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

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embedded in the rock (Figures 1,3) and as is typical for fossils from the area, much of the fossilised remains had been replaced by shiny sulphide crystals (Figure 2).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.

A Bit of Background Information

Dr. Jens Rafaelsen, Applications Engineer, EDAX

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

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

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

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

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

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


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

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

What Kind of Leaves Are These?

Dr. Bruce Scruggs, XRF Product Manager, EDAX

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

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

Gold leaf “Gold leaf'” embedded in resin

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

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

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

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.

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).