Applications

XRF: Old Tech Adapting to New Times

Andrew Lee, Senior Applications Engineer, EDAX

X-rays were only discovered by Wilhelm Roentgen in 1895, but by the early 1900’s, research into X-rays was so prolific that half the Nobel Prizes in physics between 1914 to 1924 were awarded in this relatively new field. These discoveries set the stage for 1925, when the first sample was irradiated with X-rays. We’ve immortalized these early founders by naming formulas and coefficients after them. Names like Roentgen and Moseley seem to harken back to a completely different era of science. But here we are today a century later, still using and teaching those very same principles and formulas when we talk about XRF. This is because the underlying physics has not really changed much, and yet, XRF remains as relevant today as it ever was. You can’t say that for something like telephone technology.

XRF has traditionally been used for bulk elemental analysis, associated with large collimators, and pressed pellet samples. For many decades, these commercial units were not the most sophisticated instruments (although Apollo 15 and 16 in 1971 and 1972 included bulk XRF units). Modern hardware and software innovations to the core technique have allowed XRF to adapt to its surroundings in a way, becoming a useful instrument in many applications where XRF previously had little to offer. Micro-XRF was born this way, combining the original principles with newer hardware and software advancements. In fact, micro-XRF is included on the new NASA rover, scheduled for launch to Mars in 2020.

Biological/life sciences is one of those fields where possibilities are now opening as XRF technology progresses. A great example that comes to mind for both professional and personal reasons is the study of neurodegenerative diseases. Many such diseases, such as Parkinson’s, Alzheimer’s, and amyotrophic lateral sclerosis (ALS), exhibit an imbalance in metal ions such as Cu, Fe, and Zn in the human body. While healthy cells maintain “metal homeostasis”, individuals with these neurodegenerative diseases cannot properly regulate, which leads to toxic reactive oxygen species. For example, reduced Fe and Cu levels can catalyze the production of hydroxyl radicals which lead to damaged DNA and cell death. Imaging the distribution of biological metals in non-homogenized tissue samples is critical in understanding the role of these metals, and hopefully finding a cure. The common language between the people who studied physics versus the people who studied brain diseases? Trace metal distribution!

A few years ago, I had the opportunity to analyze a few slices of diseased human tissue in the EDAX Orbis micro-XRF (Figure 1 and 2), working towards proving this concept. Although the results were not conclusive either way, it was still very interesting to be able to detect and see the distribution of trace Cu near the bottom edge of the tissue sample. XRF provided unique advantages to the analysis process, and provided the necessary elemental sensitivity while maintaining high spatial resolution. This potential has since been recognized by other life science applications, such as mapping nutrient intake in plant leaves or seed coatings.

Figure 1. Stitched montage video image of the diseased human tissue slice, with mapped area highlighted in red. Total sample width ~25 mm.

Figure 1. Stitched montage video image of the diseased human tissue slice, with mapped area highlighted in red. Total sample width ~25 mm.

Figure 2. Overlaid element maps: Potassium{K(K) in green} and Copper {Cu(K) in yellow} from mapped area in Figure 1, showing a clear area of higher Cu concentration. Total mapped width ~7.6 mm.

Figure 2. Overlaid element maps: Potassium{K(K) in green} and Copper {Cu(K) in yellow} from mapped area in Figure 1, showing a clear area of higher Cu concentration. Total mapped width ~7.6 mm.

Sometimes, the application may not be obvious, or it may seem completely unrelated. But with a little digging, common ground can be found between the analysis goal and what the instrument can do. And if the technology continues to develop, there seems to be no limit to where XRF can be applied, whether it be outwards into space, or inwards into the human biology.

Aimless Wanderin’ in 3D (Part 3)

Dr. Stuart Wright, Senior Scientist, EDAX

In my research on the origins of the term texture to describe preferred lattice orientation I spent some time looking at one of the classic texts on the subject: Bunge’s “red bible” as we called it in our research group in grad school – Texture Analysis in Materials Science Mathematical Methods (1969). As I was reading I found an interesting passage as it relates to where we are with EBSD today:

“In a polycrystalline material crystallites of different shape, size and orientation are generally present. It can thus also occur that regions of different orientation are not separated from one another by unequivocally defined grain boundaries, but that, on the contrary, the orientation changes continuously from one point to another. If one desires to completely describe the crystal orientation of a polycrystalline material, one must specify the relevant orientation g for each point with coordinates x, y, z within the sample:

g=g(x,y,z)           (3.1)

If one writes g in EULER’s angles, this mean explicitly

φ_1=φ_1 (x,y,z);  Φ=Φ(x,y,z);  φ_2=φ_2 (x,y,z);           (3.2)

One thus requires three functions, each of these variables, which are also discontinuous at grain boundaries. Such a representation of the crystal orientation is very complicated. Where therefore observe that it has as yet been experimentally determined in only a very few cases (see, for example, references 139-141, 200-203), and that its mathematical treatment is so difficult that it is not practically applicable.”

I don’t quote these lines to detract in any way from the legacy of Professor Bunge in the field of texture analysis. I did not know Professor Bunge well but in all my interactions with him he was always very patient with my questions and generous with his time. Professor Bunge readily embraced new technology as it advanced texture analysis forward including automated EBSD. I quote this passage to show that the ideas behind what we might today call 3D texture analysis were germinated very early on. The work on Orientation Coherence by Brent Adams I quoted in Part 2 of this series was one of the first to mathematically build on these ideas. Now with serial sectioning via the FIB or other means coupled with EBSD as well as high-energy x-ray diffraction it is possible to realize the experimental side of these ideas in a, perhaps not routine but certainly, tractable manner.

A schematic of the evolution from pole figure-based ODF analysis to EBSD-based orientation maps to 3D texture data.

Others have anticipated these advancements as well. In chapter 2 of Rudy Wenk’s 1985 book entitled Preferred Orientation in Deformed Metal and Rocks: An introduction to Modern Texture Analysis it states:

“Pole figures and fabric diagrams provide information only about the orientation of crystals. It may be desirable to know the relation between the spatial distribution of grains and grain shape with respect to crystallographic orientation. Orientation relations between neighboring grains further defined the fabric and help to elucidate its significance.”

But let us return to the theme of aimless wanderin’s in texture terminology. The title for Chapter 4 of Bunge’s book is “Expansion of Orientation Distribution Functions in Series of Generalized Spherical Harmonics”. This chapter describes a solution the determination of the three-dimensional ODF (orientation distribution function) from two-dimensional pole figures. The chapter has a sub-title “Three-Dimensional Textures”. The three dimensions in this chapter of Bunge’s book are in orientation space (the three Euler Angles). What we call today a 3D texture is actually a 6D description with three dimensions in orientation space and three spatial dimensions (e.g. x, y and z). And those working with High-Energy x-rays have also characterized spatially resolved orientation distributions for in-situ experiments thus adding a seventh dimension of time, temperature, strain, …

It is nice to know in the nearly 50 years since Bunge’s book was published that what can sometimes appear to be aimless wanderin’s with mixed up terminology has actually lead us to higher dimensions of understanding. But, before we take too much credit for these advances in the “metallurgical arts”, as it says on the Google Scholar home page we “stand on the shoulders of giants” who envisioned and laid the groundwork for these advances.

Journey of Learning: Teaching Yourself the Power of EBSD

Shawn Wallace – Applications Engineer, EDAX

The joy of learning is sadly something that many people forget about and some never really feel. One of the things I like to keep in mind when I am learning something new is that learning is usually not a eureka moment, but a process of combining concepts and ideas already known, to reach a new solution or idea. The reason I was thinking about learning as a process is because recently I found myself forgetting that. A customer sample came in that was, for EBSD, hard in every way: Difficult crystal system/orientation, sample prep issues, poor diffractor. With all those factors, the sample was putting up a fight and winning, mainly because I allowed it to. I had tried all my normal tricks and was not making much headway. I knew the sample was analyzable, but I was not treating the process as a personal learning opportunity, instead I was treating it as a fight that I had to win. I was quickly bouncing from potential solution to potential solution and trying them, without spending much time on thinking what would be best to try and how to tackle the problem as a problem, and not a challenge. I didn’t even frame it that way in my own head until a week later when I was visiting a customer site to do some training.

During the training session, a sample came up with a very different set of problems, but still ones that were stymieing us as we sat at the microscope. I found the user resorting to what I had done previously; just try this and see if it works, without thinking about what the best course of action was. As I sat there, I told them to take a step back and evaluate what the issue was and how we could use our knowledge of all the functions available to us in the TEAM™ software and/or our microscope to find a solution. We sat and talked about the issue and the user was able to come up with a game plan and try some things that would help him reach a solution or gain additional knowledge, aka LEARN. I learned that day – that I sometimes need to treat myself the way I would treat a user. There will always be cases when I don’t know the answer and I have to teach myself the solution.

That leads us to an open question. How do you learn EBSD as you go along? With that in mind, here at EDAX we are going to start a new series of blog posts to discuss the basics of EBSD, from pattern formation, the Hough Transform, and finally indexing. More importantly, I hope to touch on how to troubleshoot issues using your newfound understanding of these concepts and tie the entire processes together as they all play off each other.

My final goal is get your creative juices flowing to dive deeper into understanding the kind of questions that EBSD can answer, and how that, in the end, can provide you with an incredible understanding of your analysis challenges and ultimately a solution to the problem. EBSD is one of the most powerful analytical techniques that I know. It can answer the simple questions (what phase is my sample?) to the incredibly complex (if I squeeze my sample this way, which grains will tend to deform first?). As your knowledge grows, EBSD is one step ahead of you, egging you on to learn more and more. I hope to be your guide on this Journey of Learning. I think I will learn quite a bit too.

Caveat Emptor – Especially with Microanalysis Samples

Matt Nowell – EBSD Product Manager, EDAX

My wife tells me I’m a bit of a hoarder. As we have done our spring cleaning, I’ve found coasters of places I’ve dined around the world, shirts a size (or more) smaller that I haven’t worn in years, and 2 Lego minifigures I bought and forgot to give to the kids. I’ve been forced to admit I didn’t need to keep all this any longer. Of course, as someone who develops and demonstrates EDS and EBSD microanalysis tools, the one thing you can never have too much of is interesting samples. I have drawers full of samples I’ve analyzed, or hope to analyze, and they come in handy when someone wants an interesting example for a customer or presentation.

With that in mind, I’d like to describe my adventures with a new sample I obtained this year. I found a bracelet online that claimed to have 62 elements. To me, that seemed wonderful, and potentially a great sample for EDS and EBSD analysis. I ordered one, and anxiously awaited its delivery.

When it arrived, and I opened it, I immediately became a bit suspicious. For the size and volume of material, it felt very light. I have a set of metal coupons that are all the same size but different alloys and materials, and there is a significant different in feel between different alloys. I guessed it was aluminum, but would use EDS and EBSD to determine the composition.

It was an interesting characterization problem though – potentially it contained 62 elements, but I didn’t know the concentration or spatial distribution of these elements. I started with EDS, and used my Octane Elite EDS detector. Initially I set up the SEM for 20kV analysis, with ≈15kcps output through the detector with ≈ 30% deadtime. Under these conditions, the resolution of the EDS detector was 122.8eV. I imaged a 600µm x 800µm area of the bracelet, and collected EDS spectra for 1, 10, 100, 1000, and 10,000 seconds. The signal to background increases as the square of the time collected, so for each 10X increase, I expected to improve the detection by about a factor of 3.

Figure 1. EDS Spectra collected for 10,000 Live Seconds

Figure 1 shows the EDS spectra collected for 10,000 live seconds. With careful review and analysis, I was able to identify 22 of the possible 62 claimed elements. Aluminum had the largest peak, and had the highest concentration. Of course, I knew I was only sampling the surface, and made no attempt to section into the sample. There was also a strong oxygen peak, which I would attribute to an oxidation layer. Most other detectable elements were present in smaller concentrations. Figures 2 and 3 show an energy range between 7.75eV – 9.00 eV, where the k-line peaks for nickel, zinc, and copper are present, for 10 and 10,000 live seconds of collection. These elements were selected because they were present in low concentrations. At 10 live seconds, these peaks are very noisy but present, and additional collection time significantly improves their distribution shape and counting statistics.

Figure 2. EDS Spectra collected for 10 Live seconds with 15kcsp output

Figure 3. EDS Spectra collected for 10,000 Live Seconds with 15kcsp output

Knowing that better counting improves lower limits of detection, I increased the beam current on the SEM to obtain ≈215kcps output counts, and then collected spectra over the same time intervals.* Figure 4 shows the collection under these conditions after 10,000 live seconds. I should note that while I analyzed the same size area, I did not analyze the exact same area, so it is possible any variations could be due to this approach.

Figure 4. EDS Spectra collected for 10,000 Live Seconds with 215kcps output

At this point, I had a lot of data, but increasing the count rate did not reveal any more elements than were initially detected. To evaluate performance, I quantified each spectra, and focused my analysis on the nickel, zinc, and copper elements. The weight percentage of each of these elements is shown in Figure 5 for each collection time and count rate. Each element has the same color (blue for Nickel, red for Zinc, and black for Copper), the lower count rate lines have a marker, while the higher count rate lines do not.

Figure 5. Weight percentage of selected elements as a function of acquisition time and output count rate

To me, this data was very impressive. Except for the 1 and 10 live second collections at the lower output count rate, the consistency of the data was good, even with concentrations of less than 1 weight percentage. The quantification output does give an error percentage value, and rule-of-thumb acceptance criteria was met after 100 live seconds collection at the lower count rate and 10 live seconds collection at the higher count rate. The fact that I continued to collect data for significantly longer times past this point would suggest that the remaining elements are either not-present, not at the surface where I am analyzing, or are present at concentrations lower than my detection limits.

I also wanted to look at this sample structurally, hoping for an interesting multiphase sample with pretty microstructures I could hang in the hall. I sectioned the sample, and polished a portion for EBSD analysis. The PRIAS + IPF Orientation map is shown in figure 6. I was able to index 99.7% of the collected points with high confidence using the aluminum FCC material file. It has a very large grain structure. I did see a number of smaller Fe precipitates, but I have not examined at higher magnification yet.

Figure 6. PRIAS + IPF Orientation map .

All in all, it didn’t turn out to be the sample I had hoped for, but was good to help think about collecting EDS data for both accuracy and sensitivity. I’ll have to share the sample with other colleagues for WDS and µXRF analysis to see if we can find more of these missing elements.

For more information on quantative analysis with EDS, join our upcoming webinar, ‘Practical Quantitative Analysis – How to optimize the accuracy of your data’. Please click here to register.

It’s a zoo in there!

Dr. René de Kloe, Applications Specialist, EDAX

For most of us EBSD users, our day to day experience is with metals, ceramics, or perhaps rocks. For man-made materials, analysis allows us to characterise the microstructure so that we can finetune the processing or fabrication of a material for a specific application. Another common use of EBSD data is for failure analysis where the crystallographic information can be coupled to external characterisation data and deformation structures such as cracks, welds, or ductile deformation features.

Figure 1. IPF map of partially recrystallized steel (left); IQ map of quartzite rock from the Pilbara region in Australia (right).

For natural materials like rocks, the questions start to get a bit trickier as we typically do not know exactly how a rock has come to exhibit the structures that it has. In combination with other tools, EBSD can then be an invaluable tool to add crystallographic and phase information to the puzzle. This allows researchers to piece together the deformation, temperature, and pressure history of the rock. This way tiny samples can provide insight in processes on a global scale like mountain building and the motion of the continents.

A third group of materials that gets a bit less attention in EBSD analysis are biominerals, materials that are formed with a certain degree of biological control to become part of an organism. In these biomaterials, the question is not how we have produced it, or how it could be finetuned to its intended application. Here the question is how biological processes have been able to optimise a material to such a remarkable degree and the EBSD analysis is used to try to understand the biological use and control of crystallisation. Unfortunately, we rarely get to look at structures that are produced by living organisms, except possibly fossils. One of the reasons that “fresh” biomineral structures are rarely studied with EBSD is that they often contain an organic fraction that makes electron microscopy samples susceptible to beam damage. To analyse such materials, the researcher must be very careful. A single pass with the electron beam is often all you get as the structure is easily damaged. In fossilised remains of animals, the organic component has been lost or replaced by solid crystals which make its analysis somewhat easier. For example, in recent years, papers have been published on crystalline lenses in the eyes of long extinct trilobites which were formed of calcite [1] and EBSD has also been used to estimate which areas of dinosaur eggs are most likely to represent the original microstructure such that the isotope ratios from these grains can be used to estimate the crystallisation temperature of the eggs [2].

A bit closer to us is perhaps the analysis of hydroxyapatite in bones. In the SEM image this cross section of a bone consists of a fibrous framework with brighter areas containing individual hydroxyapatite grains. What is not clear from such an image is if the grain orientations in these areas are all identical or perhaps exhibit random orientation. EBSD analysis clearly shows that the apatite grains occur in small clusters with similar IPF colours or equivalent orientations, which indicates that these smaller clusters are connected in the 3rd dimension in the material.

Figure 2. BSE image cross-section of bone (left); Hydroxyapatite IPF map on a single hydroxyapatite region in bone (right).

The recent introduction of the easy recording of all EBSD patterns during a scan and performing NPAR (neighbour pattern averaging and reindexing) during EBSD post-processing have allowed dramatic improvements in the analysis of beam sensitive materials. You still have to use gentle beam currents and relatively low kV to obtain the EBSD patterns. These patterns are then very noisy and the initial maps often show poor indexing success rates, but once these have been collected you are free to find the optimum way to analyse these patterns for the best possible results. For example, beam sensitive materials like the aragonite in the nacre of shells can be successfully analysed.

Figure 3. Calcite-aragonite transition the inside of a shell: original measurement (left); after NPAR reprocessing (right).

The aragonite-calcite phase map above on the left shows the initial results of an EBSD map of the inner surface of a shell over a transition zone from the calcite “framework” on the right to the smooth nacre finish on the left of the analysis area. Directly at the interface the EBSD pattern quality is so poor that it is difficult to interpret the microstructure. The phase map on the right is after NPAR reprocessing. Now the poorly indexed zone at the transition is much narrower and the map clearly shows how the aragonite starts growing in between the calcite pillars, then forms a thin veneer on top of the calcite until it gets thick enough to create euhedral planar crystals that form the smooth nacre surface at the inside of the shell.

Figure 4. Aragonite structure from pillars to nacre: original measurement (left); after NPAR reprocessing (right).

Figure 4 shows another shell structure which is now completely composed of aragonite. In cross section the structure resembles that of the calcite pillars with the nacre platelets on top, but the initial scans do not reveal any structure in the pillars. This could be taken as evidence that the crystal structure might be damaged and cannot be characterised properly using EBSD. However, after NPAR reprocessing the crystal structure of the pillars becomes clear and a feather-like microstructure is revealed.

These fascinating biological structures don’t appear often to the average materials scientist or geologist, but if you keep an open mind for unexpected structures you can still be treated to beautiful virtual creatures in or on your samples. For example, dirt is not always just in the way. Here it poses as a micron sized ground squirrel overlooking your analysis. And this magnetite duck is just flying into view over a glassy matrix.

Figure 5. Dirt patch in the shape of a ground squirrel (left); crystal orientation map of a magnetite duck flying through glass (right).

And what to think of these creatures, a zirconia eagle that is flying over a forest of Al2O3 crystals and this micron sized dinosaur that was lurking in a granite rock from the highlands of Scotland. Perhaps we finally found an ancestor of Nessie?

Figure 6. Zirconia EDS Eagle: in zirconia -alumina ceramic (left); on PRIAS bottom image (right).

Figure 7. Ilmenite-magnetite dinosaur in a granite rock.

It is clear that “biological” EBSD can occur in many shapes and sizes. Sometimes it is literally a zoo in there!

[1] Clare Torney, Martin R. Lee and Alan W. Owen; Microstructure and growth of the lenses of schizochroal trilobite eyes. Palaeontology Volume 57, Issue 4, pages 783–799, July 2014
[2] Eagle, R. A. et al. Isotopic ordering in eggshells reflects body temperatures and suggests differing thermophysiology in two Cretaceous dinosaurs. Nat. Commun. 6:8296 doi: 10.1038/ncomms9296 (2015).

My New Lab Partner Part 2 (East Coast Edition)

Jens Rafaelsen, Applications Engineer, EDAX

During a recent trip to our Draper lab in Utah for a training class, I got a first-hand look at Matt’s new lab partner (https://edaxblog.com/2017/02/14/my-new-lab-partner/). I must admit that I am a little envious of his new microscope and how easily you get great looking images (even at low acceleration voltage or high beam current) compared to the systems we have in our Mahwah lab. However, I must also admit that he needed an upgrade a lot more than we did. While his old XL has been very reliable (and still seems to be, even after moving it to another room), it was always a bit of a worry conducting a training class with only one microscope available and one that was at end of service life at that.

Around the time when Matt got his new microscope we also had an addition to our Mahwah lab as seen in the picture below:

OK, it’s definitely not an ARM or a TITAN, it only goes to 120kV, it’s not quite as new and fancy as Matt’s microscope, and the firmware might read 1994 when you hit the ON button, but it’s still good to have a TEM in the building once again. One of the things that’s great about older scientific instruments is that they often include full vacuum and wiring diagrams, schematics, and troubleshooting directions. Not so great: pressure readings in arbitrary numbers… I did some creative plumbing and mounted extra gauges on the line of the microscope gauges so now I know that a pressure reading in the buffer tank of 26 corresponds to roughly 10-1 mbar and that the camera chamber goes down to the mid 10-5 mbar. As an added bonus, several people in the building have been around long enough to have experience with the CM12 both as users and service and have had their memories jogged for how to run and align it. This also spurred the comment: “That’s right, this is why I decided to get out of field service…”.

Having had very limited TEM experience it’s been a bit of a learning curve for me but I think it’s getting there. There’s still a lot to learn when it comes to fine tuning of the instrument, diffraction, and aligning for dark field imaging, but at least I am able to get bright field images at over 500k magnification without spending too much time. And some of the images actually have somewhat decent resolution and recognizable features at that:

Holey carbon at 660.000x magnification

Of course, a lot of what we do at EDAX doesn’t really require great resolution or the newest instruments. While it’s always nice to have pretty pictures to go along with things, the X-rays don’t really care much about your astigmatism or spot size (unless you are trying to map of course). But there’s a significant difference in what you see in your spectra whether your electrons are hitting the sample with 15 kV or 120 kV. There are also very different considerations and limitations between a SEM and a TEM when it comes to actually mounting the detector, designing collimators, and even what materials can be used. With that being said, I hope that with my “new” lab partner we will move things along so that we can show you new applications, software, and hardware specifically for the TEM in the near future.

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!