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!

A fusion of excellence: the thrilling synergy of Gatan and EDAX in our merged company, advancing science in Central and Eastern Europe

Rudolf Krentik, Direct Sales and Distributor Manager CEE, Gatan/EDAX

In electron microscopy, precision and insight are the bedrock of scientific discovery. When Gatan, a company specializing in transmission electron microscopy (TEM), and EDAX, a leader in analytical scanning electron microscopy techniques, including energy dispersive x-ray spectroscopy (EDS), wavelength dispersive spectroscopy (WDS), and electron backscatter diffraction (EBSD) decided to merge, it created a unique and exciting environment. This is the story of how the merger of these two renowned companies changed the game, particularly how it transformed the landscape for scientists in Central and Eastern Europe (CEE), where I took a role as sales manager.

A symphony of expertise

Gatan brought its unparalleled knowledge of high-resolution TEM imaging, allowing scientists to scrutinize samples at the atomic level. On the other hand, EDAX excelled in SEM, capturing fine details while analyzing elemental composition. The merger was a meeting of minds and machines, combining the best of both worlds.

The power of integration

The fusion of Gatan and EDAX under one roof unleashed a wave of possibilities for scientists in CEE. Researchers, scientists, and engineers now have access to an unprecedented range of imaging and analytical capabilities. From exploring the innermost structure of nanomaterials with TEM to revealing the intricate topography of surfaces with SEM and conducting precise elemental analysis with EDS-WDS, the comprehensive suite of tools is a game-changer for those pushing the boundaries of science and technology in the region.

A new playground for discovery in CEE

The exciting environment that emerged from the merger has created a palpable synergy, which is especially beneficial to scientists in CEE. It’s not just about the advanced hardware but the convergence of ideas, collaboration, and innovation. Scientists in CEE are now working on projects that seamlessly transition between TEM, SEM, and EDS, gaining holistic insights that were previously unimaginable.

Whether it’s delving into the intricate lattice structures of advanced materials, meticulously examining the surface features of biological specimens, or identifying the elemental composition of a sample, the combined expertise and equipment offer the ideal platform for exploration. It’s no longer about choosing between TEM and SEM; it’s about having the best of both worlds for comprehensive analysis.

The impact on research and industry in CEE

The implications of this merger extend beyond the lab and profoundly affect research and industry in CEE. The seamless integration of TEM, SEM, and EDS accelerates research, product development, and quality control across various sectors.

One example is from the automotive industry. The fast-growing electronic vehicle market brought new challenges in analyzing lithium content in lithium batteries. Lithium is unstable when exposed to air and, hence, almost impossible to analyze in SEM. However, with the combination of a backscatter electron detector with very high dynamic range from Gatan and an EDAX EDS detector with extreme sensitivity for low energies, lithium can be mapped to see where it is and can be quantified with a high accuracy of 1 wt%.

Figure 1. (left) Map of the Li content in NMC 811 particles and (right) re-scaled Ni, Mn, Co, and O elemental maps after accounting for the Li content. Note that the grey color in the lithium map corresponds to regions of the sample that were not suitable for analysis by Cipher due to the significant fraction of H in the epoxy.

Providing cutting-edge technology in CEE

The merger has had a transformative impact on me, who is responsible for Central and Eastern Europe. It has allowed me to provide cutting-edge technology to scientists in the region, enabling them to make groundbreaking discoveries and advancements in their respective fields. The dynamic combination of our scientific products delivers the tools needed to push the boundaries of science in CEE.

Unveiling the power of EBSD in SEM

Furthermore, the EBSD technology provided by EDAX offers complete material characterization within the SEM. This addition has expanded the capabilities, providing scientists with a comprehensive solution for studying the microstructure and crystallography of materials. The latest development at EDAX provides the fastest EBSD cameras on the market and a solution for sensitive materials requiring low kV and low current conditions in SEM. All this is addressed by the first and only direct detection EBSD system, Clarity. Seeing the customer’s enthusiasm when you show them something that wasn’t possible until recently is great.

Figure 2. The EDAX Clarity EBSD Detector Series.

Enthusiastically looking to the future

Our entire European team is honored to be part of this incredible journey. We eagerly look forward to unforeseen developments in electron microscopy, materials analysis, and the world of science in Central and Eastern Europe. The possibilities are limitless, and as we continue to pioneer breakthroughs, the future looks even more thrilling. The journey has just begun, and the world of science and industry is the ultimate beneficiary of this exciting union.

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!

Blockbuster inspirations

Jordan Moering, Director Sales & Service Europe, Gatan/EDAX

When I look around me, I feel surrounded by the consequences of the good work we are doing at Gatan/EDAX.

Sometimes, this is less obvious, like knowing that electron backscatter diffraction (EBSD) and energy dispersive x-ray spectroscopy (EDS) were likely used to evaluate the microstructural performance of the steel used in the bridge I’m driving over. Or appreciating that the vaccines used to mitigate the damage of COVID-19 were developed largely due to the performance of Gatan cameras used in cryo-EM.

Recently, I felt surrounded by reminders of our equipment on blockbuster movie billboards, as I was in awe of the perseverance and astounding capabilities of scientists at the dawn of the quantum revolution. I find it humbling and inspiring that researchers from around the world raced to better understand the subatomic physics of the atom. Since then, it’s fascinating to see how nuclear science has evolved for the betterment of society to address many important issues relating to health, the environment, and fuel.

Today, many of the labs featured in a recent blockbuster movie are still doing critical work in nuclear energy, advanced materials, and reactor design that benefits all of us. As you can imagine, many challenges plaguing researchers in the late 1930s are still prevalent today. Radioactive or “hot” materials are some of the most challenging samples to study, as the very properties of these samples (e.g., radiation) that give them their unique energy-generating abilities also make them incredibly difficult to examine. For example, many detectors used in analyzing x-ray radiation can be immeasurably destroyed in the presence of gamma, beta, or nuclear radiation. Nonetheless, scientists still need to understand the chemical or crystallographic makeup of these materials.

One challenge in studying these materials is the discrimination of plutonium and uranium in the composition of mixed-oxide (MOX) fuels. As it turns out, the overlapping x-ray peak characteristics of these two very different metals make it quite difficult or impossible to qualify some of these fuels properly. The answer to this puzzle is, of course, wavelength dispersive spectroscopy (WDS). Because WDS uses diffracting crystals to interrogate the x-ray spectrum wavelength by wavelength (or energy by energy), it can present sensitivities up to 10x higher than traditional EDS alone. As a result, a single U/Pu peak that shows up in the EDS spectrum can be resolved to show two, three, or even more peaks that are all convoluted together. It is a very real statement to say that modern nuclear science has a critical need for WDS and other forms of analytical components in the scanning electron microscope (SEM).

Figure 1. WDS analysis (red) shows the presence of numerous metal peaks of Ta, Hf, and W within a single peak on the EDS (blue) spectrum. A similar result may be seen when investigating the characteristic peaks of nuclear materials like Pu and U.

Personally, I find this incredibly fulfilling and exciting, and hopefully, you’ll feel the same.

Being more precise, again

Dr. Stuart Wright, Senior Scientist, Gatan/EDAX

In my last blog posting, I was excited to show results from version 9 of EDAX OIM Analysis™ for refining EBSD orientation measurements. However, two questions have been gnawing at me since that post. (1) How much does the size of the patterns affect the results? and (2) How sensitive is the refinement to noise in the patterns? To explore these two questions, I will use data from the same silicon single crystal I used in my previous post – a 1 x 1 mm scan with a 30 µm step size. The patterns were 480 x 480 pixels and of excellent quality.

I added two levels of Poisson noise to the patterns, as shown in Figure 1, and will term these noise levels 1 and 2 for the subsequent analysis.

Figure 1. Si single crystal patterns processed with adaptive histogram equalization [1]. (a) initial pattern, (b) pattern after a moderate level of added noise, and (c) pattern after a significant level of added noise.

The next step was to bin the patterns, index them using spherical indexing, and then apply orientation refinement as implemented in version 9 of EDAX OIM Matrix™. To perform the experiments, I binned the patterns to 360 × 360, 240 × 240, 160 × 160, 120 × 120, 96 × 96, 80 × 80, 60 × 60, and 48 × 48. After binning, I re-indexed them using spherical indexing and then calculated kernel average misorientations (KAM). I used the average KAM value as a measure of precision and plotted that against the binned pattern size for all three noise levels (0, 1, and 2). Figure 2 shows the results of the experiments.

Figure 2. Plot of average KAM values vs. pattern width for all three noise levels.

I have a couple of observations from these results.

  • In general, the first level of noise has only a minimal impact on the precision, whereas the higher level of noise has a more significant impact.
  • For noise levels 0 and 1, the average KAM values remain relatively constant until the pattern size dips below 120 × 120 pixels. Surprisingly, good results can be obtained until the smallest size of 48 × 48 pixels is reached. For noise level 2, the precision drops off significantly at a pattern size of 96 × 96. Those using Velocity cameras have probably noticed that the default pattern size is 120 × 120 pixels. Similar results to those I’ve presented here lead us to choose 120 × 120 pixels as the default. These results confirm the soundness of that choice.

I hope these results can guide the expectations for what orientation refinement can achieve in your samples. We will announce the official release of EDAX OIM Analysis 9 in the next few weeks. We hope you are excited to apply it to your materials. The orientation refinement tools are part of EDAX OIM Matrix, which is an add-on module. While you wait for your copy of version 9, make sure you save the patterns you plan to apply orientation refinement measurements to. No, I’m not getting paid by the hard drive manufacturers 😉.

Figure 3. Screenshot of EDAX APEX showing where the check-box to save patterns is located within the software.

[1] Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J.B. and Zuiderveld, K., 1987. Adaptive histogram equalization and its variations. Computer vision, graphics, and image processing 39: 355-368.

Embracing the return

Dr. Shangshang Mu, Application Scientist, Gatan/EDAX

Over the past year, I’ve rekindled my enjoyment of traveling as I visited customers in the Americas, Asia, and across Europe. During my return journey, I was deeply touched by an airline billboard at the Munich, Germany airport that read, “We all live under one sun. Let’s see it again.” Indeed, it is genuinely nice to see the world once more since reemerging from the pandemic.

While flying over Hudson Bay, an inland marginal sea of the Arctic Ocean, I saw numerous ice caps floating on the water from the aircraft’s belly camera view. To me, these were very reminiscent of the counts per second (CPS) map (Figure 1) in one of the wavelength dispersive spectrometry (WDS) datasets I shared with customers during these trips. Although they were orders of magnitude larger than the micron-scale sample, the resemblance was striking.

Figure 1. Ice caps in Hudson Bay (left) resemble the CPS map of a Si-W-Ta sample (right).

Throughout these journeys, our EDAX Lambda WDS system was one of the hot topics drawing customers’ attention. This parallel beam spectrometer features a compact design compatible with almost every scanning electron microscope (SEM). The improved energy resolution and sensitivity and lower limits of detection make it an excellent supplement to your energy dispersive spectroscopy (EDS) detectors. The CPS map I referred to was captured from a Si-W-Ta sample. The energy peaks of Si K, W M, and Ta M are heavily overlapped, with only approximately 30 eV energy difference between each other. Lambda WDS systems provide up to 15x better energy resolution than typical EDS systems, effectively resolving the ambiguity in analysis.

Figure 2. Overlay of EDS (red outline) and WDS (cyan color) spectra from the central area of the Si-W-Ta sample.

The overlay of EDS/WDS spectra from the central area of the map shows that the Lambda WDS system intrinsically resolves the overlapping EDS peaks (red outline), as depicted by the cyan color WDS spectrum (Figure 2). The shortcoming of EDS in resolving these overlapping peaks results in the distributions of the three elements appearing identical in EDS maps. However, the WDS maps provide clear and distinct visualizations of the individual distributions of the three elements (Figure 3).

Figure 3. EDS (top) and WDS (bottom) maps of the Si-W-Ta sample. The WDS maps resolve the artifacts due to Ta M, Si K, and W M peak overlaps in the EDS maps.

This year’s M&M meeting is just around the corner. If you are traveling to this entirely in-person event, stop by our booth (#504) to check out our integrated EDS-WDS SEM solutions and many other products that will capture your interest.