Matt Chipman, Sales Manager – Western U.S., EDAX and Gatan
I recently watched a local news story about inflation in consumer goods. The reporter wanted to know if the dollar store could save you money on groceries. The general answer was perhaps on some items, but it wasn’t significant. However, it was interesting to see how some stores focus on a perceived value instead of a real value to its consumer. First, the dollar store raised its starting price from $1.00 to $1.25. Then they used odd-sized packages that were not equivalent to regular grocery store items, making a direct comparison difficult and offering minimal to no real savings. Finally, the dollar store’s selection was very limited so you may end up back at the regular grocery store for anything other than packaged goods.
So, what does this have to do with the microanalysis business? Well, I believe it’s important to look at the big picture with real, tangible benefits that can impact your research. By offering both EDAX and Gatan products, there are more opportunities to combine different technologies to enable unique analyses that can provide a tremendous value to your material studies.
One great example is the quantification of lithium on a scanning electron microscope. By uniting Gatan’s low-kV OnPoint™ Backscattered Electron Detector with EDAX’s Octane Elite Super EDS Detector, this one-of-a-kind analysis is now possible, surpassing what can be done by either technique alone.
Figure 1. The lithium mapping from joint characterization of the EDAX Octane Elite EDS Detector and Gatan OnPoint BSE Detector.
Not to forget, we’ve also been combining the strengths of the Gatan DigitalMicrograph® Software with the EDAX EDS detector technology for TEMs. I believe we are just beginning to scratch the surface of creative things we can do by joining microanalysis systems and techniques. I love discussing creative ways my customers can coalesce microanalysis techniques to do something new.
Figure 2. Multimodal data acquisition of EELS and EDS data combines the chemical sensitivity of EELS with the broad compositional mapping of EDS. Pictured – STEM EELS/EDS mapping of vertical channel 3D NAND acquired with DigitalMicrograph software.
I hope we can all figure out ways to get a real, noticeable value from the equipment we purchase during this time of inflation. I hope to hear ideas from some of you as you tell me about the needs of your laboratories.
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 . 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  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  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 . 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.
 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.
 PG Callahan, and M De Graef (2013) “Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations” Microscopy and Microanalysis, 19, 1255-1265.
 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.
 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.
As a classically trained chemist, most of my career has been spent looking into the challenges presented within the materials science world. Not so coincidentally, I never spent much time thinking about the problems that face our scientific brethren in the life sciences. Cells, tissues, biomolecules – these things were the squishy equivalent of a foreign language to me.
In the past few years, while working in the Atomic Force Microscopy space and the Electron Microscopy (EM) space, I kept coming across researchers from the life sciences who asked questions that sounded more and more like traditional materials science questions. What is the structure of my biomolecule? What elements are present, and in what proportion in this tissue or vesicle? What are the mechanical properties of my cell? And I thought, what kinds of questions should we be asking, and where can we use classic materials analysis methods (in this case, Energy Dispersive Spectroscopy (EDS)) to answer questions in the life sciences? Like any good researcher, this led me to a literature search to see what’s been done before. I’d like to share what I found and what problems we can potentially solve with EDS.
Taking a half-step back, I should briefly explain the principles of EDS as a technique to make sure we’re all on the same page. The too long; didn’t read (TL;DR) explanation of EDS goes something like this. When the electron beam interacts with a sample within an electron microscope, X-rays are emitted. Each element releases X-rays with a unique energy signature, proportionally increasing as a function of their atomic number (e.g., carbon X-rays are lower energy than iron X-rays, which are lower still than lead X-rays). An EDS X-ray detector (like EDAX manufactures) captures these X-rays, identifies the elements present in the sample, and quantifies their concentration. The big advantage of an EDS detector in your SEM is identifying which elements are present. The disadvantages are that it doesn’t discriminate the structure of how these elements are bonded to one another, what molecules or more complex compounds might be present, and historically, EDS hasn’t been tremendously efficient at detecting or quantifying elements lighter than, say, fluorine (the latest generation EDS detectors by-and-large have overcome this, and now routinely measure elements as light as boron, beryllium, and in the right conditions, lithium).
The latter disadvantage has historically been only part of the limiting factor in the general acceptance of EDS in the life science community. Biological systems are basically a collection of carbon, nitrogen, hydrogen, and oxygen, and the structural arrangement of these elements is what matters. EDS didn’t do that, unlike, say, vibrational spectroscopy (Fourier-transform infrared spectroscopy (FTIR) or Raman) or mass spectroscopy, and therefore was discounted for lack of usefulness to the standard biologist. But, with the novel researcher looking for any edge they can find to learn more about their system, new attention is being given to attributes where heavy element detection or accumulation is present in biological systems.
Mineralization and bioaccumulation of materials in tissues and systems is a perfect example of how EDS provides new insights into biological processes. Heavier elements like calcium, phosphorus, and potassium are accumulated and concentrated in easily detectable amounts in tissues leading to the formation of biominerals like kidney stones, sclerotic materials, and bone spurs. EDS provides a simple and clear method to visualize and quantify how these elements are distributed.
Figure 1. STEM EDS images of human sclerotic tissues to show elemental concentrations.
There are numerous examples in the literature of using nanoparticles from a host of different elements to understand cellular and biomechanistic behaviors better. From tumor growth studies using iron and copper nanoparticles to track the deposition of drugs within a cancer cell, to using zinc and iron nanoparticles to understand biomaterial scaffolding, to using silver and gold nanoparticles to understand the efficacy of erectile dysfunction drugs, nanoparticles allow for a targeted tracking of materials using EDS. 
Toxicological leaching of biological implants for dental and orthopedic research to novel biomaterials always require a toxicological study to ensure that the materials used do not leach or otherwise migrate during in vivo applications. EDS is a suitable tool to evaluate lifetime studies looking for the flow of titanium, nickel, iron, or other metallic elements during post-mortem analysis of implanted structures. In addition, environmental leaching of hazardous materials, which are accumulated in plant life, can also be measured via SEM EDS. The ability of different phenotypes of plants to absorb iron or manganese from the soil and concentrate it in the cellular structure can be measured effectively.
Figure 2. EDS spectra and cartoon characterization showing changes in Fe or Mn uptake and concenration as a function of phenotype of Arabidopsis thaliana cotelydons seed pods. 
These are just a few examples of the numerous ways that SEM, STEM, and TEM-based EDS can be used to complement research in the life sciences. As we continue to see a blending of the materials and biological worlds, I look forward to seeing more examples of elemental analysis being used to further scientific discovery.
Satoshi Hara, E. et al, Nanostructural analysis of distinct nucleation sites in pathological mineralization. RSC; Mater. Adv., 2021, 2, 4423.
As an applications engineer, it is always fun to play with cutting-edge products. Last year, I got an exciting new lab partner, an Orbis PC Micro X-ray Fluorescence (Micro-XRF) Analyzer, which is an excellent complement to Scanning Electron Microscopy – Energy Dispersive Spectroscopy (SEM-EDS)-based X-ray microanalysis.
Figure 1. The Orbis PC Micro-XRF Analyzer.
For those of you who are not familiar with Micro-XRF, it is a technique similar to Energy Dispersive Spectroscopy (EDS) in that they both detect generated X-rays after interaction with the sample. For EDS, X-rays are generated by electrons boarding the sample, while in a Micro-XRF unit, fluorescent X-rays are excited by high-energy X-rays emitted from the X-ray tube. Silicon Drift Detectors (SDDs) are used for X-ray detection in modern EDS and Micro-XRF systems. Data collection is also similar because it is possible to use either one to do qualitative and quantitative analysis, mapping, and linescan.
This benchtop Orbis PC analyzer utilizes the benefits of conventional XRF while implementing micro-spot X-rays down to 30 μm by employing a polycapillary technique with a moveable stage. For higher Z elements, it improves the detection limits ten times or more than SEM-EDS. It uses higher-energy X-rays to generate lines that are not detectable with EDS, such as Sr L, Zr K, and Ag K, which is useful when lower energy lines overlap in the EDS spectrum. The industry-exclusive motorized turret, integrating video and X-ray optics, provides coaxial X-ray analysis and sample view perpendicular to the sample surface for more accurate sample positioning and no shadowing of the X-ray beam. The analysis is non-destructive, with no beam damage to the sample, and minimal sample preparation is required.
Grinding and polishing of the sample are not generally required, and conductivity is not an issue. Sample loading is flexible in that thicker samples can be loaded directly on the stage, and thinner samples, particulates, and fibers can be mounted. The sample shape and height can be irregular, and the large sample chamber in this benchtop Micro-XRF unit can accommodate a wide range of sample sizes. Samples can be run either in low-vacuum mode or air mode, allowing the analysis of liquids or samples that will dehydrate in a vacuum. An SEM-based Micro-XRF system does not have many of the benefits brought by this benchtop unit. Once the sample is loaded in an SEM chamber, all the requirements of SEM samples apply. The chamber size and stage of an SEM largely limit the sample dimensions, and non-conductive samples must be coated. The ability to analyze samples that cannot tolerate a vacuum atmosphere is also lost.
Figure 2. The unique four-position turret. Position 1 is the high magnification video, and position 2 is the 30 μm polycapillary X-ray optic. Position 3 and 4 are 1 mm and 2 mm collimators, respectively.
Figure 3. The video (green) and X-ray (dark red) paths of the Orbis Analyzer are coaxial and perpendicular to the sample surface, which means the X-ray path is observable in the video, and there is no shadowing of the X-ray beam. If the X-ray path is non-coaxial (red), it can be blocked by the high topography object.
Since SEM-EDS and Micro-XRF share many similarities and work together to accomplish the complete needs of spectral analysis (see the How to Correlate Micro-XRF and SEM-EDS for Optimal X-ray Characterization of Materials article in the March 2022 issue of the Insight newsletter), I always like to correlate them from every aspect. The absorption edge is the most recent one that caught my attention. For EDS users, if you ever take a close look at the Bremsstrahlung background modeling in the APEX™ software, it is not a smooth curve but exhibits sharp edges (e.g., Figure 4). These are absorption edges, indicating the minimum energy required for an element to eject an electron from its core orbital to create a vacancy. For example, the absorption edge of Ni K lies at approximately 8.33 keV. As the electron energy reaches this value, there is a huge spike in energy attenuation because this is the point that the excitation of Ni K lines begins (Figure 5). The Mass Absorption Coefficient quantitatively represents each element’s absorption of energy. The self-absorption of X-ray photons in a specimen is the dominant effect in EDS, as well as the Bremsstrahlung background distribution shape. The mass absorption coefficient jumps visibly influence the spectrum, mainly in the soft X-ray region. Our Bremsstrahlung background modeling includes these absorption edges for fine control and accurate background correction.
Figure 4. The absorption edge of Fe K at approximately 7.11 keV in an EDS spectrum.
Figure 5. The absorption edges of Cr, Fe, and Ni K lines.
The absorption edge plays an extraordinary role in Micro-XRF since the design of primary beam filters employs the knowledge of absorption edges. The Orbis PC unit is equipped with six primary beam filters to preferentially absorb X-rays at certain ranges to reduce the background to improve detection limits and eliminate artifact peaks. The filter wheel is placed between the X-ray tube and X-ray optic, so the X-rays scattered by the filter do not reach the sample (Figure 6). Only X-rays focused by the optic or collimated by the collimator reach the sample for accurate sample targeting. Figure 7 shows the background in the spectrum if the X-rays generated from the X-ray tube are exposed to a Ni filter. There is a strong correlation between the background in this figure and the graph illustration in Figure 5. The X-ray attenuation decreases as the absorption edge at approximately 8.33 keV is approached in Figure 5. This coincides with more and more of the tube X-rays penetrating through the filter and being present in the spectrum. Once the energy reaches 8.33 keV, there is a sudden increase in the absorption of X-rays shown in Figure 5, and this is why a huge amount of the background signal is absent in Figure 7 since most of the signal is now absorbed by the Ni filter. After the significant jump at the absorption edge, the attenuation continues to decrease as energy increases in Figure 5. This correlates to the background getting higher and higher in Figure 7 since more and more tube X-rays continue to penetrate through the Ni filter. The area with the lowest background signal in Figure 7 is the high-sensitivity region where the Ni filter cleans up the spectrum, allowing true elemental peaks of interest to show up. Figure 8 is an example of the detection limits of As in an As2O3 sample. The Al-heavy and Ni filters significantly increase the peak-to-background ratio to push the detection limit to a single-digit ppm-level.
Figure 6. Schematic of filter wheel design in Orbis system.
Figure 7. Background spectrum from the X-ray tube after being exposed to a Ni filter.
Figure 8. A spectrum overlay of As2O3 was collected using an Orbis PC without a filter (red), with Al-heavy (blue), and Ni (green) filters.
Expect a few new application notes and experiment briefs from this unique lab partner!
Back in my early days of installing some of the first EBSD systems in the world, one of the issues I had was figuring out how to demonstrate the system’s performance and how to help users operate their systems to get that same performance. As EBSD users know, this technique requires a certain level of sample preparation to obtain useable patterns and good quality maps. Because of this, I would bring my own previously prepared samples to set up a system. However, I generally would not leave these behind. This encouraged customers to figure out sample preparation before using their EBSD system.
After a few of these visits, we decided it would be beneficial to provide users with standard samples that could be left with the systems. To do this, we selected the material, prepared it for EBSD, and then packaged it for delivery. The question at that point was, ” what material do we use?”
We wanted something that would produce good EBSD patterns, not significantly degrade over time, and was something we could prepare ourselves. One of the materials EBSD had consistent success with early on, and still do today, is nickel-based superalloys. These materials have a higher average atomic number than aluminum alloys for stronger EBSD pattern intensity, large enough grains for work on both tungsten and FEG source SEMs, and can sit in a lab for years while still producing good EBSD patterns after the initial preparation. This led us to select Inconel 600 as our standard material.
It also led to it being one of the most well-characterized alloys by EBSD globally, even if the results are not all published. We have used our nickel standard to test all our detectors, from early SIT video cameras, to the first DigiView CCD cameras, the high-speed Velocity CMOS cameras, and now the Clarity Super direct-detector system. This material gives us a consistent reference point to better understand performance. We have also used the material for validating PRIAS™ imaging, NPAR™ processing, and OIM Matrix™ indexing.
Figure 1. An EBSD IQ map with random grain boundaries drawn as black lines, primary twins drawn as red lines, and secondary twins drawn as blue lines.
Several interesting microstructural features can be measured with these alloys. First, a high fraction of twin boundaries are typically present within the nickel samples. Figure 1 shows an EBSD Image Quality (IQ) map with random grain boundaries drawn as black lines, primary twins drawn as red lines, and secondary twins drawn as blue lines. We can also show grain maps with this high twin fraction, where grains are determined from the measured orientations and then randomly colored while including and excluding the twins in the grain grouping algorithm. Figure 2a shows the grain map, including the twin boundaries, while Figure 2b shows the grain map excluding the twin boundaries. There is a significant difference in effective grain size between these two microstructure views. Finally, we know that the twinning plane in face-centered cubic nickel alloys is the (111) plane. We can display the (111) plane trace on both sides of the twin boundaries, as shown in Figure 3.
Figure 2. a) A grain map that includes the twin boundaries. b) A grain map excluding the twin boundaries.
Figure 3. Combined IQ and IPF orientation map with (111) plane traces shown on both sides of selected twin boundaries.
Now you have some idea of what you can measure with your EDAX EBSD nickel standard.
Ever since I started university and later began my graduate research work on energy-related topics, global warming and renewable energy are two subjects that appear frequently in papers and conferences. To mitigate and avoid the potential climate catastrophes that global warming may cause, governments and companies have invested heavily in renewable energy research over the years. Lithium batteries are one of the renewable energy technologies that are commonly used for cars and appliances. As you may know, many governments have implemented laws to ban fossil fuel cars sales in the foreseeable future and have encouraged companies like Telsa, Nio, and BYD to make these batteries more readily available.
However, charging an automobile is not as convenient as adding gasoline. And if you’ve ever driven an electric car, you’re probably aware of how much the mileage varies between summer and winter. But electric cars are the future. As universities, research institutes, and enterprises troubleshoot issues like these, I think the future of battery technology will be bright and more surprises will show up.
Since I joined Gatan, I have also been responsible for some of EDAX products. Gatan and EDAX are both scientific equipment providers of material characterization solutions for electron microscopy. For lithium batteries, we have a series of products that cover users’ application needs in one way or another. Last year, we introduced a joint characterization solution for lithium using the EDAX Octane Elite Energy Dispersive Spectroscopy (EDS) Detector and Gatan OnPoint™ Backscattered Electron (BSE) Detector. With this solution, we can reduce the detection limit of lithium by nearly ten times, compared with current schemes, to a single-digit mass percentage. At the same time, the characterization ability is not affected by the oxidation state of lithium.
Figure 1. The lithium mapping from joint characterization of the EDAX Octane Elite EDS Detector and Gatan OnPoint BSE Detector.
Many users wonder why it is difficult to characterize lithium as a light metal (whether elemental or ionic) with an EDS detector alone. The reasons behind this are related to the mechanism by which X-rays are generated in electron microscopy and the window material of the EDS detector. Long story short, the generation of EDS signals requires the electron beam to knock out the electrons in the inner shell of an element, and then the vacancies cause the electrons from the outer shell to refill. After refilling the vacancy, due to the difference in energy levels of the two electron shells, an EDS signal corresponding to this energy difference is generated.
Figure 2. Characteristic X-ray production using Si K_α as an example. Adapted from myscope.training.
So, in this over simplified scheme, EDS can only detect lithium metal, and cannot detect lithium ions (just have two electrons in the K shell, no electron to refill the hole). In addition, due to the fact that the characteristic X-ray energy of lithium is only 55 eV, the common thick polymer window in EDS detectors absorbs low-energy X-rays heavily. However, the unique ultra-thin Si3N4 window material in EDAX EDS detectors provides higher X-ray transmittance at the low-energy range (see red line in Figure 3). Therefore, EDAX helps.
Figure 3. The low energy X-ray transmission rate comparison between EDAX Si3N4 window material (in red) and commonly used polymer window (in green).
Gatan’s image filter (GIF) system offers a different solution from another technical point of view on the same lithium detection issue, and the electron energy loss spectroscopy (EELS) spectrum is much better and easier at detecting lithium. In contrast to the generation process of EDS signals, EELS signals begin to generate in the first step (inelastic scattering), namely the interaction of the electron beam with electrons outside the nucleus. The signal counts of EELS are much stronger than that of EDS, and the characterization of lithium is naturally much more convenient than that of EDS. Of course, lithium or battery materials, as a whole, are very sensitive and are not resistant to the electron beam, which creates additional requirements for Gatan’s imaging filter system. It needs to be fast, have high sensitivity, and low noise.
Figure 4. Gatan GIF Continuum K3 System.
The figure above shows the Gatan GIF Continuum K3 System, which has high sensitivity from the K3 direct detection camera. It can also collect data at high speeds with little noise. Last November, professor Meng Gu’s team at the Southern University of Science and Technology (SUST) in China published a paper on Matter. They used an extremely low beam dose (10 pA) to successfully characterize lithium and acquire the fine structure of the lithium element from electron loss near edge structure (ELNES) spectra. Then, they mapped out lithium metal and surficial oxidized lithium in their battery material using the MLLS function in the Gatan DigitalMicrograph® Software. The GIF Continuum K3 not only detects lithium but also identifies lithium in different chemical valence states. This work has important values for studying the “dead lithium” problem.
However, for lithium-ion battery research, the detection of lithium is only the first step. The more important content is about studying the transport pathways of lithium ions, and these pathways determine the energy density, capacity, and life span of a battery. But how do we characterize the flow of those ions? This problem corresponds with figuring out how to characterize the grain structure inside the cathode material of a battery. There is a correlation between the grain size of a cathode material, the specific crystal plane, grain boundaries, and the transport tendencies of lithium ions. In an ACS Nano article published at the end of last year, Yuki Nomura from Panasonic Company of Japan employed both precession electron diffraction (PED), a crystallographic characterization method similar to Electron Backscatter Diffraction (EBSD) but on a transmission electron microscope (TEM)), and the Gatan Quantum Imaging Filter Series, taking data from the same region of electrode material on an in-situ TEM. The results show the relationship between the real-time distribution of lithium at different stages during a charging reaction and certain grain boundaries and crystal planes block the movement of lithium ions. For a particular crystal orientation, lithium ions have a clear tendency to move through during charging, while some other crystal planes and grain boundaries have obvious resistance to the movement of lithium ions. Personally speaking, it is believed that from Yuki’s work, there will be more relevant research published in this field in the future. As a result, researchers are helping to achieve a more reasonable design for battery material’s crystal structure and chemical composition.
It’s hard not to think of the EBSD technology on a scanning electron microscope (SEM) after looking through the PED used in Panasonic’s paper. After all, EBSD can do all the functions that PED can achieve on TEM, except spatial resolution, on scanning electron microscopy, or even better (for example, angular resolution). Given the electron beam dose issue on battery materials, the main CMOS scintillator-based EBSD detectors on the market may have some difficulty with characterization. In response to this problem, EDAX has an EBSD product based on direct detection technology, the Clarity™.
Figure 5. a) Inverse pole figure (IPF) of lithium battery cathode material using normal EBSD experimental conditions, HV: 20 kV, beam current: 1.6 nA. Many unindexed points in cathode particle; b) IPF of the same cathode material but on a different region using the Clarity EBSD Detector, HV: 10 kV, beam current: 400 pA. where more structural details are disclosed; c) The Clarity EBSD Detector.
In August 2020, Donal Finegan’s team at the Renewable Energy National Laboratory (NREL) in the USA used Clarity to obtain orientations, grain boundaries, and morphologies information about NMC electrode material for lithium-ion batteries. This ample structural information helps researchers identify the mechanism by which intergranular cracks occur to understand the transport pathway of lithium ions, and the reduction of battery capacity caused by the expansion of cathode material lattice during charging and discharging processes. Previously, many publications only showed that polycrystalline, small grain cathode materials contributed to better battery performance. Still, the performance advantage caused by specific polycrystalline materials or those characteristics in small grains is not clear. Finegan’s work, through Clarity EBSD, helps us find the grain boundary structure that could be potentially beneficial thereby this work can guide people to designing more accurate battery materials. In addition, EBSD has another advantage. Counting on the material processing capabilities of Focused Ion Beam (FIB) electron microscopy, we can also achieve 3D-EBSD characterization and study grains on a three-dimensional scale. This feature is nearly impossible for PED. I believe that more research on grain size and boundaries based on three dimensions is the future, and will bring us more surprises.
As an application scientist works who for a scientific instrument company, I enjoy thinking deeply about the field of equipment applications and taking the practical problems from our users’ research as opportunities for us to improve our technical knowledge and demonstrate the superior performance of our equipment. In the future, I look forward to seeing our Gatan and EDAX equipment shine in the fields of renewable energy, additive manufacturing, ultrafast electron diffraction, cryo-EM coronavirus research, and other research fields. And I also look forward to, through my own learning and improvement, bringing more inspiration and thinking to our users from the application perspectives so that our users can not only use our equipment properly but also use our equipment in a more advanced way.
 Han, Bing, et al. “Conformal Three-Dimensional Interphase of Li Metal Anode Revealed by Low Dose Cryo-Electron Microscopy.” Matter (2021).
 Nomura, Yuki, et al. “Lithium Transport Pathways Guided by Grain Architectures in Ni-Rich Layered Cathodes.” ACS nano (2021).
The new year is here. And with it, we look for ways to start our research on the right foot.
Over the past two years, I’ve traveled less and been fortunate to spend more time supporting customers online and working in the lab analyzing samples. Whether it’s a customer’s sample or my own, my goal is to push the limit by using different conditions across various samples to get the best results. Using this approach, I can always get good Electron Backscatter Diffraction (EBSD) indexing results when analyzing challenging samples. It is the highlight of my day when I see the colorful Inverse Pole Figure (IPF) maps and feel that my hard work has paid off.
On the other hand, my experience in technical support provides me with some tips. After all, most people’s mistakes are similar. I want to take this opportunity to discuss this.
In many cases, analysis results are sub-optimal because experimental details are not well controlled and are not a reflection of the product age or technology. Recently, I have seen many papers (such as the deformed Ti on the cover of Science) where they did not use the latest hardware/technology, but the results are excellent. If you take EBSD as a whole process and properly deal with all influencing factors, then any shortcomings will affect the final result. Many factors need to be accounted for, such as the preparation of the sample, sample mounting, the input signal strength or weakness, and then it comes to the EBSD operation itself.
Figure 1. September 17, 2021 issue of Science magazine featuring an EBSD orientation map of cryoforged Titanium.
Since I have a Gatan Ilion® II (model 697) in my sample preparation room, I no longer worry about the sample preparation process. Ion polishing is the best method, and it can achieve the requirements most of the time. Of course, vibration or electrolytic polishing is also a suitable method; just pay attention to the choice of parameters.
Figure 2. Gatan Ilion II System
Often it is attention to the small details that pay the biggest dividends. When mounting the sample for EBSD, we want to eliminate sources of physical sample drift due to the effects of a 70° sample tilt. I use a mechanical method or choose a liquid glue when performing this step. This becomes more important as the sample size and mass increase. Also, use an appropriate beam current selected for EBSD. Beam currents used for high-resolution SEM imaging are often lower than required for traditional EBSD detectors. Make sure the image is in focus and is properly stigmated. I once demonstrated the effect of focus on EBSD Image Quality (IQ) values, and the people present were astonished. When needed, use the dynamic focus correction on the SEM to keep the focus constant across the tilted surface.
Be aware of the different parameters that can be set for the EBSD system as well as the SEM. From my personal experience, the selection and optimization of these parameters can easily increase the speed and quality of your data.
Once you get to this point, the process is almost complete. The EBSD parameters are pretty simple, as long as the signal can be reached. Think about the number of grains to analyze and the SEM magnification required for this field of view. Then select a step size appropriate for the average grain size and type of analysis. Selecting this requires thinking about our desired acquisition time, the speed of the detector, and the details of the microstructure, and the APEX software can recommend different values. If every step is done well, then this process should be perfect. Then, just do it.
In the end, these proven approaches can be applied to your existing or new instrumentation to achieve your best results. I hope these thoughts are helpful to ensure your work goes smoothly in the new year.
Last year we released APEX 2.0 software, which had grown considerably into a more complete microanalysis package to offer both Energy Dispersive Spectroscopy (EDS) and Electron Backscatter Diffraction (EBSD) characterization capability. APEX 2.1 was delivered earlier this year to include support for dual EDS detectors on the EDS side. Towards the end of this year, we added another major feature, Full Standards Quant, and multiple other new functionalities with the release of APEX 2.2. APEX 2.2 has been out for more than a month, and many users have enjoyed the higher-level features and enhancements brought by the latest release. In this blog post, I will go over what is new in APEX 2.2 for EDS.
Spectrum and Image Annotation
With the new floating Annotation Toolbar, the user can easily add text and shapes in images and spectra. A ruler option is available to measure any feature in the image, and the measurement is saved with the image. All of the annotations can be edited or deleted after creation. Annotations are saved with HDF5 files and included in the report. Spectra can be saved as images with annotations.
Figure 1. left) Floating toolbar and annotations in the spectrum. right) Annotations in the SEM image.
Previously, multiple spectra were normalized with respect to the highest energy peak by default. The new Spectrum Normalization feature gives the user more flexibility. The spectra can be normalized based on any peak by selecting the region or element or drawing in the spectrum overlay. The ratio information is shown on the overlay, and the normalized energy range is displayed in the report.
Figure 2. Spectra normalized with respect to the Si K alpha peak. The ratio information is shown at the top-right corner.
Quant Montage Maps
In APEX 2.0, Montage mapping was released as a key feature that allows precise large-area imaging and EDS and EBSD mapping. It uses stage movements to collect individual high-resolution images and maps through a grid pattern over a large sample surface and stitches them into montages. In APEX 2.2, we took one more step forward, and now the user can rebuild Montage maps as NET and ZAF (Wt% and At%) maps. The workflow is the same as rebuilding single-field EDS maps for ease of use.
Full Standards Quant
The most exciting new functionality in this release is Full Standards Quant. We calculate a unique reference value from a single element standard spectrum to determine the product of the beam current and detector solid angle. The reference value will be burned with every subsequent spectrum collected at the given parameters. It normalizes out any differences in beam current and detector geometry. As long as the standard and unknown spectra were collected at the same accelerating voltage, the standard can be applied to quantify the unknown sample. There are three modes available in Full Standards Quant. The Standard mode is suitable for those users who want to take complete control of the standard selection. The MultiStandards mode uses the mean values of the loaded standards. The SmartStandards mode is an intelligent approach that automatically picks the best fitting standards using an iterative internal assessment algorithm. The Full Standards Quant chart (Figure 3) gives a visual representation of how close your standards are to your unknown. It also generates a curve to show the calculated element concentration versus net counts based on the loaded standards.
Figure 3. Full Standards Quant chart of Si K. The yellow diamonds show standards, and the “X” indicates the unknown sample. The calculated curve gives the element concentration versus net counts based on the loaded standards.
APEX 2.5 with new features and productivity enhancers for EDS and EBSD is on the horizon in 2022, followed by more integrations in the APEX platform. As always, minor version upgrades of APEX are free, so experience the power of APEX 2.2 now and prepare for the benefits of APEX 2.5 soon. It will be an exciting year for APEX!