product management

Building an EBSD Sample

Matt Nowell, EBSD Product Manager, EDAX

Father’s Day is this weekend, and I like to think my kids enjoy having a material scientist for a father. They have a go-to resource for math questions, science projects are full of fun and significant digits, and when they visit the office they get to look at bugs and Velcro with the SEM. I’m always up to take them to museums to see crystals and airplanes and other interesting things as we travel around. That’s one way we have tried to make learning interactive and engaging. Another activity we have recently tried is 3D printing. This has allowed us to find or create 3D digital models of things and then print them out at home. Here are some fun examples of our creations.
At home we are printing with plastics, but in the Material Science world there is a lot of interest and development in printing with metals as well. This 3D printing, or additive manufacturing, is rapidly developing as a new manufacturing approach for both prototyping and production in a range of industries including aerospace and medical implants. Instead of melting plastics with a heated nozzle, metal powders are melted together with lasers or electron beams to create these 3D shapes that cannot be easily fabricated by traditional approaches.

In these applications, it is important to have reliable and consistent properties and performance. To achieve this, the microstructure of the metals must be both characterized and understood. EBSD is an excellent tool for this requirement.

The microstructures that develop during 3D printing are very interesting. Here is an example from a Ni-based superalloy created using Selective Laser Melting (SLM). This image shows a combined Image Quality and Orientation (IQ + IPF) Map, with the orientations displayed relative to the sample normal direction. Rather than equiaxed grains with easily identifiable twin boundaries, as are common with many nickel superalloys, this image shows grains that are growing vertically in the structure. This helps indicate the direction of heat flow during the manufacturing process. Understanding the local conditions during melting and solidification helps determine the final grain structure.
In some materials, this heating and cooling will cause not only melting, but also phase transformations that also affect the microstructure. Ti-6Al-4V (or Ti64) is one of the most common Titanium alloys used in both aerospace and biomedical applications, and there has been a lot of work done developing additive manufacturing methods for this alloy. Here is an IQ + IPF map from a Ti64 alloy built for a medical implant device.
At high temperatures, this alloy transforms into a Body-Centered Cubic (or BCC) structure called the Beta phase. As the metal cools, it transforms into a Hexagonal Closed Pack (HCP) structure, called the Alpha phase. This HCP microstructure develops as packets of similarly oriented laths as seen above. However, not all the Beta phase transforms. Here is an IQ + Phase EBSD map, where the Alpha phase is red and the Beta phase is blue. Small grains of the Beta phase are retained from the higher temperature structure.
If we show the orientations of the Beta grains only, we see how the packets relate to the original Beta grains that were present at high temperatures.
The rate of cooling will also influence the final microstructure. In this example, pieces of Ti64 were heated and held above the Beta transition temperature. One sample was then cooled in air, and another was quenched in water. The resulting microstructures are shown below. The first is the air-cooled sample.
The second is the water-cooled sample.

Clearly there is a significant difference in the resulting structure based on the cooling rate alone. As I imagine the complex shapes built with additive manufacturing, understanding both the local heating and cooling conditions will be important for optimization of both the structure and the properties.

Materials Selection While Black Friday Shopping

Matt Nowell, Product Manager EBSD, EDAX

I’m writing this blog the Monday after the Thanksgiving holiday, and having survived a Black Friday shopping adventure that started just a couple of hours after finishing the turkey last Thursday. While waiting in line for the doors to open and the tryptophan to wear off, I worked on plotting a strategy through the store to find a robotic vacuum cleaner, an Amazon Echo, some LED lights for outside, and the latest Minecraft toy for my youngest son. As the clock ticked towards 6 P.M., I felt confident in my plan and ready to go.

When the doors opened, and folks started streaming in, I grabbed a cart. This is always a tricky decision, as it immediately limits your mobility and possible escape routes. However, I knew ironically that the vacuum robot wasn’t going to push himself around quite yet. With cart in hand, I had to take a wider path, so I went a circuitous route to avoid the anticipated crowds, and ended up in housewares near where I expected the robot to be.

The first thing that caught my eye though wasn’t the vacuum cleaners, or the shiny Christmas plates, it was the cooking pans. It wasn’t the color, or size, or even price that piqued my interest, it was the material on the label: Titanium.

Now I’m no gourmet chef, but I generally don’t think of Titanium as a material used for pans. I’m more familiar with its applications in aerospace engine applications, in medical implants (see https://edaxblog.com/2014/01/22/bringing-oim-analysis-closer-to-home/), and in golf clubs. I’ve certainly polished more Titanium samples than I want to remember. Seeing these Titanium pans, it got me thinking about material selection, how material scientists must balance different materials properties (and cost) to match a material with an application, and where Titanium fits in the world of cooking.

One of my favorite cookbooks is “The Food Lab”, by J. Kenji Lopez-Alt, which has the sub-title “Better Home Cooking Through Science”. I’ve enjoyed reading this book because the author systematically tackles questions like “Is New York pizza better because of the water?” using the Scientific Method, and writes humorously about the results as well as providing delicious recipes. I’ve also taken to following him on Twitter (@TheFoodLab), and a recent post shows the pictures, (shown here as Figure 1), using an Infrared (IR) camera, of skillets made of different materials.

Figure 1. Heat distribution in pans made of different materials.

I found it a fascinating picture visualizing the heat distribution, derived from the thermal conductivity of the materials. Stainless steel is non-reactive, so you can cook anything in it. However, it doesn’t have the greatest thermal conductivity. Cast iron has a similar issue, and takes a while to warm up but once it’s hot, it stays hot, which is great for searing meat. Aluminum has better thermal conductivity, but also soft, scratchable, and can react with some foods. Copper is another material with excellent thermal conductivity, but it is reactive to certain foods. When confronted with these types of property challenges, material scientists like the best of both worlds, so composite pans have been made where copper and/or aluminum are sandwiched with steel to try and combine thermal performance with a non-reactive surface.

So what advantages does Titanium bring to this application? As with the aerospace and recreational applications, Titanium has a good strength to weight ratio. It’s lighter than steel and stronger than aluminum, as well as being corrosion-resistant. This means it’s the lightest cookware you can buy. Not necessarily the most important feature in the kitchen, but it does have value for cookware designed for camping.

All this thinking about optimization of thermal conductivity made me think about work done on thermoelectric materials. These materials convert a temperature differential into an electrical potential. Unlike these cooking pans, these materials want to minimize thermal conductivity while maximizing electrical conductivity. This is an interesting challenge. Thermoelectric properties can be optimized by increasing grain boundary density to disrupt phonon heat transfer. Figure 2 shows an EBSD IPF map of a Bismuth Telluride thermoelectric material that was made by shock-wave consolidation. This manufacturing process was investigated as a way to consolidate thermoelectric powders while retaining the nanostructure. More information can be found in our paper at: https://link.springer.com/article/10.1007/s11664-011-1878-4.

Figure 2. EBSD IPF map of a Bismuth Telluride thermoelectric material that was made by shock-wave consolidation

In the end, I decided not to buy a pan, but I did get the robot vacuum cleaner. I look forward to asking Alexa EBSD-related questions, just to see what happens. I also hope Santa brings me something that is microstructurally interesting, that perhaps I’ll use in my next blog.

A Bit of Background Information

Dr. Jens Rafaelsen, Applications Engineer, EDAX

Any EDS spectrum will have two distinct components; the characteristic peaks that originate from transitions between the states of the atoms in the sample and the background (Bremsstrahlung) which comes from continuum radiation emitted from electrons being slowed down as they move through the sample. The figure below shows a carbon coated galena sample (PbS) where the background is below the dark blue line while the characteristic peaks are above.

Carbon coated galena sample (PbS) where the bacground is below the dark blue line while the characteristic peaks are above.

Some people consider the background an artefact and something to be removed from the spectrum (either through electronics filtering or by subtracting it) but in the TEAM™ software we apply a model based on Kramer’s law that looks as follows:Formulawhere E is the photon energy, N(E) the number of photons, ε(E) the detector efficiency, A(E) the sample self-absorption, E0 the incident beam energy, and a, b, c are fit parameters¹.

This means that the background is tied to the sample composition and detector characteristic and that you can actually use the background shape and fit/misfit as a troubleshooting tool. Often if you have a bad background, it’s because the sample doesn’t meet the model requirements or the data fed to the model is incorrect. The example below shows the galena spectrum where the model has been fed two different tilt conditions and an overshoot of the background can easily be seen with the incorrect 45 degrees tilt. So, if the background is off in the low energy range, it could be an indication that the surface the spectrum came from was tilted, in which case the quant model will lose accuracy (unless it’s fed the correct tilt value).


This of course means that if your background is off, you can easily spend a long time figuring out what went wrong and why, although it often doesn’t matter too much. To get rid of this complexity we have included a different approach in our APEX™ software that is meant for the entry level user. Instead of doing a full model calculation we apply a Statistics-sensitive Non-linear Iterative Peak-clipping (SNIP) routine². This means that you will always get a good background fit though you lose some of the additional information you get from the Bremsstrahlung model. The images below show part of the difference where the full model includes the steps in the background caused by sample self-absorption while the SNIP filter returns a flat background.

So, which one is better? Well, it depends on where the question is coming from. As a scientist, I would always choose a model where the individual components can be addressed individually and if something looks strange, there will be a physical reason for it. But I also understand that a lot of people are not interested in the details and “just want something that works”. Both the Bremsstrahlung model and the SNIP filter will produce good results as shown in the table below that compares the quantification numbers from the galena sample.

Table

While there’s a slight difference between the two models, the variation is well within what is expected based on statistics and especially considering that the sample is a bit oxidized (as can be seen from the oxygen peak in the spectrum). But the complexity of the SNIP background is significantly reduced relative to the full model and there’s no user input, making it the better choice for the novice analyst of infrequent user.

¹ F. Eggert, Microchim Acta 155, 129–136 (2006), DOI 10.1007/s00604-006-0530-0
² C.G. RYAN et al, Nuclear Instruments and Methods in Physics Research 934 (1988) 396-402

What Kind of Leaves Are These?

Dr. Bruce Scruggs, XRF Product Manager, EDAX

This year is shaping up to be an interesting year for travel. Five countries and counting, and I’m not even including a stopover in Texas. The last trip was to Brazil. Beautiful country. But, there’s a reason you see snack and beverage vendors roaming the side of the highways in Rio and Sao Paulo..…

I started out with a micro-XRF workshop at the Center for Mineral Technology at the Federal University at Rio de Janeiro. We were working out of the Gemological Research Laboratory with Dr. Jurgen Schnellrath. At the end of the technical presentations, we analyzed some various pieces of jewelry that participants from the workshop brought. I must admit that this makes me a bit nervous to analyze anything with unforeseen sentimental value and I refuse to analyze engagement and wedding rings. A large pair of blue sapphire earrings turned out to be glass. (Purchased at a garage sale at a garage sale price. So, no big surprise …) Another smaller set of blue sapphire earrings were found to be natural sapphires accompanied by a sigh of relief from the owner. (They came from a reputable jewelry shop with a reputable jewelry shop price.)

Gold leaf “Gold leaf'” embedded in resin

At the end, we analyzed what was termed “gold leaf” jewelry, i.e. a ring and a pair of earrings. The style of these pieces was thin gold leaf foil embedded in resin. The owner was one of the younger students in the lab and she had purchased the jewelry herself from a relatively well-known designer’s collection. The goal was to measure for the presence of gold. Since the gold leaf was embedded in resin, XRF was the ideal tool to measure the pieces non-destructively. The jewelry also had some rather odd topography at times given the surrounding resin, but the Orbis had no problem to target the gold leaf given the co-axial geometry of the exciting X-ray and video imaging. I would have liked to have used the excuse that we couldn’t target the sample accurately because of XRF system geometry. There was no gold. Copper / Zinc alloy. That was it. She had paid about $30 US for the earrings and she said she felt cheated. I kept thinking “Cheated? Maybe … live a little, wait until you buy a house!” Later, I was searching the internet looking for a technical definition for “gold leaf”. I knew I was onto something when I found a webpage that said that gold leaf was “traditionally” 22K gold thin foil used for gilding. The page later described modern Copper/Zinc alloy metal leaf “… offering the same rich look of gold leaf, but at a fraction of the price….” Apparently, this metal leaf can be found at art stores. Who knew?

From there, we went on to the state of Sao Paulo and did a workshop at the Center for Nuclear Energy in Agriculture at the University of Sao Paulo. During the workshop, some of the students gave presentations on their work. I saw a very interesting experimental setup with live plants being measured in the Orbis. The plant’s roots were placed in a water bath doped with various forms of minerals or fertilizers. The whole plant, roots, stem, leaves, was then inserted into the Orbis and the stem was measured to monitor the uptake time for the relevant components in the bath. The plants could be moved in and out of the chamber to monitor the uptake over extended periods of time and over various portions of the plant.

On the way to the Sao Paulo airport, I had the pleasure of sitting in the longest traffic jam I have ever endured with the monotony being broken by roaming snack and beverage vendors. It was quite the sight to watch the peanut vendors carrying propane fueled peanut warmers traversing the lane dividers on the highway with the occasional motorcycle speeding between the cars along the same lane dividers.
Tip for next time … buy the Brazilian produced chocolate before going to the airport. The selection at the airport is rather limited and you never know when you may be having more fun than humans should be allowed to have watching motorcycles and peanut hawkers.

My New Lab Partner

Matt Nowell, EBSD Product Manager, EDAX

It has been an exciting month here in our Draper Utah lab, as we have received and installed our new FEI Teneo FEG SEM. We are a small lab, focusing on EBSD development and applications, and without a loading dock, so timing is critical when scheduling the delivery. So, 3 months ago, we looked at the calendar to pick a day with sunshine and without snow. Luckily, we picked well.

Figure 1: Our new SEM coming off the truck.

Figure 1: Our new SEM coming off the truck.

Once we got the new instrument up and running, of course the next step was to start playing with it. This new SEM has a lot more imaging detectors than our older SEM, so I wanted to see what I could see with it. I chose a nickel superalloy turbine blade with a thermal barrier coating, as it had many phases for imaging and microanalysis. The first image I collected was with the Everhart-Thornley Detector (ETD). For each image shown, I relied on the auto contrast and brightness adjustment to optimize the image.

Figure 2: ETD image

Figure 2: ETD image

With imaging, contrast is information. The contrast in this image shows phase contrast. On the left, gamma/gamma prime contrast is visible in the Nickel superalloy while different distinct regions of the barrier coating are seen towards the right. The next image I collected was with the Area Backscatter Detector (ABS). This is a detector that is positioned under the pole piece for imaging. With this detector, I can use the entire detector, the inner annular portion of the detector, or any of three regions towards the outer perimeter of the detector.

Figure 3: ABS Detector image.

Figure 3: ABS Detector image.

I tried each of the different options, and I selected the inner annular ring portion of the detector. Each option provided similar contrast as seen in Figure 3, but I went with this based on personal preference. The contrast is like the ETD contrast is Figure 2. I also compared with the imaging options using the detector in Concentric Backscatter (CBS) mode, where 4 different concentric annular detectors are available.

Figure 4: T1 Detector (a-b mode).

Figure 4: T1 Detector (a-b mode).

My next image used the T1 detector, which to my understanding is an in-lens detector. In this mode, I selected the a – b mode, so the final image is obtained by subtracting the image from the b portion of the detector from the a portion of the detector. I selected this image because the resultant contrast is reversed from the first couple of images. Here phases that were bright are now dark, and detail within the phases is suppressed.

Figure 5: T2 Detector.

Figure 5: T2 Detector.

My final SEM image was collected with the T2 detector, another in-lens detector option. Here we see the same general phase contrast, but the contrast range is more limited and the detail within regions is again suppressed.

I have chosen to show this set of images to illustrate how different detectors, and their positioning, can generate different images from the area, and that the contrast/information obtained with each image can change. Now I have done a cursory interpretation of the image contrast, but a better understanding may come from reading the manual and knowing the effects of the imaging parameters used.

Figure 6: Always Read the Manual!

Figure 6: Always Read the Manual!

Of course, I’m an EBSD guy, so I also want to compare this to what I can get using our TEAM™ software with Hikari EBSD detectors. One unique feature we have in our software is PRIAS™, which uses the EBSD detector as an imaging system. With the default imaging mode, it subsets the phosphor screen image into 25 different ROI imaging detectors, and generates an image from each when the beam is scanned across the area of interest. Once these images are collected, they can be reviewed, mixed, added, subtracted, and colored to show the contrast of interest, similar to the SEM imaging approach described above.

The 3 most common contrasts we see with PRIAS™ are phase, orientation, and topographic. To capture these, we also have a mode where 3 pre-defined regional detectors are collected during EBSD mapping, and the resulting images available with the EBSD (and simultaneous EDS) data.

Figure 7: PRIAS™ Top Detector Image.

Figure 7: PRIAS™ Top Detector Image.

The first ROI is positioned at the top of the phosphor screen, and the resulting phase contrast is very similar to the contrast obtained with the ETD and ABS imaging modes on the SEM.

Figure 8: PRIAS™ Center Detector Image.

Figure 8: PRIAS™ Center Detector Image.

The second ROI is positioned at the center of the phosphor screen. This image shows more orientation contrast.

Figure 9: PRIAS™ Bottom Detector Image.

Figure 9: PRIAS™ Bottom Detector Image.

The third ROI is positioned at the bottom of the phosphor screen. This image shows more topographical contrast. All three of these images are complementary, both to each other but also to the different SEM images. They all give part of the total picture of the sample.

Figure 10: Defining Custom ROIs in PRIAS™.

Figure 10: Defining Custom ROIs in PRIAS™.

With PRIAS™ it is also possible to define custom ROIs. In Figure 10, 3 different ROIs have been drawn within the phosphor screen area. The 3 corresponding images are then generated, and these can be reviewed, mixed, and then selected. In this case, I selected an ROI that reversed the phase contrast, like the contrast seen with the T1 detector in Figure 4.

Figure 11: PRIAS™ Center Image with EDS Bland Map (Red-Ni, Blue – Al, Green-Zr)

Figure 12: PRIAS™ Center Image with Orientation Map (IPF Map Surface Normal Direction).

figure-12a

Of course, the PRIAS™ information can also be directly correlated with the EDS and EBSD information collected during the mapping. Figure 11 shows an RGB EDS map while Figure 12 shows an IPF orientation map (surface normal direction with the corresponding orientation key) blended with the PRIAS™ center image. Having this available adds more information (via contrast) to the total microstructural characterization package.

I look forward to using our new SEM, to develop new ideas into tools and features for our users. I imagine a few new blogs posts should come from it as well!

Considerations for your New Year’s Resolutions from Dr. Pat

Dr. Patrick Camus, Director of Research and Innovation, EDAX

The beginning of the new calendar year is a time to reflect and evaluate important items in your life. At work, it might also be a time to evaluate the age and capabilities of the technical equipment in your lab. If you are a lucky employee, you may work in a newly refurbished lab where most of your equipment is less than 3 years old. If you are such a fortunate worker, the other colleagues in the field will be envious. They usually have equipment that is much more than 5 years old, some of it possibly dating from the last century!

Old Jalopy circa 1970 EDAX windowless Si(Li) detector circa early 70’s

In my case, at home my phone is 3 years old and my 3 vehicles are 18, 16, and 3 years old. We are definitely evaluating the household budget this year to upgrade the oldest automobile. We need to decide what are the highest priority items and which are not so important for our usage. It’s often important to sort through the different features offered and decide what’s most relevant … whether that’s at home or in the lab.

Octane Elite Silicon Drift Detector 2017 Dr. Pat’s Possible New Vehicle 2017

If your lab equipment is older than your vehicles, you need to determine whether the latest generation of equipment will improve either your throughput or the quality of your work. The latest generations of EDAX equipment can enormously speed up throughput and the improve quality of your analysis over that of previous generations – it’s just a matter of convincing your boss that this has value for the company. There is no time like the present for you to gather your arguments into a proposal to get the budget for the new generation of equipment that will benefit both you and the company.
Best of luck in the new year!

Adding a New Dimension to Analysis

Dr. Oleg Lourie, Regional Manager A/P, EDAX

With every dimension, we add to the volume of data, we believe that we add a new perspective in our understanding and interpretation of the data. In microanalysis adding space or time dimensionality has led to the development of 3D compositional tomography and dynamic or in situ compositional experiments. 3D compositional tomography or 3D EDS is developing rapidly and getting wider acceptance, although it still presents challenges such as the photon absorption, associated with sample thickness and time consuming acquisition process, which requires a high level of stability, especially for TEM microscopes. After setting up a multi hour experiment in a TEM to gain a 3D compositional EDS map, one may wonder Is there any shortcut to getting a ‘quick’ glimpse into 3-dimensional elemental distribution? The good news is that there is one and compared to tilt series tomography, it can be a ‘snapshot’ type of the 3D EDS map.

3D distribution of Nd in steel.

3D distribution of Nd in steel.

To enable such 3D EDS mapping on the conceptual level we would need at least two identical 2D TEM EDS maps acquired with photons having different energy – so you can slide along the energy axis (adding a new dimension?) and use photon absorption as a natural yardstick to probe the element distribution along the X-ray path. Since the characteristic X-rays have discrete energies (K, L, M lines), it might work if you subtract the K line map from the L line or M line map to see an element distribution based on different absorption between K and L or M line maps. Ideally, one of EDS maps should be acquired with high energy X-rays, such as K lines for high atomic number elements, and another with low energy X-rays where the absorption has a significant effect, such as for example M lines. Indeed, in the case of elements with a high atomic number, the energies for K lines area ranged in tens of keV having virtually 0 absorption even in a thick TEM sample.

So, it all looks quite promising except for one important detail – current SDDs have the absorption efficiency for high energy photons close to actual 0. Even if you made your SDD sensor as large 150 mm2 it would still be 0. Increasing it to 200 mm2 would keep it steady close to 0. So, having a large silicon sensor for EDS does not seem to matter, what matters is the absorption properties of the sensor material. Here we add a material selection dimension to generate a new perspective for 3D EDS. And indeed, when we selected a CdTe EDS sensor we would able to acquire X-rays with the energies up to 100 keV or more.

To summarize, using a CdTe sensor will open an opportunity for a ‘snapshot’ 3D EDS technique, which can add more insight about elemental volume distribution, sample topography and will not be limited by a sample thickness. It would clearly be more practical for elements with high atomic numbers. Although it might be utilized for a wide yet selected range of samples, this concept could be a complementary and fast (!) alternative to 3D EDS tomography.