (>32C).

Figure 1. Dendrites cover this massive feldspar sample on nearly all faces.

Our first stops yielded the usual kind of rocks I was expecting, but another site did not. There we found dendrites everywhere. The rock itself is a massive feldspar (Fig. 1). You can see that most of the dendrites nucleate at the edge of a fracture surface and then do their fractal thing on the surface itself. Wanting to better understand the sample, I started searching for previous EBSD work on geological dendrites. While a lot exists in the metals world, very little exists in the geological world. To me, this means I have work to do. Let’s see what I can do to get some useful data on this sample!

P.S. I have Alaska and Hawaii to go. Who needs an onsite training in those states? 😉