Materials

Turning quantification of lithium from days to minutes of work

Dr. Shangshang Mu, Applications Engineer, Gatan/EDAX

Cipher®, the quantitative analysis of lithium system, is a shining example of the synergies brought about by the merger between Gatan and EDAX. As an application specialist involved since the beginning of this project, witnessing the evolution of the data acquisition and analysis workflow is nothing short of astounding. I vividly recall those initial moments when we tested this concept and generated our first Li measurements from an actual sample.

I conducted energy dispersive x-ray spectroscopy (EDS) data acquisition and analysis in the EDAX APEX™ software during those early stages. At the same time, my colleague focused on the quantitative backscattered electron (qBSE) work within the DigitalMicrograph® software. To analyze the lithium content in a sample from just a few locations was a painstaking process requiring the laborious process of correlating information from disparate software programs manually, checking again and again that the same area of the sample was being analyzed, and then calculating by hand the lithium content of an analysis location using a variety of different mathematical models to determine the best one.

With the release of DigitalMicrograph 3.6.0, the entire data acquisition and analysis workflow unfolds seamlessly, marking a significant advancement in efficiency and user-friendliness, not to mention making my job so much easier! A guided workflow allows a user to conduct the whole experiment using a single software package. Using the Technique Manager, data acquisition and analysis happen step-by-step as you progress from the top palette to the bottom (Figure 1).

Li quantification-related palettes within the DigitalMicrograph Technique Manager panel.

Figure 1. Li quantification-related palettes within the DigitalMicrograph Technique Manager panel.

Several steps used to be challenging experimentally, which the software now manages for you, including:

  • Ensuring that the backscattered electron signal was calibrated by atomic number (Z) and, importantly, that there were no changes to the calibration when moving between samples
  • That data that was captured sequentially could be aligned and transformed before the lithium content being calculated
  • Use of the latest models for qBSE and EDS analysis methods

For the first challenge, appropriate Z-standards are required, and the detector settings and collection geometry must remain constant between qBSE measurements. The qBSE Calibration palette (Figure 2) provides intuitive guidance through this essential process, and when using the Z-standards provided with the system, what used to take an hour or more to complete can now be done in minutes. The buttons of the qBSE calibration palette guide you through the detector setup and measurement of the Z reference samples, populating the calibration table as you go. A calibration curve can be plotted for your reference once a minimum of four reference values are acquired. Vitally, the software continuously verifies that you are at the correct working distance for qBSE. If a measurement is attempted using incorrect conditions, qBSE data cannot be generated. Furthermore, the QuickSet button becomes active, allowing the user to launch a wizard that returns the system to the appropriate conditions for qBSE analysis. This has proven invaluable for many of the customer specimens I have analyzed, as they come in all shapes and sizes!

Figure 2. qBSE Calibration palette and an example of the calibration curve used for converting BSE signals measure to atomic number.

For samples analyzed in the SEM, DigitalMicrograph 3.6 now uses the same standardless EDAX eZAF method for analysis as APEX EDS Advanced software, enabling quantified EDS measurements to be performed reliably in the same software program as used for qBSE data collection. However, to ensure that the analyzed volume is consistent between the two methods, we typically collect data for the two signals at different accelerating voltages. Previously (e.g., [1]), the complexity of registering and aligning the qBSE and EDS data was too challenging to even attempt to map the lithium distribution, with researchers instead choosing to analyze a few isolated points only.

The Cipher Analysis palette (Figure 3) simplifies the process of correlating EDS and qBSE datasets like never before, enabling lithium content to be mapped over a 2D area or 1D line scan in addition to point analyses. By simply selecting the BSE and EDS workspaces from the dropdowns and clicking on the Align button, qBSE and EDS data captured under different conditions will be automatically registered and aligned using the corresponding secondary electron images; this alignment procedure even works if the qBSE and EDS data is captured at different magnifications or pixel density.

Figure 3. Cipher Analysis palette.

Subsequently, pressing Map Low Z will generate Li maps effortlessly using the latest algorithms in EDS and qBSE analysis (Figure 4), adjusting the original elemental maps to include the Li content.

Figure 4. Lithium map (in atomic percent) of a nickel manganese cobalt oxide (NMC) sample commonly used as a cathode material in the construction of lithium-ion batteries.

Looking ahead, the streamlined workflow in DigitalMicrograph and the continued evolution of Cipher promises to revolutionize lithium analysis, empowering researchers with unprecedented insights into battery technology, energy storage, and many other fields. I’m excited to be able to be involved with the development and release of a product that turns what was once impossible into a straightforward experiment.

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.

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.

EBSD in a vacuum

Dr. Stuart Wright, Senior Scientist, Gatan/EDAX

I recently co-authored a paper with my colleagues Will Lenthe and Matt Nowell that focused on our parent grain reconstruction tool in OIM Analysis™ [1]. As part of that paper, we show the results from a little round-robin we did. I also showed some results in my webinar on parent microstructure reconstruction in January 2021.

Participating in a round-robin is always a bit unnerving as you are never completely sure how your work will stand up relative to others – especially for those well-recognized experts. This was not an officially moderated round-robin, but rather, me asking other researchers in the area that I happen to have had the good fortune of interacting with in the past if they would be willing to contribute. For the round-robin, the same input EBSD dataset was used for each algorithm. The EBSD dataset was obtained from a low-carbon steel rolled-sheet sample with a fully transformed ferrite body-centered cubic (bcc) microstructure, as shown in Figure 1.

Figure 1. a) Crystal orientation (IPF) map for a ferrite microstructure in a low carbon steel, b) color scheme for the IPF map.

This dataset was used as the input to the parent reconstruction tool in OIM Analysis, as well as several other reconstruction tools. Figure 2 shows the reconstruction results.

Figure 2. IPF Maps of the parent austenite microstructure reconstructed using a) OIM Analysis [2], b) Merengue [3], c) Graph Cutting [4] and d) ROPA [5].

Generally, the results are in reasonable agreement, e.g., the grain sizes and orientations (colors) are in general agreement. The results suggest that if these algorithms were applied to the input dataset obtained from a larger area, then the textures and grain size statistics would all be expected to be quite similar. The differences tend to be in the details, particularly at the boundaries between grains. Our paper discusses some of the nuances of the different algorithms that lead to the differences in reconstruction results.

In the paper, we briefly acknowledge each of those who were kind enough to provide us with the reconstruction results using the different algorithms. However, I want to add a little more detail about the contributors.

The original dataset came to me via Stephen Cluff when he was a Ph.D. researcher in Professor David Fullwood’s group at Brigham Young University working on austenite reconstruction (https://scholarsarchive.byu.edu/etd/9051/). Stephen is now a Materials Engineer at the U.S. Army Research Lab.

The original dataset was collected and shared by Matt Merwin at U. S. Steel. Matt and I co-organized a symposium on EBSD analysis of steel for the 2009 TMS meeting.

The dataset was used in a paper by Chasen Ranger and co-workers on austenite reconstruction (Ranger, C., Tari, V., Farjami, S., Merwin, M.J., Germain, L. and Rollett, A., 2018. Austenite reconstruction elucidates prior grain size dependence of toughness in a low alloy steel. Metallurgical and Materials Transactions A, 49, pp.4521-4535.).

Anthony Rollett is the last author on this paper. I have known Tony for many years – he was my ‘boss’ in my first job out of school as a post-doc at Los Alamos National Lab and is now the U.S. Steel Professor of Metallurgical Engineering and Materials Science at Carnegie Mellon University. I reached out to Tony for data from the paper, and he kindly supplied reconstruction results on the austenite dataset obtained using Lionel Germain’s Merengue code. Lionel is at the University of Lorraine in France, which is where the next ICOTOM will be held (https://icotom20.sciencesconf.org/).

I saw a presentation on parent reconstruction using Graph Cutting by Stephen Niezgoda of Ohio State University (OSU), so I asked if he would apply his algorithm to this dataset. He kindly responded, and his student Charles Xu supplied me with the results from their algorithm. I have known Stephen for many years and have had the opportunity to visit his research group at OSU.

I was also aware of an algorithm from Goro Miyamoto called ROPA. I asked my Japanese colleagues Seichii Suzuki and Tatsuya Fukino of TSL Solutions KK, who are familiar with the ROPA software, if they would run the same dataset, and they kindly obliged. I’ve had the good fortune of enjoying many trips to Japan to visit with my colleagues at TSL Solutions and had the opportunity to host them in Utah.

Why the shameless “name-dropping”?

First, it is good to see the agreement between the different algorithmic approaches to the reconstruction problem. While there are certainly differences between the results, the overall reconstructed microstructures are quite similar.
Second, I have interacted with many of these researchers through a shared interest in EBSD and personal connections that started during my graduate school research under Professor Brent L. Adams. I did a Master of Science degree at BYU and a Ph.D. at Yale University under Brent’s guidance, both of which focused on EBSD. Many of the researchers listed here have worked and published with Brent Adams.

So, my second point is to emphasize that while EBSD is performed in a vacuum – science is much more fruitful and enjoyable when not performed in a vacuum. The connections we build through our interactions with others in the research community are essential to moving science forward – it is good to attend conferences again after the COVID-enforced hiatus.

References

  1. Wright, S.I., Lenthe, W.C., Nowell, M.M. Parent Grain Reconstruction in an Additive Manufactured Titanium Alloy, Metals, 2023, 14, 51. DOI: https://doi.org/10.3390/met14010051.
  2. Ranger, C.; Tari, V.; Farjami, S.; Merwin, M.J.; Germain, L.; Rollett, A. Austenite reconstruction elucidates prior grain size dependence of toughness in a low alloy steel. Metall Mater Trans A 2018, 49, 4521-4535. DOI: https://doi.org/10.1007/s11661-018-4825-7.
  3. Germain, L.; Gey, N.; Mercier, R.; Blaineau, P.; Humbert, M. An advanced approach to reconstructing parent orientation maps in the case of approximate orientation relations: Application to steels. Acta Mater 2012, 60, 4551-4562. DOI: https://doi.org/10.1016/j.actamat.2012.04.034.
  4. Brust, A.; Payton, E.; Hobbs, T., Sinha, V.; Yardley, V.; Niezgoda, S. Probabilistic reconstruction of austenite microstructure from electron backscatter diffraction observations of martensite. Microsc Microanal 2021, 27, 1035-1055. DOI: https://doi.org/10.1017/S1431927621012484.
  5. Miyamoto, G.; Iwata, N.; Takayama, N.; Furuhara, T. Mapping the parent austenite orientation reconstructed from the orientation of martensite by EBSD and its application to ausformed martensite. Acta Mater 2010, 58, 6393-6403. DOI: https://doi.org/10.1016/j.actamat.2010.08.001.

DIY grain growth modeling

Matt Nowell, EBSD Product Manager, Gatan/EDAX

My son Parker graduated with a degree in materials science and engineering last May, and we are fortunate to enjoy discussing materials, microstructures, and characterization together as a shared interest. About a month ago, he sent me a video of someone showing a 2D-grain growth example using BBs moving between two pieces of plexiglass. He expressed an interest in trying to do this together. During his recent visit home during the holiday season, we tried to replicate this work.

To build this, we decided to make the plexiglass casing using the 3D printer we have at home. I purchased this years ago to encourage my boys to learn about technology and because of my interest in additive manufacturing. While I’m used to analyzing 3D printed metallic materials with electron backscatter diffraction (EBSD), we printed using a polylactic acid (PLA) filament, a recyclable thermoplastic.

We had to make a 3D drawing of our design. I haven’t done 3D CAD work in a long time, but we were able to hack a base and a lid design together in Blender. This lid is shown in Figure 1.

Figure 1. 3D model of the lid of our design.

Printing wasn’t as easy as I had hoped, but it was a learning experience. We learned that our printer is better if printing directly from an SD card rather than communicating with a PC. We learned you shouldn’t keep PLA filament for years, as it becomes brittle and breaks during long prints. We learned that our printer had a maximum printing size close to our design’s dimensions. We learned that sometimes, when upgrading the firmware and software to fix a problem, it will introduce new issues that then need to be resolved. In the end, though, and after a few iterations, we were able to print a working design. Figure 2 shows the printing of the plexiglass frame. And yes, my 3D printer is made by AnyCubic, which seems appropriate for my EBSD interests.


Figure 2. The printing of the plexiglass frame.

Once printed, assembled, and filled with BBs, you can set this model on its side, and the BBs arrange themselves in a 2D lattice arrangement, as shown in Figure 3. Figure 3a shows the initial distribution. Some areas are organized into ordered regions, which are analogous to 2D grains. Some stacking defects are also observed within some of these grains. There are also regions that are not ordered, which would be comparable to amorphous materials.

Figure 3. a) The initial distribution and b-d) the evolution of the 2D model structure as energy is input into the system through tapping and shaking the model.

We then proceeded to tap and shake the model gently. This is essentially input energy into the system, like what thermal energy would be in a real material. Figures 3b-3d show the evolution of the 2D model structure. Grains coalesce and then grow. Eventually, only a few grains remain, with some twinning-like defects present. A video of this process is shown in Figure 4.


Figure 4. Eventually, only a few grains remain, with some twinning-like defects present.

Of course, this model isn’t perfect, and we will continue to spend some time working with it. It’s easy to get templated growth from the sides aligning the BBs, and we have to be extra careful not to spill them everywhere, or we will both be in trouble. It does remind me, though, of the in-situ grain growth experiments I’ve done with EBSD. Figure 5 shows a video of an orientation map of recrystallization and grain growth in aluminum.


Figure 5. A video of an orientation map of recrystallization and grain growth in aluminum.

I like how models can help us understand the physical phenomena that are actually occurring, and I like being able to discuss them with Parker.

Sometimes, you don’t know what you’ve been missing until you find it

Dr. Leslie O’Brien, SEM Manager, Lehigh University – Institute for Functional Materials and Devices

As a manager of an electron microscopy facility with a dozen instruments and a diverse user base, we often find ourselves heeding the adage, “If it ain’t broke, don’t fix it,” particularly when it comes to the ever-evolving field of energy dispersive x-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD) software. With many instruments to operate and maintain, priorities and funding can shift unexpectedly. Upgrading EDS/EBSD software will likely get pushed to the back burner, especially when there is nothing functionally wrong with our version.

We recently had the opportunity to upgrade the EDAX computer on our focused ion beam (FIB) from TEAM™ to the new APEX™ software. The FIB does a substantial amount of EBSD work, with lesser EDS, and is one of our facility’s busiest instruments among academic and industry users. Of course, sometimes, with progress comes resistance! Users become comfortable and proficient with software or hardware and are frustrated or reluctant about spending the time and energy to learn something new.

Figure 1. EDAX EDS and EBSD systems running APEX software in the SEM lab in the Institute for Functional Materials and Devices at Lehigh University.

The transition from TEAM to APEX was, for the most part, an easy one. APEX has much of the same fundamental functionality of TEAM, with some nice additions, only minor restructuring, and an updated user interface (UI) that was a welcome sight.

Our facility serves researchers across all disciplines with various levels of analytical experience. We provide a mix of paid service research and hands-on training for users wanting to develop their own microscopy skill set. I have found that APEX’s updated, user-friendly interface has made the training aspect easier for both the teacher and the student. We can focus on the fundamentals of EBSD and EDS analysis as well as the specifics of each individual’s analytical goals without being bogged down or distracted by a clunky UI.

APEX Review mode is also quite popular with the user base. Our facility does charge user fees, so anything that makes someone’s instrument time more efficient without compromising the quality of their data is a big positive. We do quite a bit of EBSD and EDS mapping, and being able to process existing data or generate reports while new data is being collected simultaneously adds value to the time and money spent sitting and working at the FIB. Another simple yet valuable feature we appreciate is being given an estimated EDS map time before you start the map.

There has been positive feedback from users who conduct EBSD analyses regarding integrating EDAX OIM Analysis™ with the APEX software. Taking an APEX EBSD dataset and opening it in OIM Analysis to process the data is much more efficient than using the TEAM software. When it comes to EBSD, we want to ensure that we are operating the system carefully so as not to damage the camera. I prefer the separate software icons for EDS, EBSD, or Suite in APEX over the combined software of TEAM. This helps to ensure that a distracted user who is solely there for EDS doesn’t accidentally insert the EBSD camera – it happens.

All of this has made for a more streamlined approach to data collection, data analysis, and report generation on the FIB. The upgrade to APEX has allowed us to continue to produce quality data with improved efficiency in a high-throughput environment. It’s just something we didn’t realize we needed until we had it!

Semper Fi

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

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

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

Figure 1. Captain Ralph Jim Chipman.

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

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

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

Semper Fi!

Being more precise

Dr. Stuart Wright, Senior Scientist, Gatan/EDAX

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

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

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

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

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

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

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

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

[1] Harland CJ, Akhter P, Venables JA (1981) Accurate microcrystallography at high spatial resolution using electron backscattering patterns in a field emission gun scanning electron microscope. Journal of Physics E 14:175-182
[2] Dingley DJ (1981) A Comparison of Diffraction Techniques for the SEM. Scanning Electron Microscopy IV: 273-286
[3] Wright SI (1992) Individual Lattice Orientation Measurements Development and Applications of a Fully Automatic Technique. Ph.D. Thesis., Yale University.
[4] Krieger Lassen NC (1994) Automated Determination of Crystal Orientations from Electron Backscattering Patterns. Ph.D. Thesis, Danmarks Tekniske Universitet.
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Reaching Out

Dr. René de Kloe, Applications Scientist, EDAX

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

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

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

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

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

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

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

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

This was no thermocouple.

Figure 4. The element distribution in the sensor.

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

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

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

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

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

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

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

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

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

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

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

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

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

I wish everybody a very happy and peaceful 2023.

Simulate them!

Dr. Chang Lu, Application Specialist, Gatan & EDAX

In early 2022, Gatan and EDAX completed the integration, and our new division was named Electron Microscope Technology (EMT). As an EMT application scientist on the China applications team, I am responsible for almost all the Gatan and EDAX products for Northern China, on both Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) platforms. Therefore, I work with diversified products and diversified user groups that focus on different subject matters. In the first half of this year, I found that the data analysis software from EMT Gatan’s DigitalMicrograph® (DM) and EDAX’s OIM Analysis™ are not completely isolated, but in many cases, they can cooperate with each other to help our customers.

For instance, DM can do a series of electron microscopy-related data processing. For some energy dispersive spectroscopy (EDS) mapping data from the minor content, there are various methods to achieve smoothing and enhance the contrast. While in the MSA panel, the principal component analysis (PCA) function can be helpful in terms of high-resolution EDS mapping. However, in today’s EDAX blog, I will talk a little bit more about one feature in OIM Analysis that could potentially benefit a lot of Gatan camera users.

In northern China, there are a group of Gatan users who are focused on nanoscale phases and grains in the TEM. In most scenarios, they heavily employ electron diffraction or bright field imaging to make judgments. However, it is really difficult to determine the unknown (unidentified but has a known x-ray diffraction (XRD) pattern and chemical composition, so there is a potential for it) phase by simply relying on the minor changes of grayscale bright field images. You may say diffraction could help. Yes, a clean, beautiful diffractogram of a particular crystal direction can be helpful. But, no, you need to find the zone axis carefully. If this unknown phase has a crystal structure of low symmetry, most of the time, the effort will be in vain. Generally speaking, the Difpack tool in the DM software could help in determining d-spacing and angles, however, it is not intuitive enough to know the sample at first sight.

The solution is pattern simulation with OIM Matrix™. At first, I noticed this feature because it helped an EDAX user who was studying strains. It can easily export a theoretical Kikuchi pattern for a specific sample orientation with zero stress. Then one day, I had a sudden thought during my morning shower. Maybe I can change the acceleration voltage to 200 kV (typical for TEM), and the sample tilt angle to 0° (make it flat). After entering a specific orientation, we can get a Kikuchi pattern under TEM conditions! For example, take the simulated pattern from NdCeB. With Kinematic Color Overlay, we can also find out what crystal plane corresponds to a specific Kikuchi line. Now, when we start changing the zone axis in an unidentified sample, we can first simulate several orientations and compare them with what we see under TEM. In this way, the process of finding the Kikuchi pole turns out to be very convenient.

Figure 1. A simulated pattern from NdCeB using OIM Matrix.

Now, when some Gatan users bring in some “weird” unidentified samples and say they want to find various zone axis for doing diffractions. I don’t worry about it. I think from a problem-solving point of view, the powerful software from both Gatan and EDAX, like the integration of two companies, can also be combined to solve complex and difficult problems for our customers in the future.

模拟他们！

Dr. Chang Lu, Application Scientist, Gatan & EDAX

2022年初，Gatan和EDAX这两家公司完成了整合，我们的部门更名为Electron Microscope Technology (EMT)。作为EMT中国团队的应用技术支持，我负责中国北方区扫描电镜（SEM）还有透射电镜（TEM）上Gatan还有EDAX的几乎全部产品。产品多样，服务的用户群体也多样。慢慢地在工作过程中，我就发现两边的数据分析软件（Gatan的Digital Micrograph以及EDAX的Orientation Imaging Microscopy）其实并不是完全分开的，很多时候它们可以在不同程度上相互支持，互通互联。

比如说，Gatan公司在TEM平台上知名的DigitalMicrograph（DM）软件。DM可以处理一系列的电镜相关的表征数据。对于部分信号较弱的EDAX能谱结果，我们也可以把数据载入，通过DM内置的图像平滑，互相关算法以及主成分分析（PCA）进行数据的优化处理。但是我今天更想要分享的是EDAX Orientation Imaging Microscopy（OIM）软件在TEM平台上的一些应用。

在我服务的客户群体中，有一些用户尤其关注一些纳米级的物相和晶粒。这时候在TEM平台，我们往往需要使用到电子衍射的手段来对样品进行判断。然而，很多时候单单依靠透射电镜下灰度值衬度的变化，一些微弱的物相的改变很难被发觉。同时，一张干净，漂亮的来自特定晶向的电子衍射，往往需要我们对样品旋转晶带轴。虽然我们可以沿着特定的菊池线去找菊池极，但是这条菊池线对应什么晶面？菊池极对应哪个晶带轴？我们往往需要在Difpack工具里面标定晶格间距和角度，再比对样品材料不同晶面和夹角才有可能清楚，这很麻烦。

我第一次注意到OIM中的Pattern Simulation功能是因为帮助某个研究应力应变的老师输出无应力的特定取向的标准菊池花样图。然后我注意到，其实我是可以对应更改电镜的加速电压，样品倾斜角度还有取向来得到一系列的菊池花样，比如下图这个200 kV加速电压下得到的NdCeB 样品的模拟花样。我们可以对左下角的orientation参数进行修改，得到一系列模拟出来的菊池花样。通过Kinematic Color Overlay，我们还可以知道对应的菊池线对应的是什么晶面。现在，当我们处于未知状态开始转晶带轴的时候我都会首先模拟几个相似的取向，进行对比。这样一来，我沿着那个晶面（菊池线）在找哪个晶带轴（菊池极）都变得异常清楚，这非常方便。

图 1. NdCeB 使用 OIM Matrix。

现在，当Gatan的用户再拿来一个“奇奇怪怪”的样品说要找到特定晶带轴做衍射，我也不像以前担心怎么搞了。需要什么，我们就在EDAX OIM中先模拟出来一个，对着这个模拟的图，旋转，调整晶面，然后在TEM电子衍射过程中去找。我想从解决问题的角度来讲，Gatan和EDAX丰富的软件资源，就像我们这两家公司的合并一样，未来也可以合并解决客户复杂和困难的问题。