Spherical Indexing

Will Lenthe, Principal Software Engineer, EDAX

Dictionary indexing compares experimental electron backscatter diffraction (EBSD) patterns against a dictionary of simulated patterns for each orientation on a uniform grid in orientation space [1,2]. Synthetic patterns are generated by rotating the Kikuchi sphere by the crystal orientation and projecting onto a plane using the experimental geometry. Comparison against a physics-based forward model gives excellent precision and noise tolerance at the cost of significant computational overhead. Spherical harmonic-based indexing uses the same Kikuchi sphere or ‘master pattern,’ but back projects experimental patterns onto the sphere instead. The orientation is indexed using the maximum spherical cross-correlation between the back-projected pattern and the Kikuchi sphere [3,4]. Mathematically, dictionary and spherical indexing are extremely similar, but the spherical approach is more numerically efficient since it can leverage fast Fourier transforms for the computations. In practice, spherical indexing provides similar precision [5] and noise tolerance to dictionary indexing but at much faster speeds.

A GPU implementation of spherical harmonic-based EBSD indexing implemented in OIM Analysis™ as part of the OIM Matrix module provides excellent indexing quality at hundreds or thousands of patterns per second. Here, we applied it to a range of scans to demonstrate the indexing quality and user parameters.

Spherical harmonic indexing has two parameters: bandwidth and grid size. Bandwidth is how far in frequency space to compute harmonics (analogous to a low pass filter on the EBSD pattern). Grid size is the correlation resolution with an Euler angle cube of (grid size)3 used for correlation (i.e., 0 – 360 for phi1, Phi, and phi2). In general, computation time scales with the number of Euler angle grid points, and a reasonable bandwidth is one less than half the grid size. For example, the following are some reasonable pairs of values:

BandwidthGrid Size
63128
95192
127256

Once the best Euler grid point (maximum cross-correlation) is selected, subpixel resolution can be achieved through a refinement step.

Ni Sequence

This dataset is a scan of the same region at different camera gains to intentionally produce corresponding sets of low and high-quality patterns.

Figure 1. Shows a) the result of indexing high-quality patterns, b) spherical harmonic indexing at a bandwidth of 63 and Euler grid of 1283 without refinement, and c) at a bandwidth of 63 with refinement.

Figure 1 shows a) the result of indexing high-quality patterns, b) spherical harmonic indexing at a bandwidth of 63 and Euler grid of 1283 without refinement, and c) at a bandwidth of 63 with refinement. Note that since grid point spacing is ~2.8° (360° / 128), the unrefined result has a stepped appearance due to the discrete orientation possibilities. After refinement, any orientation is possible, providing smooth results.

Figure 2. KAM maps are shown for the same region at a) 0°, b) 1°, and c) 2°.

In Figure 2, KAM maps are shown for the same region at a) 0°, b) 1°, and c) 2°. Notice that without refinement, there is no misorientation within a patch and a sharp spike between them. Even though both the Hough and refined spherical appear smooth, the slight orientation noise in the Hough indexing is visible using KAM.

Figure 3. With low-quality patterns, Hough indexing a) starts to fail, but b) spherical indexing still provides robust solutions and c) accurately captures continuous orientation gradients after refinement.

With low-quality patterns, Hough indexing a) starts to fail, but b) spherical indexing still provides robust solutions and c) accurately captures continuous orientation gradients after refinement (Figure 3).

Figure 4. a) bandwidths of 63, b) 95, and c) 127 are compared before (a – c) and after (d – f) refinement.

For very low-quality patterns, higher bandwidths may be required for better indexing results. In Figure 4, a) bandwidths of 63, b) 95, and c) 127 are compared before (a – c) and after (d – f) refinement. Note that the discrete steps in orientations before refinement become smaller with increased Euler angle grid resolution, but they refine to similar orientations. For all three bandwidths, the grid size is 2 * (bandwidth + 1).

Figure 5. 4. a) Raw pattern and b) NPAR pattern using Hough indexing and c) raw pattern and d) NPAR pattern using spherical indexing with a bandwidth of 127.

With spherical indexing integrated into OIM Analysis, existing image processing algorithms can be used for especially difficult patterns. At extremely high noise levels, Hough indexing cannot index any points, and the spherical indexing begins to fail for some points. NPAR trades spatial resolution for pattern quality by averaging each pattern with its neighbors. The improved patterns can be indexed reliably by both methods but Hough indexing struggles with the resulting overlap patterns near grain boundaries (Figure 5).

Hot Rolled Mg

Figure 6. Hough indexing struggles to index when pattern quality is reduced by a) high deformation, but b) spherical indexing is robust against significantly degraded pattern quality. Note that the d) spherical indexing confidence index strongly correlates with c) image quality but is high even in some regions with extremely low IQ.

Hough indexing struggles to index when pattern quality is reduced by a) high deformation, but b) spherical indexing is robust against significantly degraded pattern quality. Note that the d) spherical indexing confidence index strongly correlates with c) image quality but is high even in some regions with extremely low IQ (Figure 6).

Rutile

Figure 7. Excellent results are possible even with a single pattern center used for the entire dataset. Vignetting is visible in a) an IPF+IQ map of Hough indexing with a fixed pattern center. The field is flat over the entire area for b) an IPF+CI map of spherical indexing with a fixed pattern center.

Spherical indexing can use a unique pattern center for each point at no extra cost for large fields of view. Excellent results are possible even with a single pattern center used for the entire dataset, as shown in Figure 7. 

Deformed Duplex Steel

Figure 8. Phase discrimination depends on the similarity of the phases with a two-phase steel. In addition to the quality in orientation results with d – f) spherical indexing vs. a – c) Hough indexing, b – c & e – f) phase discrimination is improved with spherical BCC and FCC iron well separable.

Spherical indexing can be applied to multiple phases in the same way as any other indexing technique. Phase discrimination depends on the similarity of the phases with a two-phase steel shown in Figure 8. In addition to the quality in orientation results with d – f) spherical indexing vs. a – c) Hough indexing, b – c & e – f) phase discrimination is improved with spherical BCC and FCC iron well separable. Real space refinement may be required for particularly difficult cases in addition to the spherical harmonic refinement shown.

Figure 9. a) spherical CI + IPF shows similar trends as b) Hough IQ + IPF.

Again, spherical indexing’s confidence index correlates well with pattern quality. In Figure 9, a) spherical CI + IPF shows similar trends as b) Hough IQ + IPF.

References

  1. Callahan, P. G., & De Graef, M. (2013). Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations. Microscopy and Microanalysis, 19(5), 1255-1265.
  2. Callahan, P. G., & De Graef, M. (2013). Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations. Microscopy and Microanalysis, 19(5), 1255-1265.
  3. Lenthe, W. C., Singh, S., & De Graef, M. (2019). A spherical harmonic transform approach to the indexing of electron backscattered diffraction patterns. Ultramicroscopy, 207, 112841.
  4. Hielscher, R., Bartel, F., & Britton, T. B. (2019). Gazing at crystal balls: Electron backscatter diffraction pattern analysis and cross-correlation on the sphere. Ultramicroscopy, 207, 112836.
  5. Sparks, G., Shade, P. A., Uchic, M. D., Niezgoda, S. R., Mills, M. J., & Obstalecki, M. (2021). High-precision orientation mapping from spherical harmonic transform indexing of electron backscatter diffraction patterns. Ultramicroscopy, 222, 113187.

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。

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.

Is lack of invention a side effect of COVID?

Dr. David Stowe, Senior Product Manager – EDS and SEM Products, EDAX/Gatan

My friends and family have always thought that, as a microscopist, I spend my working days in a darkened room staring at dimly lit screens or developing negatives. Yet, the reality of working for a commercial company in the electron microscopy business could hardly be more different—scientific meetings, workshops, and spending time with users have allowed me to travel the world and make friends with some of the most interesting people. It’s always been a source of wonder and amazement for my family that the microscopic world could provide so many opportunities to see our world at large!

Sadly, over the last two years, our daily routines have been aligned much more closely to those visions of darkened rooms and computer screens than we care to remember. However, for many of us, there does appear to be (sun)light at the end of the tunnel. In recent weeks, I attended my first in-person workshop in almost two years. Together with colleagues and more than 60 researchers, I traveled to Munich to attend the Gatan-EDAX Leading Edge Workshop held at the Allianz Arena (home to Fußball-Club Bayern München e. V.—known to many as Bayern Munich). As lovely as it was to visit such a famous sporting stadium, the enjoyment of attending scientific talks and engaging in exciting technical discussions with leading researchers far outweighed the attraction of the venue. The feedback from everyone who participated in the day was incredibly positive. Many of us walked away from the event with new ideas inspired by discussions that day.

One of my fears regarding the impact of COVID-19 on the scientific community is the impact that the lack of these in-person interactions has had on innovation, both in terms of new scientific ideas and technological advancements. While working from home, many of us have missed those stimulating ‘coffee break conversations’ with colleagues outside of our teams. To add, there has been a noticeable drop in interactions on virtual platforms at scientific conferences and commercial webinars, with many preferring to review material offline at a time of their choosing.

Fortunately for EDAX, the opportunity for engagement with others during the pandemic had rarely been as high. Since 2019, we have been joined in the Materials Analysis Division of AMETEK by Gatan; the exchange of ideas between the R&D and Applications teams of the two companies has been significant. Within the last year, we have already seen the first innovations arising from the interactions between the two companies with the announcement of EDAX EDS Powered by Gatan for elemental analysis in the transmission electron microscope, simultaneous EDS and cathodoluminescence spectroscopy, and the development of a workflow solution for lithium analysis in the scanning electron microscope.

Within the last month, our applications teams were able to quantify the lithium content in lithium-ion battery cathode materials for the first time using the lithium-composition by difference method (Li-CDM).

Figure 1. First quantitative analysis of lithium content in cathode materials using the Li-CDM technique.

Their analysis used a range of tools from EDAX and Gatan to prepare, transfer, and analyze the specimen in the scanning electron microscope, highlighting the potential for innovation by Gatan and EDAX working together.

Figure 2. The tools from EDAX and Gatan used for the quantitative analysis of lithium in metal oxide cathode materials.

With the imminent arrival of the latest Microscopy and Microanalysis conference in Portland, OR, I am sure we will learn far more about how the changes to working practices have impacted innovation in our world. I am excited to leave my darkened room and discover your latest works in electron microscopy. I am sure that all participants will enjoy and value the personal interactions that are so important for innovation.

Inflation Got You Down?  

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.

Stimulating Simulations

Dr. Stuart Wright, Senior Scientist, EDAX

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

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

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

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

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

Figure 1. Phase diagram for the feldspar group.

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

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

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

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

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

Table 2. Normalized dot products.

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

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

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

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

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

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

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

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

EDS in Life Science

Drew Griffin, Sales Manager – Northeast, EDAX/Gatan

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.

Example 1

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.[1]

Example 2

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. [2]

Example 3

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. [3]

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.

References

  1. Satoshi Hara, E. et al, Nanostructural analysis of distinct nucleation sites in pathological mineralization. RSC; Mater. Adv., 2021, 2, 4423.
  2. Moretti, E. et al (2013), In vitro effect of gold and silver nanoparticles on human spermatozoa. Andrologia, 45: 392-396. https://doi.org/10.1111/and.12028.
  3. 3. Gillet, C. et al, (2016) Subcellular localization of metal pools determined by TEM-EDS in embryo Arabiopsis thaliana mutants, EMC 2016.6740.

My Unique Lab Partner

Dr. Shangshang Mu, Applications Engineer, EDAX

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!

Setting the Standard for EBSD

Matt Nowell, EBSD Product Manager, EDAX

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.

电池研究:从探测锂到找到锂传输的路径,Gatan 和EDAX 助力我们用户的锂电池研究

Chang Lu, Application Scientist, EDAX/Gatan

自从我进入大学学习,并且从事能源相关的课题研究以来,无论是阅读的论文还是参加会议听的报告,“全球变暖”和“新能源”是两个出现频率非常高的词。为了缓解/避免因为全球变暖所带来的潜在气候灾难,各国政府和企业这些年里投资了大量的经费在新能源的研究上面。锂电池,就是其中一种新能源技术。你可能知道,许多政府都生成使用化石能源的燃油车在可见的未来会禁售,我们常用的四轮家电未来是属于特斯拉,理想,比亚迪等电动车。

然而,目前的充电并不像加汽油那么便利。而且如果你开过电动车,我想你或多或少都会对它冬天和夏天的里程数额变化产生很多微词。但是电动车是未来发展的方向。大学,研究所,企业,拿着资金,继续研发下去,我想未来的电池技术会有不同的发展,也会给我们带来惊喜。

自从我去年加入Gatan以来,除了Gatan的工作内容,我还负责一部分EDAX的产品。Gatan还有EDAX公司都是电子显微镜上材料表征解决方案的设备提供商。针对锂电池相关的表征和分析,我们有一系列的产品可以实现用户这样或那样的应用需求。去年,我们推出了针对锂元素表征的EDAX 能谱仪与Gatan OnPoint背散射电子探测器的联合表征方案。在这个解决方案中,我们可以将锂元素的检出限降低仅10倍,达到个位数质量百分比。同时表征能力不受锂元素的价态影响。

图1:EDAX 能谱仪与Gatan OnPoint 背散射探测器表征轻金属合金的锂元素面分布。

很多用户了解到了这个产品后都会因此好奇,为什么单用能谱仪(EDS)很难完整表征作为轻金属的锂元素(无论单质或化合态)。这背后的原因与电镜中X射线的发生机制以及EDS的窗体材料相关。简单来说,EDS信号的产生需要借助电子束敲掉元素最内层的电子,然后空位引起外层电子回填。回填后由于两层电子的能级差,产生对应差值能量的EDS信号。

图2:以Si元素  特征X射线的产生原理。原图来源:myscope.training

可想而知,对于锂元素而言,EDS只能探测锂金属,测不到锂离子(就一层两个电子)。此外,由于锂的特征X射线能量仅为55 eV,对于低能端的信号,市面上普遍的EDS聚合物窗体材料吸收率很低,所幸EDAX EDS独特的超薄Si3N4 窗体提供了更高的低能端X射线透过率(下图红线)。

图3:EDAX 独特的Si3N4窗体材料和市面厂商常用的聚合物窗体材料(红色曲线)的低能端X射线的透过率(绿色曲线)。横坐标单位:keV。

然而相同的问题,GATAN的能量过滤系统(GIF)从技术角度提供了不同的解决方案,电子能量损失谱EELS在探测锂元素上面则轻松的多。与EDS信号相比,EELS信号在EDS信号产生的第一步,也就是 “电子束与核外电子相互作用”这部分就开始产生信号。EELS的信号强度相比EDS强了很多,针对锂元素的表征,自然也比EDS方便很多。当然锂元素或者电池材料整体上很敏感,不耐电子束的辐照,这就对GATAN的GIF系统产生了额外的要求——信号收集快,灵敏度高,噪音低。

图4:Gatan 1069 Continuum K3系统。

上图展示的是Gatan 1069 Continuum K3 系统,得益于直接电子探测相机K3的高灵敏度,去年11月,中国南方科技大学的谷猛团队在Matter上面发表论文,在极低的电子束剂量下(10 pA)不光成功地表征了锂元素,还针对锂元素的ELNES谱图的精细结构进行了MLLS解析,最终在面分布中区分开了金属锂和表层被氧化的锂。Gatan 1069 GIF不光是探测到了锂元素,还识别出来了不同化学价态的锂元素。这项工作对研究锂电池里面的致命的死锂问题具有重要意义。

然而对于锂电池研究来说,探测锂元素只是第一步。更重要的内容其实是研究锂离子的流向,锂离子流向和传输路径决定了电池的能量密度,容量以及寿命等性质。但是如何描述锂离子的流向呢?这个问题其实对应着如何表征梳理锂电池正极材料内部的晶粒结构。正极材料晶粒的尺寸和特定的晶面以及晶界对锂离子的流向倾向存在一定关联。在去年底上线的ACS Nano文章中,日本松下公司的Yuki Nomura在原位透射电镜平台使用旋进电子衍射(PED,透射电镜上类似EBSD的晶体学表征方法)和Gatan Quantum 系列GIF扫描电极材料的相同区域,通过充电反应,展示不同时刻锂元素的实时分布情况与材料晶界,晶面的关系。对于特定的取向,锂离子在充电过程中流向明显的倾向,而另一些晶面和取向则对锂离子的移动存在明显的阻力。相信有了这样一篇论文开端,后续还会有更多的研究发表,帮助科研人员实现更为合理的材料晶体结构与化学成分设计。

松下公司的这篇论文中使用的PED很难不让人联想到扫描电镜上EBSD技术。毕竟PED 在透射电镜上面所能实现的功能,除了空间分辨率,EBSD在扫描电镜上都可以做到,甚至做的更好(比如,角分辨率)。当然,考虑到电子束的剂量,业内主流的基于CMOS闪烁体技术的EBSD探测器可能在表征锂电池材料上就有一些吃力了。针对这个问题,EDAX有一款基于直接电子探测技术的EBSD产品——Clarity。

图5:(a)使用常规EBSD参数采集的锂电池正极材料数据,实验参数:20 kV,1.6 nA。标定结果噪点多;(b)使用Clarity EBSD表征的锂电池正极材料数据,实验参数:10 kV,400 pA。标定结果细节充分;(c) Clarity EBSD 产品图

2020年8月,来自美国可再生能源实验室的Donal Finegan 团队就是使用 Clarity得到了锂电池NMC电极材料的取向,晶界和形貌信息。这些丰富的结构信息有助于研究人员确认晶粒间缺陷的产生机制,从而理解充放电过程中锂离子的走向还有正极材料晶格膨胀导致的电池容量降低的问题。此前,业界很多论文之前只是知道使用多晶,小晶粒的正极材料会有比较好的电池性能,可是具体多晶材料或者小晶粒中的那些特性导致的这个性能优势暂不明朗。而Finegan 的这项工作通过Clarity EBSD帮助我们找到那个可能有益的晶界结构,从而更为准确地指引人们设计电池材料。此外,EBSD还具有一个额外的优势,就是依托FIB电镜的加工能力,我们还可以实现3D-EBSD的表征,在三维尺度上面研究晶粒。这个功能是PED难以实现的。相信未来更多的基于三维的晶粒和晶界的研究会给我们带来更多的惊喜。

作为科学仪器公司的应用技术人员,我们期待可以对设备应用的领域进行更多的挖掘,将客户研究中遇见的实际问题当成我们个人提升技术水平和展示设备优越性能的契机。新的一年,我期待我们的GATAN和EDAX的设备在新能源,增材制造,超快电镜电子衍射,冷冻电镜新冠病毒解析等研究领域会带来更多出色的成果。而我也期待,通过我自己的学习和提高,可以从设备的应用角度给客户们带来更多的启发与思考,让我们的客户不光用起来我们的设备还能用好我们的设备。

参考文献:

  • [1] Han, Bing, et al. “Conformal Three-Dimensional Interphase of Li Metal Anode Revealed by Low Dose Cryo-Electron Microscopy.” Matter (2021).
  • [2] Nomura, Yuki, et al. “Lithium Transport Pathways Guided by Grain Architectures in Ni-Rich Layered Cathodes.” ACS nano (2021).
  • [3] Quinn, Alexander, et al. “Electron backscatter diffraction for investigating lithium-ion electrode particle architectures.” Cell Reports Physical Science 1.8 (2020): 100137.