EBSD

How to Increase Your Materials Characterization Knowledge with EDAX

Sue Arnell, Marketing Communications Manager, EDAX

The EDAX Applications and Product Management teams have been very busy offering free ‘continuing education’ workshops in September and October – with a great global response from our partners and customers.

At the end of September, Applications Specialist Shawn Wallace and Electron Backscatter Diffraction (EBSD) Product Manager Matt Nowell joined 6 additional speakers at a ‘Short Lecture Workshop for EBSD’, sponsored by EDAX at the Center for Electron Microscopy and Analysis (CEMAS) at The Ohio State University. The participants attended sessions ranging from ‘EBSD Introduction and Optimization of Collection Parameters for Advanced Application’ to ‘The Dictionary Approach to EBSD: Advances in Highly-Deformed and Fine-Grained Materials’.

Feedback on this workshop included the following comments, “This was a great learning opportunity after working with my lab’s EDAX systems for a couple of months”; “I like the diversity in the public and the talks.  I was very pleased with the overall structure and outcome”; and “Great! Very helpful.”

Matt Nowell presents at the ‘Short Lecture Workshop for EBSD’ at CEMAS, OSU.

In mid-October, EBSD Applications Specialist, Dr. Rene de Kloe traveled to India for a series of workshops on EBSD at the Indian Institute of Science (Bangalore), the International Advanced Research Center (Hyderabad), and the Indian Institute of Technology (Mumbai). Topics discussed at the sessions included:

• Effects of measurement and processing parameters on EBSD
• The application of EBSD to routine material characterization
• Defining resolution in EBSD analysis
• Three Dimensional EBSD analysis – temporal and spatial
• Advanced data averaging tools for improved EDS and EBSD mapping – NPAR™
• Microstructural Imaging using an Electron Backscatter Diffraction Detector – PRIAS™
• Transmission EBSD from low to high resolution

Dr. René de Kloe presents at one of three recent workshops in India.

According to our National Sales Manager in India, Arjun Dalvi, “We conducted this seminar at different sites and I would like to share that the response from all our attendees was very good. They were all eager to get the training from Dr. René and to take part in very interactive Q and A sessions, in which many analysis issues were solved.”

Global Applications Manager Tara Nylese was at the Robert A. Pritzker Science Center in Chicago, IL last week to give a presentation on “Materials Characterization with Microscopy and Microanalysis” for the Illinois Institute of Technology. “In this lecture, we started with a basic introduction to electron microscopy, and then dived deeper into the fundamentals of X-ray microanalysis. We explored both the basics of X-ray excitation, and how to evaluate peaks in an X-ray spectrum. From there, we looked at applied examples such as composition variation in alloys, chemical mapping of components of pharmaceutical tablets, and some fascinating underlying elemental surprises in biological materials.”

Finally, today we have 50 participants at the Geological Museum in Cambridge, MA for a training workshop given by Dr. Jens Rafaelsen and sponsored by Harvard University on “Taking TEAM™ EDS Software to the Next Level” * Presentation topics include:

• Basic operation of the TEAM™ EDS Analysis package
• How to get the most out of TEAM™ EDS Analysis
• Advanced training
• Tips and Tricks using TEAM™ EDS Analysis

Dr. Jens Rafaelsen presents at the Harvard workshop.

Here at EDAX, we are keen to provide our customers, potential customers, and partners with opportunities to improve their knowledge and polish their skills using the techniques, which are central to the EDAX product portfolio.  Our EDS, EBSD, WDS and XRF experts are keen to help with regular training sessions, webinars, and workshops. If you would like to be included, please check for upcoming webinarsworkshops, and training sessions at www.edax.com.

*A video of these workshop sessions will be available from EDAX in the coming weeks.

My Fossil Background

Dr. René de Kloe, Applications Specialist, EDAX

Call me old-fashioned, but when I want to relax I always try to go outdoors, away from computers and electronic gadgets. So when I go on vacation with my family we look for quiet places where we can go hiking and if possible we visit places with interesting rocks that contain fossils. Last summer I spent my summer vacation with my family in the Hunsrück in Germany. The hills close to where we stayed consisted of shales. These are strongly laminated rocks that have been formed by heating and compaction of finegrained sediments, mostly clay, that have been deposited under water in a marine environment. These rocks are perfect for the occurrence of fossils. When an organism dies and falls on such a bed of clay and is covered by a successive stack of mud layers, it can be beautifully preserved. The small grains and airtight seal of the mud can give a very good preservation such that the shape of the plant or animal can be found millions of years later as a highly detailed fossil. Perhaps the most famous occurrence of such fossil-bearing shale is the Burgess shale in British Columbia, Canada which is renowned for the preservation of soft tissue of long-extinct creatures. The Hunsrück region in Germany may not be that spectacular, but it is a lot closer to home for me and here also beautiful fossils have been found.

Figure 1. Crinoid or sea lily fossil found in the  waste heap of the Marienstollen in Weiden, Germany.

Figure 2. Detail of sulphide crystals.

Figure 3. Example of a complete crinoid fossil (not from the Hunsrück area). Source https://commons.wikimedia.org/wiki/File:Fossile-seelilie.jpg

So, when we would go hiking during our stay we just had to pack a hammer in our backpack to see if we would be lucky enough to find something spectacular of our own. What we found were fragments of a sea lily or crinoid embedded in the rock (Figures 1,3) and as is typical for fossils from the area, much of the fossilised remains had been replaced by shiny sulphide crystals (Figure 2). Locally it is said that the sulphides are pyrite. FeS2. So of course, once back home I could not resist putting a small fragment of our find in the SEM to confirm the mineral using EDS and EBSD. The cross section that had broken off the fossil showed smooth fracture surfaces which looked promising for analysis (Figure 4). EDS was easy and quickly showed that the sulphide grains were not iron sulphide, but instead copper bearing chalcopyrite. Getting EBSD results was a bit trickier because although EBSD bands were often visible, shadows cast by the irregular surface confuse the band detection (Figure 5).

Figure 4. Cross section of shale with smooth sulphide grains along the fracture surface.

Figure 5. EBSD patterns collected from the fracture surface. Indexing was done after manual band selection. Surface irregularities are emphasized by the projected shadows.

Now the trick is getting these patterns indexed and here I do like computers doing the work for me. Of course, you can manually indicate the bands and get the orientations of individual patterns, but that will not be very helpful for a map. The problem with a fracture surface is that the substrate has a variable tilt with respect to the EBSD detector. Parts of the sample might be blocking the path to the EBSD detector which complicates the EBSD background processing.

The EDAX EBSD software has many functions to help you out of such tight spots when analyzing challenging samples. For example, in addition to the standard background subtraction that is applied to routine EBSD mapping there is a library of background processing routines available. These routines can be helpful if your specimen is not a “typical” flat, well-polished EBSD sample. This library allows you to create your own recipe of image processing routines to optimize the band detection on patterns with deviating intensity gradients or incomplete patterns due to shadowing.

The standard background polishing uses an averaged EBSD pattern of more than ~25 grains such that the individual bands are blended out. This produces a fixed intensity gradient that we use to remove the background from all the patterns in the analysis area. When the actual intensity gradient shifts due to surface irregularities it is not enough to just use such a fixed average background. In that case you will need to add a dynamic background calculation method to smooth out the resulting intensity variations.

This is illustrated in the EBSD mapping of the fossil in Figure 6. The first EBSD mapping of the fossil using standard background subtraction only showed those parts of the grains that happened to be close to the optimal orientation for normal EBSD. When the surface was pointing in another direction, the pattern intensity had shifted too much for successful indexing. Reindexing the map with optimised background processing tripled the indexable area on the fracture surface.

Figure 6. Analysis of the fracture surface in the fossil. -1- PRIAS center image showing the smooth sulphide grains, -2- Superimposed EDS maps of O(green), Al(blue), S(magenta), and Fe(orange) -3- EBSD IPF on IQ maps with standard background processing, -4- original IPF map, -5- EBSD IPF on IQ maps with optimized background processing, -6- IPF map with optimized background.

In addition to the pattern enhancements also the band detection itself can be tuned to look at specific areas of the patterns. Surface shadowing mainly obscures the bottom part of the pattern, so when you shift the focus of the band detection to the upper half of the pattern you can maximize the number of detected bands and minimize the disturbing effects of the edges of the shadowed area. It is unavoidable to pick up a false band or two when you have a shadow, but when there are still 7-9 correct bands detected as well, indexing is not a problem.

Figure 7. Band detection on shadowed EBSD pattern. Band detection in the Hough transform is focused at the upper half of the pattern to allow detection of sufficient number of bands for correct indexing.

In the images below are a few suggestions of background processing recipes that can be useful for a variety of applications.

Of course, you can also create your own recipe of image processing options such that perhaps you will be able to extract some previously unrecognized details from your materials.

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.

Aimless Wanderin’ at the Meso-Scale (Part 2)

Dr. Stuart Wright, Senior Scientist, EDAX

If my memory is functioning correctly, I believe Val Randle coined the term “meso-texture” to describe the texture associated with the misorientations at grain boundaries.

I confess that, whenever I hear the term, I chuckle. This is because of a humorous memory tied to the first paper I was involved with. I was an undergraduate at Brigham Young University (BYU) at the time. The lead author, Brent Adams, later became my PhD advisor. The ideas presented in this work became the motivation behind my PhD work to automate EBSD.

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.

The paper describes some impressive work on the mathematical side by Brent and Peter and painstaking work by Tong-Tsung Wang who did hundreds of manual orientation measurements from individual grains in several planar sections of aluminum tubing using selected area diffraction. My role was digitize the microstructures in such a way that the two-point orientation correlations could be computed. The following is an example of one section plane from this work.

Digitized microstructure of one half of one section of a total of 10 sections used in the calculation of the orientation coherence function for aluminum tubing. Each grain number represents a individual grain orientation measurement.

The experimental work was a major undertaking. Thus, Brent Adams was so interested to hear David Dingley’s talk on EBSD at ICOTOM 8 in Santa Fe in 1987 shortly after this paper was published. Brent envisioned a fully automated system to link crystallographic orientation with microstructure via EBSD.

One of the interesting findings of this work was the discovery of a Meso-Structure:

“The strong implication of Table 2 is that there exists a new scale of microstructure in the material (and presumably in other polycrystalline materials) which has not previously been characterized, or even observed except in a qualitative manner. It seems appropriate to identify this new scale of microstructure as mesostructured since it clearly pertains to clusters or aggregates of grains or crystallites”

Greek statue who seems to be suppressing a chuckle.
Source: www.britannica.com/art/Archaic-smile

After this paper was published Brent received a letter from Sir Charles Frank. Sir Charles expressed his interest and appreciation for the ideas presented in the work. However, he objected to the term Meso-Structure. One of his objections was that “Meso” has its roots in Greek, but “Structure” is Latin. He didn’t like that we were mixing words of different etymological origins. I have to think this criticism was given “tongue in cheek” as the term microstructure with which Sir Charles was well familiar also mixes Greek and Latin. Thus, whenever I hear the term mesotexture used to describe grain boundary or misorientation texture I have to chuckle given it’s mix of the Greek “meso” and Latin “texture”.

I’m not sure what the best term is to describe the preferred misorientation of grain boundaries. The community uses the terms misorientation, disorientation, orientation difference and others sometimes as synonyms and at other times with differences in meaning. As all aimless wanderin’s tend to leave crisscrossing tracks, I note that my first exposure to the use of Rodrigues Vectors, which lend themselves well to describing misorientation, was by Sir Charles Frank at ICOTOM 8 in Santa Fe.

I hope my aimless wanderin’s through odd terminology and anecdotal history doesn’t leave you too disoriented 😊

(Next in this series are some ruminations on the term “3D texture”).

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).