microstructural analysis

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!

The Hough Transform – An Amazing Tool.

Shawn Wallace, Applications Engineer, EDAX

Part of my job is understanding and pushing the limits of each part of our systems. One of the most fundamental parts of the EBSD system is the Hough Transform. The Hough Transform role is finding the lines on an EBSD pattern. This is the first step in indexing a pattern (Fig. 1). If this step is not consistent, the quality of any indexing and any derivative data is questionable. A normal user does not really need to understand all the intricacies of every part of the system, but it still is worthwhile to understand how your data and data quality can be affected.

Figure 1: On the left are the overlaid lines found via the Hough Transform. On the right is the Indexed solution overlaid based on the Hough. The quality of the indexed solution is based on the quality of the Hough.

Figure 1: On the left are the overlaid lines found via the Hough Transform. On the right is the Indexed solution overlaid based on the Hough. The quality of the indexed solution is based on the quality of the Hough.

With that in mind, I ran an experiment on a steel sample to see how far the Hough could be pushed and still give consistent indexing. For this experiment, I used our Hikari Super at a series of different binnings between its native resolution of 640X480 Pixels at 1×1 binning down to 35×26 pixels at 18×18 binning. All pixel resolutions are noted in Table 1. I kept my Hough Settings and beam settings consistent. My only other variable was exposure to get the camera to be equally saturated at around 0.85 saturation.

I expected the lower binning Patterns to be consistent and they were (Fig. 2). All three Euler Angles between the 1×1, 2×2, 4×4, and 8×8, were within 0.4 degrees of each other. Pushing the camera and the Hough even further really surprised me though.

Figure 2: Indexed Pattern for the lower binning showed a remarkable consistency in indexing.

Figure 2: Indexed Pattern for the lower binning showed a remarkable consistency in indexing.

Figure 3: The indexing results still held their consistency even for highest binning settings used.

Figure 3: The indexing results still held their consistency even for highest binning settings used.

I expected some drop off with the consistency of the orientation when I dropped my binning to 10×10, 16×16, and even 18×18 and it did not fully materialize (Fig. 3). The range did broaden in the Euler Angles, specifically ᶲ₂’s range increased to 3 degrees, but that is change of <1% given the entire range for ᶲ₂ is 360 degrees. Table 1 shows the data is the raw form. Overall, the data is great, from low to high binning with minimal loss in in our indexing metrics (CI and Fit) and consistency in Euler Angles except for the 18×18 binning. That is where we have found our limit, specifically when it comes to indexing metrics. We see a sharp drop off in the CI. The pixilation of the pattern has gotten to a point where it is difficult to find a unique solution. This drop off is why we tell our customer that 16×16 is the limit of binning they should use for reliable, high quality data.

Table 1. Indexing Metrics and Euler Angles for all data points.

Table 1. Indexing Metrics and Euler Angles for all data points.

With all that said, most EBSD work is not on a single orientation, but a map. Does this hold true on a map? It does. In Figure 4 and Figure 5, we can see the mapping results for 2×2 binning and 10×10 binning. Both indexed at 99.9% with their average CI’s being 0.89 and 0.84 respectively, with very little change in orientations. This level of data quality across binnings is why EDAX uses the Hough. It is an amazing little tool.

Figure 4. This map was taken at 2x2 binning. Internal deformation of the grains is visible, with inclusions between relatively undeformed.

Figure 4. This map was taken at 2×2 binning. Internal deformation of the grains is visible, with inclusions between relatively undeformed.

Figure 5. This map was taken at 10x10 binning in approximately the same area as Figure 4. Again, internal deformation is showed in the larger grain, while the inclusions are undeformed.

Figure 5. This map was taken at 10×10 binning in approximately the same area as Figure 4. Again, internal deformation is showed in the larger grain, while the inclusions are undeformed.

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!

Molecular Machines are the Future…

René Jansen, Regional Manager, Europe

The ground in the north of Holland was recently shaking and not because of an earthquake, but because Professor Ben Feringa from the University of Groningen has won the 2016 Nobel Prize in Chemistry for his work on the development of molecular machines.
Feringa discovered the molecular motor — a light-driven rotary molecular motor – which is widely recognized as a spectacular scientific breakthrough.

Electrically driven directional motion of a four-wheeled molecule on a metal surface

Electrically driven directional motion of a four-wheeled molecule on a metal surface

‘Building a moving molecule is not that difficult in itself, but being able to steer it, have control over it, is a different matter.’, he said. Years ago he already presented the first molecular motor, consisting of a molecule, part of which performed a full rotation under the influence of light and heat. He has designed many different engines since, including a molecular ‘4-wheel drive’ car. By fixating the engine molecules to a surface, he developed a nano ‘mill park’ in which the mills rotate when exposed to light. And last year he described the world’s first symmetrical molecular engine. Feringa also succeeded in putting these molecular engines to work, having them turn a glass cylinder 10,000 times their size. Amazing.

Feringa is internationally recognized as a pioneer in the field of molecular engines. One of the potential applications of his engines is the delivery of medication inside the human body.
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I recently heard an interview with him, in which he promoted the idea that universities should be playgrounds, where scientists must be able to do whatever they want to create real breakthroughs. Today, the ability of universities to create these playgrounds is limited due to a constant reduction of budgets over recent years. It would be interesting to know how the University of Groningen has managed to do this.

Another, less famous, department at the University of Groningen is working on the formation/deformation of materials which are exposed to high temperature (> 1000 degrees Celsius). Measuring EBSD patterns while temperature increases, shows that new crystals are formed at a certain temperature. Now my hopes are that this “playground” too will end up in a few years from now with a Nobel prize for a breakthrough in Materials Science.

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.

Help!

Dr. René de Kloe, Applications Specialist EDS, EBSD, EDAX

The job of an applications engineer is to help people. Help sales people to explain to customers what a system can do. Help customers to get the most out of their system and help them to understand their materials better. Help the marketing group with nice examples. And help the development team to devise applications that have not been tried before.

One thing you need in order to be able to help is knowing the EDAX analysis systems inside-out. But the other thing you need is samples. Lots of samples. Every function or analysis tool in the software, regardless if it is for EDS, EBSD, WDS, or XRF is best shown with a specific material or combination of elements or phases. Some of these, like chemical standards with known composition, you have to make or perhaps buy. Others you have to collect yourselves, but from where? A great source for new materials are our customers. People often send me materials to evaluate our systems, or for help on how to best analyse their samples. When I then get permission to keep a bit of the material it goes directly into my collection, together with valuable information on the current analysis requirements in different scientific disciplines.

Eight phase FeSi alloy Brass with NiMnSi particles 

This goes a long way in getting good example materials, but I always keep my eyes open for new interesting things. When I see a metal strip in an anti-theft label in clothing I keep it (after buying the item of course), when a droplet of lead-tin solder falls on the floor, I stick it in the microscope to see if it looks good. I also scrutinize things that get thrown away, ranging from the lid of a vegetable jar to a damaged bellows of an EBSD system. That has given me beautiful cast aluminium samples for EDS mapping, multiphase brass alloys for ChI-Scan EDS-EBSD analysis, and recently an unexpected copper-plated zinc-aluminium-silicon alloy for EBSD phase identification from a broken belt buckle.

Grain structure of a staple Grain structure of a key ring 

Luckily I don’t always have to go dumpster diving to get my example materials. One of my favorite sample mounts contains different types of heavily deformed ferrite, duplex stainless steel, and also martensitic structures. That sounds perhaps complicated, but on the outside the same sample just looks like staples, a paperclip, a key ring, and a screw.

The screw, for example, I polished after doing some DIY work at home and because a certain type of screw kept breaking off when I tightened it, I wanted to take a close look why that happened. It turned out that there were lots of small cracks along the thread, which then also lined up with trails of carbides further inside the screw. That turned out to be a really bad combination and when you tighten the screw, the cracks propagate, connect with the carbide trails and the screw head snaps off. The replacement screws that I used instead had a much finer structure without any cracks and that is what is still holding things together in the house. This shows how microstructures literally shape our daily life. And it also provides a beautiful example to help illustrate the importance of microstructural characterization to new EBSD users.

Weak screw Strong screw

The huge variation in materials and microstructures makes the collection of demonstration samples the most important tool for an application scientist and from this place I hereby want to thank all people who have given me a piece of some material during my years at EDAX to use to help others.

By the way, I would appreciate it very much if the person who briefly “borrowed” my marble sample last year gives it back soon …

Metals, Minerals and Gunshot Residue

Dr. Jens Rafaelsen, Applications Engineer EDAX

During a recent visit to a customer facility I was asked what kind of samples, and applications I typically see. It would seem that this would be a pretty easy question to answer but I struggled to narrow it down to anything “typical”. Over the past three weeks I have spent a couple of days each week at customer facilities and I think a brief description of each of them will explain why I had a hard time answering the question.

The first facility I went to was a university in the process of qualifying an integrated EDS/EBSD system on a combined focused ion beam (FIB) and scanning electron microscope (SEM). A system like this allows one to remove material layer by layer and reconstruct a full 3D model of the sample. The dataset in Figure 1 illustrates why this information can be crucial when calculating material properties based on the grain structure from an EBSD scan. If one looks at the image on the left in the figure, it seems obvious that there are a few large grains in the sample with the area between them filled by smaller grains. However, the reconstructed grain on the right shows that several of these smaller “grains” seen in the single slice are actually interconnected and form a very large grain that stretches outside the probed volume.

Figure 1: Single slice EBSD map (left) and single reconstructed grain from 3D slice set (right).

Figure 1: Single slice EBSD map (left) and single reconstructed grain from 3D slice set (right).

While we spent a good amount of time documenting exactly what kind of speed, signal-to-noise, resolution and sensitivity we could get out of the system, one of the customer’s goals was to measure strain to use as a basis for material modelling. We also discussed a potential collaboration since our EBSD applications engineer, Shawn Wallace, has access to meteorite samples through his previous position at the American Museum of Natural History in New York and a 3D measurement of the grains in a meteorite could make a very compelling study.

Next up was a government agency where the user’s primary interest was in mineral samples but also slag and biological materials retained in mineral matrices. Besides the SEM with EDS they had a microprobe in the next room and they would often investigate the samples in the SEM first before going to the microprobe for detailed analysis (when and if this is required is a different discussion, I would recommend Dale Newbury and Nicholas Ritchie’s article for more details: J Mater Sci (2015) 50:493–518 DOI 10.1007/s10853-014-8685-2).

A typical workflow would be to map out an area of the sample and identify the different phases present to calculate the area fraction and total composition. Since the users of the facility work with minerals all the time, they could easily identify the different parts of the sample by looking at the spectra and quantification numbers, but I have a physics background and will readily admit that I would be hard pressed to tell the difference between bustamite and olivine without the help of Google or a reference book. However, this specific system had the spectrum matching option, which eliminates a lot of the digging in books and finding the right composition. The workflow is illustrated in Figure 2, where one starts by collecting a SEM image of the area of interest and, when the EDS map is subsequently collected, the software will automatically identify areas with similar composition and assign colors accordingly. The next step would then be to extract the spectrum from one area and match it up against a database of spectra. As we can see in the spectrum of Figure 2, the red phase of the EDS map corresponds to a obsidian matrix with slightly elevated Na, Al, and Ca contributions relative to the standard.
Figure 2a

Figure 2: Backscatter electron image (top left) and corresponding phase map (top right) showing different compositions in the sample. The bottom spectrum corresponds to the red phase and has an obsidian spectrum overlaid.

Figure 2: Backscatter electron image (top left) and corresponding phase map (top right) showing different compositions in the sample. The bottom spectrum corresponds to the red phase and has an obsidian spectrum overlaid.

The last facility I visited was a forensic lab, where they had purchased an EDS system primarily for gunshot residue (GSR) detection. The samples are usually standard 12.7 mm round aluminum stubs with carbon tabs. The sticky carbon tabs are used to collect a sample from the hands of a suspect, carbon coated and then loaded into the SEM. The challenge is now to locate particles that are consistent with gunshot residue amongst all the other stuff there might be on the sample. The criteria are that the particle has to contain antimony, barium and lead, at least for traditional gunpowder. Lead free gunpowder is available but it is significantly more expensive and when asking how often it is seen in the labs, I was told that apparently the criminal element is price conscious and not particularly environmentally friendly!

The big challenge with GSR is that the software has to search through the entire stub, separate carbon tape from particles down to less than 1 micron, and then investigate whether a particle is consistent with GSR based on the composition. The workflow is illustrated in Figure 3 and is done by collecting high resolution images, looking for particles based on greyscale value in the image, collecting a spectrum from each particle and then classifying the particle based on composition. Once the data is collected, the user can go in and review the total number of particles and specifically look for GSR particles, relocate them on the sample, and collect high resolution images and spectra for documentation in a potential trial.

Figure 3: Overview showing the fields collected from the full sample stub (top left), zoomed image corresponding to the red square in the overview image (top right) and gunshot residue particle from the red square in the zoomed image (bottom).

Figure 3: Overview showing the fields collected from the full sample stub (top left), zoomed image corresponding to the red square in the overview image (top right) and gunshot residue particle from the red square in the zoomed image (bottom).

Three weeks, three very different applications and a very long answer to the question of what kind of samples and applications I typically see. Each of these three applications is typical in its own way although they have little in common. This brings us to the bottom line: most of the samples and applications we come by might be based in the same technique but often the specifics are unique and I guess the uniqueness is really what is typical.