Minimum Detection Limit and Silicon Nitride Window

Dr. Shangshang Mu, Applications Engineer, EDAX

A couple of weeks ago, a question regarding the minimum detection limits (MDL) of our Energy Dispersive Spectroscopy (EDS) quantitative analysis was forwarded to me from a potential customer. This is a frequently asked question I get from customers during EDS training. We understand researchers are looking for a simple answer; however, they don’t get a straightforward answer from us most of the time. This is not because we don’t want to tell the customer the configurations of our systems, but detection limits depend on various factors, including detector window, geometry, detector resolution, collection time, count rate, and sample composition. The detection limit for a given amount of an element in different sample matrixes is not the same. For example, calcium in indium has a much higher detection limit than it has in carbon because calcium energy lines are heavily absorbed by indium, but not by carbon. The limit also changes if you have a bit more of a given compound in the sample. The limits are lower if the data collection time is doubled. So, it is impossible to provide a general MDL for an EDS system or even a given element, but we can calculate the MDL for a given spectrum.

This function is available in APEX™ Software for EDS version 2.0 or later. For each element identified in the spectrum, the MDL is given in the quantification table and flagged if it is below the detection limit (Figure 1). To determine the MDL for a given spectrum, one must look at the statistical significance of the signal above the background. We generally use the single-channel definition for peak and background counts.

Figure 1. Quantification table with MDL.

Figure 1. Quantification table with MDL.

Figure 2. Illustration of background and peak counts.

Figure 2. Illustration of background and peak counts.

For a given element to be above the significance level, it requires that the total number of counts on the peak NP be above background counts NB by a predetermined confidence, see Figure 2. For significance, we use 1.7 standard deviations (SD) in a one-tail test since we are only concerned about having counts above the threshold (Figure 3). A SD of 1.7 corresponds to about 95% confidence for a single-tail.

Figure 3. Single-tail normal distribution. NB is the background mean level.

Figure 3. Single-tail normal distribution. NB is the background mean level.

The significance level can be calculated as:

NS=NB+1.7σB= NB+1.7√(NB)

This means that the requirement for an element to be considered significant is:

NP≥ NB+1.7√(NB)

For the MDL calculation, we are considering the net counts on the peak (NP-NB). Analog to the significance level, it is required that the counts are above the background plus a significance level, but we are now considering net counts instead of gross counts.

NDL=NB+∆(NP-NB)

To calculate the error, we consider the error of the peak and the background. If an element is close to the detection limit, the number of counts are comparable to the background counts, and we can approximate the total error:

∆(NP-NB)=√(NP+NB)≈√(2NB)

Using a 2s/95% confidence level, we can write the count detection limit as:

NDL=NB+2√(2NB)~2.8√(NB)

With the count-based detection limit and assuming the counts are linear with concentration, the concentration MDL can be calculated from the concentration C of a given element in a spectrum:

MDL=2.8√(NB)*C/NP

As I mentioned earlier, the detector window is one of the most important factors determining the MDL. With the introduction of Silicon Drift Detectors (SDD) and the development of fast and low-noise pulse processors, EDS analysis has seen remarkable increases in throughput and reliability in the last decade. But one often overlooked aspect of the detection technology is the detector window. A variety of window technologies are available, including beryllium, polymer films, and the most recent addition by EDAX, silicon nitride. Due to the polymer window’s composition and thickness, a significant part of the low-energy X-rays is absorbed before reaching the X-ray detector. This absorption effect is vastly reduced in the range below 2 keV for the silicon nitride windows, as shown in Figure 4.

Figure 4: Transmission curves for silicon nitride and polymer windows measured using synchrotron radiation.

Figure 4. Transmission curves for silicon nitride and polymer windows measured using synchrotron radiation.

The MDL for spectra acquired from the same samples with different window configurations can be calculated by employing the derived equation above. This study was led by Dr. Jens Rafaelsen at EDAX using five different standards. To eliminate the detector resolution and response as a variable in the experiment, the window was removed from a standard detector, and exchangeable caps with silicon nitride and polymer windows were mounted in front of the electron trap. Figures 5 and 6 show the relative improvement in MDL for the window-less and silicon nitride window configurations compared to the polymer window. Figure 6 documents the silicon nitride’s superiority over the polymer window in the low energy range with improvements of over 10% for the MDL of oxygen. While Figure 5 shows that further improvements can be gained in the window-less configurations, the silicon nitride window still allows for the use of variable pressure mode and spectrum collection from samples exhibiting cathodoluminescence (CL).

On a side note, our friends at Gatan recently captured fantastic EDS and CL data simultaneously from a meteorite thin-section using an EDAX Octane Elite EDS Detector and a Gatan Monarc CL Detector mounted on the same SEM. Check out the blog post written by Dr. Jonathan Lee to see how combined EDS and CL analysis can provide a glimpse into the history of our solar system’s evolution.

Figure 5. Relative gain in MDL for window-less configuration compared to polymer window.

Figure 5. Relative gain in MDL for window-less configuration compared to polymer window.


Figure 6. Relative gain in MDL for silicon nitride configuration compared to polymer window.

Figure 6. Relative gain in MDL for silicon nitride configuration compared to polymer window.

Microanalysis That’s Out of This World!

Dr. Jonathan Lee, Application Scientist, Gatan

Working as a cathodoluminescence (CL) application scientist at Gatan, I observe a great variety of interesting specimens from semiconductor devices, plastics, and geological samples to novel nanoscale optical devices demonstrating the capabilities of the Monarc® Pro CL detector. In case you don’t know, CL is the visible, ultraviolet, and infrared light emitted by many specimens in the scanning electron microscope (SEM). Recently, I was contacted regarding a meteorite sample and asked what analysis I could demonstrate using CL. As a physicist and amateur astronomer, I was naturally very excited at the rare opportunity to analyze something that literally came from out of this world! You might say I was… over the moon 🌙!

The sample is a thin-section from a meteorite collected from Antarctica – Miller Range 090010, you can read more about the classification here: Meteoritical Bulletin: Entry for Miller Range 090010 (usra.edu). Likely to have been a constituent of the asteroid belt, our specimen had a trajectory that eventually led it to fall to Earth. The study of these meteorites allows us to understand more about the age and history of our solar system. Given the origins and unusual conditions experienced by meteorites, the microstructure can be incredibly complex, but often, chondritic meteorites like this one contain calcium aluminum inclusions (CAIs) and corundum grains which are among the first solids to condense from the solar nebula! Now, before I get wrapped up with the Cosmic Calendar, let’s take a look at our specimen!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Boxes

Dr. Stuart Wright, Senior Scientist, EDAX

It has been a tough year for all of us – at times, I get cabin fever and feel boxed-in. The recent holiday break was a pleasant diversion. Even though we weren’t able to gather like we usually do, we did get to spend some time with a couple of our grandkids. As we opened gifts, per the usual stereotype, our youngest grandson had more fun playing with the boxes than the toys in them! Since today’s blog is on boxes, Figure 1 shows a picture of our granddaughter atop an old toy box. Yes, she is more than willing to pose for the camera.

My granddaughter atop a toy box I built many years ago.

Figure 1. My granddaughter atop a toy box I built many years ago.

So why the picture of a toy box? That toy box is 32 years old and has a tie-in to the development of EBSD (since I am getting older, I’m allowed to be a bit nostalgic.)

I joined Professor Brent Adams’ group as an undergrad at BYU in 1985. Brent was working on the orientation coherence function (OCF) at the time, which is a statistical description of crystallographic orientation arrangement within a polycrystalline microstructure. One of the Ph.D. students, T. T. Wang, went off to what was then the Alcoa Technical Center to make orientation measurements using selected area diffraction – a painstakingly slow process. He returned with a large set of Euler angles and a box of micrographs with numbered spots to indicate where the orientation measurements were from. My assignment was to digitize those micrographs – both to manually point-and-click each grain vertex and to write software to use those vertices to reconstruct and visualize the digital microstructure. Figure 2 shows one example from the set of 9 section planes. The entire set contained 5,439 grains and 21,221 boundaries. It was a lot of tedious work.

Digitized microstructure from aluminum tubing for work reported in B. L. Adams, P. R. Morris, T. T. Wang, K. S. Willden and S. I. Wright (1987). “Description of orientation coherence in polycrystalline materials.” Acta Metallurgica 35: 2935-2946.

Figure 2. Digitized microstructure from aluminum tubing for work reported in B. L. Adams, P. R. Morris, T. T. Wang, K. S. Willden and S. I. Wright (1987). “Description of orientation coherence in polycrystalline materials.” Acta Metallurgica 35: 2935-2946.

When Brent saw David Dingley’s presentation on EBSD at ICOTOM in 1987, he got very excited as he realized how much it could help with our group’s data collection needs. We got the first EBSD system in the US shortly after ICOTOM. It was installed on an old SEM in the botany department. The system was all computer-controlled, but it still required a user to manually (with the mouse) identify zone axes in each EBSD pattern to be indexed. It was a huge step forward for our research group. Brent quickly envisioned a fully automated system for site-specific orientation measurements. In 1988, Professor Adams moved to Yale University. I was fortunate to be invited to be a member of the research team that accompanied him. My wife and I boxed up our belongings and moved our small family of four from Utah to Connecticut for our new adventure.

The first few weeks at Yale were spent cleaning out an old laboratory space (some items even went to the Yale museum) in preparation for receiving our new CamScan SEM and the next generation EBSD system from David Dingley. When the SEM boxes arrived at the lab, we were very excited to see the microscope uncrated and installed. It was great to have our own microscope to work with, and we waited in eager anticipation for David’s arrival to install the new EBSD system. Unfortunately, I don’t have many photos from the Yale lab, but Figure 3 shows one with three of my colleagues in front of the SEM.

Brent Adams, John Hack, and Karsten Kunze in the SEM lab at Yale.

Figure 3. Brent Adams, John Hack, and Karsten Kunze in the SEM lab at Yale.

After everything was installed, there were a lot of wooden boards left over from all the crates in which the equipment was shipped. Being the stereotypical poor-starving student, I saw the wood as an opportunity. I diverted the bigger pieces of wood to my car instead of the dumpster and took them to our apartment. It was enough wood to build a toy box for each of our two kids (Figure 4).

Building a toy box with my kids while at Yale.

Figure 4. Building a toy box with my kids while at Yale.

A picture of the SEM at BYU (Brent returned to BYU after I graduated in 1992 and brought the system with him back to BYU) can be seen in Figure 5. Note all the boxes surrounding the instrument. In the very first system, instead of controlling the SEM beam, we moved the sample under a stationary beam using piezoelectric stages. In this photo, the camera was fixed so that it was always inserted into the microscope chamber, so there wasn’t a box to control the slide yet. Eventually, the stages were replaced with beam control, the SEM image could be viewed live on the workstation monitor, the camera was controlled through the computer, the image processing was done in the computer, the camera slide was controlled in software until we reached the modern, streamlined systems we are accustomed to today.

Photo of the first fully automated EBSD system in a lab at BYU (originally at Yale but later moved to BYU).

Figure 5. Photo of the first fully automated EBSD system in a lab at BYU (originally at Yale but later moved to BYU).

The old SEM was scrapped several years ago, but the two toy boxes are still in use and filled with “stuffies” as my granddaughter likes to say. So, just like the presents under the Christmas tree, the SEM boxes are still providing entertainment long after the toys they once held have been recycled into new ones 😊.

Improve the indexing rate – EDAX’s optimized EBSD solution

Dr. Sophie Yan, Applications Engineer, EDAX

As an applications specialist, I have encountered various problems over the years. There is always a common goal among EBSD users—to improve the EBSD indexing rate. Even a user who mainly tests relatively easy steel samples may run into deformed samples and intergranular precipitates that are difficult to calibrate, so they still need to improve the indexing rate. Ideally, we want to get a beautiful EBSD IPF map like Figure 1; however, the reality is that we often fail to get a map with such a high indexing rate.

IPF map with a very high indexing rate.

Figure 1. IPF map with a very high indexing rate.

Recently, I received a phone call from a customer asking for help. She had tricky ceramic samples with low crystallinity and fine grains, which are hard to index. The indexing success rate was only 5.48% from the area she tried to analyze (Figure 2). She wanted to see if we could improve it.

Ceramic sample with an indexing success rate of 5.48%.

Figure 2. Ceramic sample with an indexing success rate of 5.48%.

Of course, we can.

EDAX has a set of solutions to improve the indexing rate, as shown in Figure 3. If I had a direct detector like the Clarity™ EBSD Analysis System, I would obviously get better results. However, I only have a CMOS-based Velocity™ Super in my lab.

During the data collection process, users can optimize different parameters, such as background processing, or Hough parameters, to fit real-world samples. Combined with a unique hexagonal grid sampling and triplet indexing solution, EDAX gets a better indexing rate, which is very important for challenging samples.

If the data is not ideal, we can process the result using NPAR™. With NPAR, it averages the patterns to improve the indexing rate of challenging samples considerably. Also, in OIM Analysis™ v8.0 or higher, a module is available that can perform background processing again on the saved patterns to improve the indexing rate further.

EDAX’s optimized EBSD solution.

Figure 3. EDAX’s optimized EBSD solution.

I analyzed the sample and saved the patterns. Then I used OIM Analysis to post-process the patterns, as shown in Figure 4. The original pattern is quite fuzzy, and the bands were not clear. After NPAR processing, it improves the signal-to-noise ratio of the pattern, and the bands became clearer after further background processing.

(a) The raw pattern, (b)NPAR, (c) NPAR+dataset background, and  (d) NPAR+dataset background+dynamic background.

Figure 4. (a) The raw pattern, (b)NPAR, (c) NPAR+dataset background, and (d) NPAR+dataset background+dynamic background.

Of course, the processed patterns have indexing success rates. Figure 5 shows the IPF map of the data after a series of post-processing steps were taken, as described in Figure 4. The indexing success rate improved to 24.1%.

An IPF map with an indexing success rate of 24.1%.

Figure 5. An IPF map with an indexing success rate of 24.1%.

For this user’s case, the indexing success rate was greatly improved and was within an acceptable range. But to achieve our goal of improving the indexing rate of challenging samples, there is much more that needs to be done.

The above indexing success rates were achieved after CI >0.1 filtering. For those points with a CI <0.1 (the black areas in the IPF map), we can further process them. EDAX recently added OIM Matrix™, which includes dictionary indexing as a supplementary solution. As we all know, the result of dictionary indexing is usually better. I would expect a higher indexing success rate on the customer sample if I could use dictionary indexing to process it further.

If we push the limit, we can use the Clarity Direct Electron System to test this sample. In fact, the super-sensitive, low-beam current requirement is ideal for testing this type of sample. Maybe we can expect a better result with Clarity?

Will the result improve with Clarity?

Figure 6. Will the result improve with Clarity?

The goal of improving the indexing rate can be summed up in one sentence from a Chinese poem published in roughly 300 BC: The journey is long, but I will search up and down.

提高标定率——EDAX的EBSD解决方案

Dr. Sophie Yan, Applications Engineer, EDAX

作为应用,这些年,林林总总,我碰到各种问题。但是不能否认,对于EBSD用户来说,有一个共同的追求:提高EBSD的标定率。很少有用户没有这类困扰,即便用户主要测试的是相对容易的钢铁样品,可能也会有变形试样以及难以标定的晶间析出相;理想情况,当然是得到图1那样漂亮的EBSD图;但是受限于现实情况,我们往往不能如愿。

IPF map with a very high indexing rate.

图1:IPF图,标定率极高

最近我接到一个客户的求助电话,她有些很难的陶瓷样品,结晶程度不高,晶粒小,标定率很低。她尝试了一个区域,标定成功率只有5.48%;如下图2。她想看看有没有办法提高。

Ceramic sample with an indexing success rate of 5.48%.

图2:陶瓷样品,标定成功率5.48%

当然我们是有办法的。

EDAX对于提高标定率有一整套解决方案,如图3所示。从硬件选择开始,比如选择最新款直接电子检测相机Clarity,那这个样品明显会有更好的结果出现; 不过我的实验室只有CMOS相机Velocity Super……

采集过程,EDAX尽可能的开放了采集过程中的参数设置,如背底处理及Hough的参数,结合特有的六方步进处理及三条带组算法,使我们在实际操作过程中,参数更加贴近实际的样品情况,对于有挑战性的样品非常重要。

如果采集的数据结果不理想,我们可以对结果进行预处理。最有代表性的当属NPAR处理,由于对花样进行平均,能极大的提高挑战性样品的标定率。此外,在OIM8.0之后,OIM Ananlysis加入了新的模块,对保存后的花样能再次进行背底处理,进一步提升标定率。

EDAX的优化EBSD解决方案

图3:EDAX的优化EBSD解决方案

我对这个样品进行了EBSD分析,保存了花样。然后用OIMA对花样进行了预处理,见下图4.原始花样相当模糊,条带不太清楚。NPAR处理之后花样信噪比提升,条带变得较为清晰。再进一步进行背底处理,条带清晰程度进一步提升。

(a) The raw pattern, (b)NPAR, (c) NPAR+dataset background, and (d) NPAR+dataset background+dynamic background.

图4(a)原始花样 (b)NPAR (c) NPAR+dataset bkg (d) NPAR+dataset bkg+dyn bkg

使用这样处理过的花样进行标定,结果当然会有所不同。下图5是我进行图4中一系列后处理后标定的IPF图。标定成功率24.1%。

An IPF map with an indexing success rate of 24.1%.

图5,IPF图,标定成功率24.1%

对这个用户,整件事情告一段落:标定成功率有了大的提升,这个结果已经在可接受的范围。但是对于我们致力于提升挑战性样品标定率的目标,其实这件事还大有可为。

以上的标定成功率都是CI>0.1过滤之后的数值。对于CI<0.1的那些点,亦即IPF图中那些黑色的区域,其实我们可以对它进一步处理:EDAX近期引入了词典算法辅助标定,作为标定算法的一部分。众所周知,词典算法标定率极高,如果能用词典算法进一步标定,相信会有更好的结果。我期待着词典算法能给这个样品更高的标定率。

如果将手段用到极致——如果有可能的话,用EDAX全新发布的探测器Clarity测试这个样品,这个超灵敏,低束流的要求其实很适合测试这一类样品——可能结果也非常值得期待?

这样看来,提升标定率这一目标,也好像适用于那一句话: 路漫漫其修远兮,吾将上下而求索?

Will the result improve with Clarity?

图6.清晰度会改善结果吗?

Thanksgiving

Dave Durham, Western Region Sales Manager, EDAX/Gatan

We are quickly approaching that special season where we are encouraged to momentarily put aside our busy schedules and take an inventory of the things in our lives that we may not have had a chance to appreciate throughout the year. Considering the pandemic we’ve all been experiencing during the majority of 2020, I think it is especially important to stay optimistic and find the positive things that have materialized during this challenging and weird year.

Professionally, as a salesperson for the company, I am undoubtedly very thankful for the fact that the team at EDAX has had the resolve to release several new and compelling products this year. Amazing! Even considering the challenges of 2020, there has been a steady stream of recent upgrades and technology that have allowed us to provide our customers with groundbreaking tools to make their work and research even more successful. All this during a period where, I would have thought, very little innovation would be introduced in the field.

First was the release of APEX™ 2.0 for Energy Dispersive Spectroscopy (EDS) and Electron Backscatter Diffraction (EBSD). This was a substantial upgrade to the APEX Software interface, integrating it with our EBSD product line, allowing our customers to analyze their sample’s compositional and structural characteristics, and implementing a handful of other critical improvements to the capabilities and functionality of the platform.

APEX Software user interface.

Figure 1. APEX Software user interface.

Then we announced the launch of the new Lambda™ WDS product line. These spectrometers utilize a proprietary X-ray optical module to give them much better sensitivity at low energies and extend the energy limit beyond 15 keV, giving them superior performance in compositional analysis within WDS applications.

Lambda WDS Analysis System.

Figure 2. Lambda WDS Analysis System.

We followed that up with another huge announcement – the release of our Clarity™ EBSD Analysis System. The Clarity is the world’s first commercially available direct detector of its type designed for EBSD, ideal for operating at low currents and low voltages, where typical phosphor-based EBSD technology is unable to collect usable EBSD patterns. This detector truly opens a new window into sample types and applications that have never been possible with EBSD analysis. Very impressive!

Clarity EBSD Analysis System.

Figure 3. Clarity EBSD Analysis System.

Lastly, we released OIM Analysis™ v8.5, an improved version of our renowned post-processing analysis software for EBSD. This new revision added compatibility with APEX 2.0 and support for OIM Matrix™, for dynamic pattern simulation and dictionary indexing, as well as a few significant upgrades to user functionality and ease-of-use.

Schematic of the dictionary indexing processes in OIM Matrix using a library of simulated patterns.

Figure 4. Schematic of the dictionary indexing processes in OIM Matrix using a library of simulated patterns.

I want to give my sincere thanks to all the folks at EDAX who played a part in bringing each of these products to fruition in 2020. I appreciate the hard work you put in this year, in addition to the multiple years it takes to bring new products to market. I’m thankful that you’ve made my job easier as a salesperson, helping me keep customers excited and engaged with new products. And you’ve also played a significant part in advancing our customer’s research and productivity.

On a side note, I’d be remiss if I didn’t also say that I was thankful for the new sample preparation instruments, the Ilion II and PECS II, added to our product portfolio the AMETEK acquisition of Gatan this year. While the instruments themselves were not released in 2020, they are “new” to me, and I am very excited to introduce them to our customers moving forward. I believe they will allow the EBSD community to spend significantly less time preparing their samples for analysis while providing substantially better patterns than what they’re used to seeing through typical sample preparation techniques. We recently released an experiment brief on the subject.

(left) Gatan Ilion II System and (right) Gatan PECS II System.

Figure 5. (left) Gatan Ilion II System and (right) Gatan PECS II System.

Finally, I’m thankful for my health – I’ve lost about 15 pounds this year and feel like I’m in the best shape I’ve been in two decades. I’m also very thankful for my family, kids, and friends, whom I love and have loved me and supported me through all of 2020’s ups and downs. When I think of everything that has been going on in the world and how there are still so many good things going on in my life, considering all of the things that could have taken a turn for the worst, I’m thankful for that too. And all of that makes me enthusiastic and hopeful for a better year in 2021.

What are you thankful for?

Dave Durham and his three children.

Figure 6. Dave Durham and his three children.

Do Vintage Toy Cars Contain Lead?

Dr. Shangshang Mu, Applications Engineer, EDAX

Collecting die-cast toy cars is a childhood hobby that I picked up again twelve years ago. As kids play with Hot Wheels in the United States, you are sure to remember Matchbox toy cars if you were a kid in the 1980s and 1990s in China, like me. The brand originated in the United Kingdom and was given its name because the original die-cast toy cars were sold in boxes similar to those in which matches came in. I stepped into this mini world at the age of four when my father bought me my first Matchbox toy car. During my adolescence, I enjoyed exploring my gradually growing collection. Many years later, when I was in graduate school, these toy cars captured my attention again while I was shopping for groceries. I ran into a small section with some Hot Wheels and Matchbox cars hanging on the pegs. I was so excited to see that my favorite childhood toy brand was still alive and immediately reconnected with my old hobby.

Besides collecting toy cars released in the current year, I started to search on the internet to re-collect the same un-opened models that became worn and even destroyed in my childhood. Soon, I expanded my collection to include toy cars made in the 1970s and even 1960s and started to collect detailed scale model cars that are about the same size. Although collecting Matchbox or Hot Wheels cars is a hobby that attracts a lot of adult fans around the world, these cars are toys that do not have small parts, and all the vehicle types are about three inches in length, regardless if it is a passenger car or a truck (Figure 1). On the other hand, matchbox-sized detailed model cars are classified as 1/64 the size of the actual automobile, with many small parts that are only suitable for ages fourteen and up. 1/64 scale models bring back memories in another way because I am collecting models of classic cars and trucks from the era in which I grew up. Figure 2 shows some impressive cars from my childhood and a fire engine from my neighborhood in Boston.

A vintage railway playset from 1979 that my daughter likes to play with, and some toy cars ranging from the 1970s to 2010s.

Figure 1. A vintage railway playset from 1979 that my daughter likes to play with, and some toy cars ranging from the 1970s to 2010s.

Some matchbox-sized detailed models (1/64 scale) of the cars and trucks that I grew up with.

Figure 2. Some matchbox-sized detailed models (1/64 scale) of the cars and trucks that I grew up with.

Sometimes my five-year-old daughter rolls my toy cars on racetracks to figure out which one is the fastest. She also likes playing with my vintage railway playset. As a parent, my daughter’s interest made me a little concerned about lead paint since some of the toy cars she plays with were manufactured decades ago. For example, the railway playset dates back to 1979. Safety standards have been changed and revised over time, so I decided to figure out if these toys are lead-free. As an Applications Engineer at EDAX, I had more than one choice of material characterization technique. The Orbis Micro-XRF Analyzer can do non-destructive elemental analysis with the flexibility to work across a wide range of sample types and shapes, meaning I could put the toy cars directly into the analyzer to get the results. At that time, I was in the middle of testing new features in our new APEX™ 2.0 Software for EDS, so I decided to go with Energy Dispersive Spectroscopy (EDS) to give the new Batch Mode feature a try. With the benefits of EDS analysis and the Batch Mode feature in the APEX 2.0 Software, I was able to load all the paint samples into the SEM chamber and run them all at once using an Octane Elite Silicon Drift Detector. I scratched a tiny paint chip from each toy car and stuck it on a 25 mm adhesive carbon tab. Overall, I got 28 samples to analyze, ranging from the 1960s to the 2010s. They were mostly Matchbox, including the cars my daughter plays with, but some were also from other major toy car brands sold in the United States (Figure 3).

A 25 mm adhesive carbon tab with paint samples from my toy cars

Figure 3. A 25 mm adhesive carbon tab with paint samples from my toy cars

The Batch Mode operation allows you to collect data sets at different stage positions as a batch operation. Since the paint samples were hand stuck on the tab, the distance between adjacent samples was relatively large, and a single field of view was only able to show one sample. The Batch Mode feature’s automated stage movement was extremely useful in covering the paint samples all over the carbon tab in one operation batch. I was able to store all the paint samples in a batch list, set up collection parameters (Figure 4), and click on the Collect button to wait for all the samples’ results. Fortunately, the results show that all the samples I analyzed do not contain lead. The identified characteristic peaks were correlated to the paint samples’ colors; titanium dioxide and zinc oxide were white, carbon was black, and sulfur-containing sodium silicate was blue (Figure 5).

The growing batch list of the paint samples.

Figure 4. The growing batch list of the paint samples.

Selected SEM images and spectra overlay of the paint samples. The arrow indicates that no Pb L peak (10.55 keV) is present.

Figure 5. Selected SEM images and spectra overlay of the paint samples. The arrow indicates that no Pb L peak (10.55 keV) is present.

On a side note, it was relatively easy to identify a single element from a bunch of spectra that the energy region around the lead peak was pretty clean without any overlapping peaks. I simply had to overlay all the spectra together and see if the lead peak stuck up from the background. If you need to identify multiple compounds of contaminants from various samples, examining every spectrum or doing quantification analysis and comparing how close these numbers are over and over again is very time-consuming. An easy solution is to use the Spectrum Matching feature provided by the APEX 2.0 Software. You can collect spectra from those contaminants to build a library for them first, and then you can run Spectrum Matching to compare the unknown samples to the library. If Spectrum Matching finds more than three matches for an unknown sample, it will display the top three matches with numerical values of fit% for each unknown sample. This feature provides a remarkable benefit in improving the efficiency of your experimental work.

Now, I can stop worrying about the toxic component and let my daughter play with the vintage toy cars as she likes. My only concern is that some are hard to find now, so be careful and don’t break my vintage toy cars!

Be Direct When You Detect!

Fred Ulmer, South East Regional Sales Manager, EDAX/Gatan

Roughly 10 years ago, I was introduced to the exciting world of research using Transmission Electron Microscope (TEM)/Scanning Electron Microscope (SEM) principles. Working first as a Gatan field service engineer, then service manager. It was my first crash course in these research principles. It was a lot to take in at the time, but the excitement and enthusiasm shown by a customer when they have their new piece of equipment installed and begin to generate data was such a payoff. It seems like every year that there is a new, exciting technique or technology to apply to user’s research that enables researchers to keep getting better data.

Recently AMETEK purchased Gatan, which allowed for a great partnership between already owned EDAX and newly acquired Gatan. Also, I switched to sales from service at this time, becoming the South East Sales Manager with Gatan, and shortly after, I became the EDAX South East Sales Manager. Again, a lot to take in at the time, but it was rest assuring that EDAX, like Gatan, is at the forefront of TEM/SEM research.

One of the most technological advances I witnessed was the introduction of the K2 & K3 direct detection cameras for TEM from Gatan. This technology has allowed users to achieve data that was previously unheard of. From cryo-techniques to direct detection Electron Energy Loss Spectroscopy (EELS), these systems have become a game-changer.

Breakthrough K3 result: 2.7 Å structure of the 20S Proteasome with the K3 camera and Elsa cryo-holder on a TF20. Data courtesy of Alexander Myasnikov, Michael Braunfeld, Yifan Cheng, and David Agard.

Figure 1. Breakthrough K3 result: 2.7 Å structure of the 20S Proteasome with the K3 camera and Elsa cryo-holder on a TF20. Data courtesy of Alexander Myasnikov, Michael Braunfeld, Yifan Cheng, and David Agard.

Unsure of how, or even if direct detection could be used in the SEM world, it was exciting to get word from EDAX that they were releasing a direct detection EBSD analysis system called the Clarity™. This system is the world’s first EBSD detector based on direct detection technology. Current EBSD non-direct detection detectors have some drawbacks that include grain size and film thickness, causing localized blooming and some imaging artifacts in the EBSD patterns. So how does the Clarity overcome these drawbacks? It comes from the inherent design and technology of the detector. The Clarity does not require a phosphor screen or light transfer system. The technology uses a CMOS detector coupled to a silicon sensor. The incident electrons generate several electron-hole pairs within the silicon upon impact, and a bias voltage moves the charge toward the underlying CMOS detector, where it counts each event. This method is so sensitive that it can detect individual electrons. Coupled with zero read noise, the Clarity provides unprecedented performance for EBSD pattern collection. It can successfully detect and analyze patterns comprised of less than 10 electrons per pixel.

High-quality EBSD patterns collected with Clarity from a) silicon, b) olivine, and c) quartz.

Figure 2. High-quality EBSD patterns collected with Clarity from a) silicon, b) olivine, and c) quartz.

Intensity profile across (113) band from the Hikari Super (blue) and Clarity (red) detectors showing improved contrast and sharpness with direct detection.

Figure 3. Intensity profile across (113) band from the Hikari Super (blue) and Clarity (red) detectors showing improved contrast and sharpness with direct detection.

Direct detection will benefit many research areas like in‐situ microscopy, EBSD, 4D STEM, imaging beam sensitive materials, quantitative measurement of radiation damage, or quantitative electron microscopy. I am excited to see how the new generation of direct detection, like the EDAX Clarity, will continue to revolutionize the field of electron microscopy. Direct detection and electron counting are poised to advance electron microscopy into a new era. Let’s go direct detect!

EDAX Clarity EBSD Analysis System.

Figure 4. EDAX Clarity EBSD Analysis System.

Cover Worthy

Matt Nowell, EBSD Product Manager, EDAX

I firmly believe that one of the factors that has helped EBSD advance as a microanalytical technique is that it makes beautiful pictures. Of course, these images are packed with valuable information regarding the microstructure of materials. But in addition to this scientific content, they catch your eye. In our lab, we have taken advantage of this by hanging the covers of different journals and publications that feature EBSD images collected with EDAX equipment (Figure 1). Some of these are images we have collected internally, and others are from our customers. It is a fun reminder of interesting work that has been done over the years.

Our EBSD cover collection.

Figure 1. Our EBSD cover collection.

We have had an exciting past 18 months with the EBSD product line at EDAX. We launched our Velocity™ high-speed CMOS camera, which delivers greater than 4,500 indexed points per second. We released the APEX™ Software for EBSD, our new data collection platform with powerful analytical capability coupled with an easy-to-use interface. We introduced our groundbreaking Clarity™ EBSD Analysis System, which is the first commercial direct detection system designed for EBSD. As part of the development, testing, and marketing of these new products, I have used these products to collect thousands of images, some of which are utilized to highlight the performance of these new tools.

So how do you choose what makes a good EBSD image? The first step is often picking an interesting sample, but interesting is in the eye of the beholder. Some examples are selected because they use specific materials, like aluminum, magnesium, or steel. I like samples that have interesting microstructures. Sometimes, this is from a novel processing approach, like friction stir welding or equal channel angular processing. Sometimes, it is from a multi-phase microstructure, where structure and chemistry can be characterized simultaneously with EDS-EBSD. Sometimes, it is application focused. In this example, I have selected a sample because it is an additively manufactured nickel alloy. Additive manufacturing is a market with growing interest, and the microstructure is important because it influences the final properties of the material.

Figure 2 shows an Inverse Pole Figure (IPF) map of this material, collected with the Velocity Super at >4,500 indexed points per second. This IPF map is colored relative to the surface normal direction, and I have included a (001) pole figure to show the crystallographic texture and a colored IPF key to help decipher the relationship between the colors and the crystal orientations, which is good practice. This image is interesting because it shows a (001) fiber texture, which explains why many of the grains are shaded red. This helps researchers understand how these grains were growing during the additive manufacturing process. But is it visually appealing? That’s a question I often ask as I share these images for different possible uses.

IPF Map of an additively manufactured nickel alloy collected with the Velocity Super at >4,500 indexed points per second.

Figure 2. IPF Map of an additively manufactured nickel alloy collected with the Velocity Super at >4,500 indexed points per second.

One possible approach to improving the visual appeal of this map is to superimpose it with a grayscale image derived from other EBSD measurement metrics. Figure 3 shows the same IPF map combined with an Image Quality (IQ) map and a PRIAS™ (center) map. The IQ value is derived from measuring the brightness and sharpness of the diffraction bands within the EBSD patterns. The PRIAS map is calculated from the intensity of the signal onto an ROI positioned within the center of the EBSD detector. Both signals show microstructural contrast and add supplemental information to the IPF map.

IPF map combined with Image Quality (left) and PRIAS center (right) contrasts.

Figure 3. IPF map combined with Image Quality (left) and PRIAS center (right) contrasts.

How about the colors, though? Is it too red? I hear that sometimes, but I wonder if it is because of the rivalry between the University of Utah (red – where I went to school) and Brigham Young University (blue – where some of my co-workers went to school). What can I do about this? One approach is to specify the IPF map relative to a different direction than the surface normal direction. Figure 4 shows an IPF map where I have selected a [111] sample vector. While it is harder to relate this to the fundamental additive manufacturing process, it does show how you are not limited to specific sample directions. This can be useful if, for example, the thermal gradient present during processing it not aligned with the sample normal direction. In this case, it gives us a different color distribution representing the same microstructure. Is this better?

IPF map relative to the [111] sample direction.

Figure 4. IPF map relative to the [111] sample direction.

I have been looking at these maps for 25+ years now, so sometimes it is the new and novel that catches my eye. Figure 5 shows the same microstructure colored using a Quaternion Misorientation scheme. Here a reference orientation is used as a baseline, and the misorientation from this reference is used for coloring. Our OIM Analysis™ software has a wide range of different methods for visualizing microstructures. I personally really like the way this one looks. It is as much art as science.

IPF map with Quaternion Misorientation coloring.

Figure 5. IPF map with Quaternion Misorientation coloring.

When images meet those aesthetic criteria, they can be used for marketing, publications, covers, and even clothing. Figure 6 shows a scarf printed using an IPF from a skutterudite material. The crystallization of this material looks a bit like exploding fireworks. I have heard plenty of times that we should be in the tie or T-shirt business with the array of stunning images we can produce. I am always amazed that beyond visual appearance, the information on orientation, grain size and shape, deformation, and phase, among other things, that can be easily represented with EBSD. I hope to continue to find interesting examples to share with you. Special thanks to Tara Nylese for sharing the photo.

EBSD scarf/dog warmer.

Figure 6. EBSD scarf/dog warmer.

The Only Constant is Change

Matt Chipman, Senior Regional Sales Manager, EDAX

The seasons are changing here in the mountains of Utah. Autumn is at least one of my four favorites! I have made my home here, largely because of the drastic seasonal changes in climate and the ability to participate in gravity fed activities, like skiing and mountain biking. My personal life has become a game of maximizing my time in the mountains within the confines of what the weather and other commitments allow. Do I ride my bike at 5,000 feet elevation or 9,000 feet elevation? Do I pull out the skis or the fat tire bike for riding on the snow? Do I have to ride early in the morning when the ground is frozen to avoid the mud? Maybe I just escape to the desert for a weekend. No matter what the weather decides to throw at me, I have an answer. If I ever get bored, then mother nature will change things up for me soon enough. I have learned to adapt and enjoy the constant change.

A perfect autumn day on the trail.

Figure 1. A perfect autumn day on the trail.

Escaping to the desert. Maybe I will see Dr. Stuart Wright there.

Figure 2. Escaping to the desert. Maybe I will see Dr. Stuart Wright there.

 Not enough powder for skiing? No problem.

Figure 3. Not enough powder for skiing? No problem.

Recently in my professional life, I have had to apply some of the same attitudes toward change. After spending my entire career with TSL, then EDAX, then AMETEK; I decided to leave and work for Gatan about five years ago. I was just shy of my 20-year anniversary with EDAX. It was a nice change of pace and scenery. I really enjoyed learning new products and getting in touch with cutting edge Transmission Electron Microscope (TEM) research applications that Gatan is involved with. Then the climate changed and AMETEK acquired Gatan! Things had come full circle, just like the seasons. Fortunately for me, selling EDS and EBSD is like riding a bike (pun intended)! I now get to associate with some old friends again and sell both Gatan and EDAX products. I’m trying to convince myself that there are never too many products to sell, just like you can never have too much snow. However, sometimes I wish there was more time in the day.

It’s impossible to have too much snow!

Figure 4. It’s impossible to have too much snow!

I am looking forward to the constant change that will come with the combined power of EDAX and Gatan products. Can we offer Gatan sample preparation equipment to EDAX Scanning Electron Microscope (SEM) users? We sure can! Check out the latest EDAX Insight newsletter to see an example. Can we offer heating stages to EDAX SEM users? Absolutely! Can we leverage the power of Electron Energy Loss Spectroscopy (EELS) and Energy Dispersive Spectroscopy (EDS) together with diffraction for the ultimate microanalysis experience for TEM users? I hope so! It will take some work, but like climbing mountains, it will be so worth it!

You can’t enjoy the descent without the hard work of climbing.

Figure 5. You can’t enjoy the descent without the hard work of climbing.

I am looking forward to being able to offer my customers more solutions to their research problems. No matter which way the wind blows; I expect to have the answer in the form of the combined EDAX and Gatan product portfolio. What research problems are you trying to solve? Let’s see what we can do together. See you out there!

If you are interested in talking with a representative about how EDAX and Gatan products can help you, please contact us.