product management

A Bit of Background Information

Dr. Jens Rafaelsen, Applications Engineer, EDAX

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

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

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

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


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

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

Table

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

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

What Kind of Leaves Are These?

Dr. Bruce Scruggs, XRF Product Manager, EDAX

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

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

Gold leaf “Gold leaf'” embedded in resin

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

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

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

My New Lab Partner

Matt Nowell, EBSD Product Manager, EDAX

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

Figure 1: Our new SEM coming off the truck.

Figure 1: Our new SEM coming off the truck.

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

Figure 2: ETD image

Figure 2: ETD image

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

Figure 3: ABS Detector image.

Figure 3: ABS Detector image.

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

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

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

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

Figure 5: T2 Detector.

Figure 5: T2 Detector.

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

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

Figure 6: Always Read the Manual!

Figure 6: Always Read the Manual!

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

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

Figure 7: PRIAS™ Top Detector Image.

Figure 7: PRIAS™ Top Detector Image.

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

Figure 8: PRIAS™ Center Detector Image.

Figure 8: PRIAS™ Center Detector Image.

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

Figure 9: PRIAS™ Bottom Detector Image.

Figure 9: PRIAS™ Bottom Detector Image.

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

Figure 10: Defining Custom ROIs in PRIAS™.

Figure 10: Defining Custom ROIs in PRIAS™.

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

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

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

figure-12a

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

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

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

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

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

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

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

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

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

Adding a New Dimension to Analysis

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

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

3D distribution of Nd in steel.

3D distribution of Nd in steel.

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

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

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

Rotary Engines Go “Round and Round”

Dr. Bruce Scruggs, XRF Product Manager EDAX

Growing up outside of Detroit, MI, automobiles were ingrained in the culture, particularly American muscle cars. I was never a car buff but if I said little and nodded knowingly during these car discussions, I could at least survive. Engine displacement? Transmission? Gear ratios? Yep, just nod your head and grunt a little bit. Well, it turns out working at EDAX that I’ve run into a couple of serious car restoration experts. There always seems to be a common theme with these guys: how do I get more power out of this engine?

Recently, one of these restoration experts brought in a small section of the rotor housing of a Mazda engine circa early ‘80s. Turns out, this guy likes to rebuild Mazda engines, tweak the turbocharging and race them. As we all know, Mazda was famous for commercializing the Wankel engine, aka the rotary engine, to power their cars. Rotary engines are famous for their simplicity and the power one can generate from a relatively small engine displacement. These engines are also infamous (i.e. poor fuel consumption and emissions) as well which has led Mazda to end general production in roughly 2012 with the last of the production RX-8s.

Now, one of the questions in rebuilding these engines is how to repair and resurface the oblong rotor housing. In older engines of this type, the surface of the rotor housing can suffer deep gouges. The gouges can be filled and then need to be resurfaced. Initially, we imaged the cross-section of the rotor housing block in an Orbis PC micro-XRF spectrometer to determine what was used to surface coat the rotor housing. If you read up on this engine, (it’s a 12A variant), the block is aluminum with a cast iron liner and a hard chromium plating. The internet buzz claims the liner is installed via a “sheet metal insert process”. And when I google “sheet metal insert process” all I get are links to sheet metal forming and links referring to webpages which have copied the original reference to “sheet metal insert process”.

In the following Orbis micro-XRF maps (Figures 1a and 1b), you can see the aluminum rotor housing block and the cast iron liner. Each row of the map is about 100 µm wide with the iron liner being about 1.5 mm thick. If you look carefully, you can also see the chrome coating on the surface of the iron liner. On the cross-section, which was done with a band saw cut, the chrome coating is about one map pixel across. So, it’s less than 100 µm thick. From web searches, hard chrome plating for high wear applications start at around 25 µm thick and range up to hundreds of microns thick. For very thick coatings, they are ground or polished down after the plating process to achieve more uniform application. So, what is found in the elemental map is consistent with the lower end of web-based information for a hard chrome coating, bearing in mind that the coating measured had well over 150k miles of wear and tear. If we had a rotor housing with less wear and tear, we could use XRF to make a more proper measurement of the chrome plating thickness and provide a better estimate of the original manufacturer’s specification on the hard chrome thickness.

Figure 2: Orbis PC elemental map

Figure 1a: Orbis PC elemental map

Overlay of 4 elements:
Fe: Blue (from the cast iron liner)
Al: Green (from the aluminum rotor housing block)
Cr: Yellow (coating on the cast iron liner)
Red: Zinc (use unknown)

Figure 3: Total counts map: Lighter elements such as Al generate fewer X-ray counts and appear darker than the brighter, heavy Fe containing components.

Figure 1b: Total counts map: Lighter elements such as Al generate fewer X-ray counts and appear darker than the brighter, heavy Fe containing components.

We did have a look at the chrome coating by direct measurement with both XRF, looking for alloying elements such as Ti, Ni, W and Mo, as well as SEM-EDS looking for carbides and nitrides. We found that it’s simply a nominally, pure chrome coating with no significant alloying elements. We did see some oxygen using SEM-EDS, but that would be expected on a surface that has been exposed to high heat and combustion for thousands of operating hours. Again, these findings are consistent with a hard chrome coating.

In some on-line forum discussions, there was even speculation that the chrome coating was micro-porous to hold lubricant. So, we also looked at the chrome surface under high SEM magnification (Figure 2). There are indeed some voids in the coating, but it doesn’t appear that they are there by design, but rather that they are simply voids associated with the metal grain structure of the coating or perhaps from wear. We specifically targeted a shallow scratch in the coating, looking for indications of sub-surface porosity. The trough of the scratch shows a smearing of the chrome metal grains but nothing indicating designed micro-porosity.

Figure 4: SEM image of chrome plated surface of rotor housing liner. The scratch running vertically in the image is about 120 µm thick.

Figure 2: SEM image of chrome plated surface of rotor housing liner. The scratch running vertically in the image is about 120 µm thick.

The XRF maps in Figure 1 also provides some insight into the sheet metal insert process. The cast iron liner appears to be wrapped in ribbons of aluminum alloy and iron. The composition of the iron ribbon (approximately 1 wt% Mn) is about the same as the liner. But, the aluminum alloy ribbon is higher in copper content than the housing block. This can be seen in the elemental map (Figure 1a) where the aluminum ribbon is a little darker green, lower Al signal intensity, than the housing block itself. The map also shows a thread of some zinc bearing component running through (what we speculate are) the wrappings around the liner. My best guess here is that it is some sort of joining compound. Ultimately, the sheet metal insert process involves a bit more than a simple press or shrink fit of a cylinder sleeve in a piston engine block. Nod knowingly and grunt a little.

With Great Data Comes Great Responsibility

Matt Nowell, EBSD Product Manager, EDAX

First, I have to acknowledge that I stole the title above from a tweet by Dr. Ben Britton (@BMatB), but I think it applies perfectly to the topic at hand. This blog post has been inspired by a few recent events around the lab. First, our data server drives suffered from multiple simultaneous hard drive failures. Nothing makes you appreciate your data more than no longer having access to it. Second, my colleague and friend Rene de Kloe wrote the preceding article in this blog, and if you haven’t had the opportunity to read it, I highly recommended it. Having been involved with EBSD sample analysis for over 20 years, I have drawers and drawers full of samples. Some of these are very clearly labeled. Some of these are not labeled, or the label has worn off, or the label has fallen off. One of these we believe is one of Rene’s missing samples, although both of us have spent time trying to find it. Some I can recognize just by looking, others need a sheet of paper with descriptions and details. Some are just sitting on my desk, either waiting for analysis or around for visual props during a talk. Here is a picture of some of these desk samples including a golf club with a sample extracted from the face, a piece of a Gibeon meteorite that has been shaped into a guitar pick, a wafer I fabricated myself in school, a rod of tin I can bend and work harden, and then hand to someone else to try, and a sample of a friction stir weld that I’ve used as a fine grained aluminum standard.

fig-1_modified
Each sample leads to data. With high speed cameras, it’s easier to collect more data in a shorter period of time. With simultaneous EDS collection, it’s more data still. With things like NPAR™, PRIAS™, HR-EBSD, and with OIM Analysis™ v8 reindexing functionality, there is also a driving force to save EBSD patterns for each scan. With 3D EBSD and in-situ heating and deformation experiments, there are multiple scans per sample. Over the years, we have archived data with Zip drives, CDs, DVDs, and portable hard drives. Fortunately, the cost for storage has dramatically decreased in the last 20+ years. I remember buying my first USB storage stick in 2003, with 256 MB of storage. Now I routinely carry around multiple TBs of data full of different examples for whatever questions might pop up.

cost-per-gigabyte-large_modified
How do we organize this plethora of data?
Personally, I sometimes struggle with this problem. My desk and office are often a messy conglomerate of different samples, golf training aids (they help me think), papers to read, brochures to edit, and other work to do. I’m often asked if I have an example of one material or another, so there is a strong driving force to be able to find this quickly. Previously I’ve used a database we wrote internally, which was nice but required all of us to enter accurate data into the database. I also used photo management software and the batch processor in OIM Analysis™ to create a visual database of microstructures, which I could quickly review and recognize examples. Often however, I ended up needing multiple pictures to express all the information I wanted in order to use this collection.

blog-fig-3_modified

To help with this problem, the OIM Data Miner function was implemented into OIM Analysis™. This tool will index the data on any given hard drive, and provide a list of all the OIM scan files present. A screenshot using the Data Miner on one of my drives is shown above. The Data Miner is accessed through this icon on the OIM Analysis™ toolbar. I can see the scan name, where it is located, the date associated with the file, what phases were used, the number of points, the step size, the average confidence index, and the elements associated with any simultaneous EDS collection. From this tool, I can open a file of interest, or I can delete a file I no longer need. I can search by name, by phase, or by element, and I can display duplicate files. I have found this to be extremely useful in finding datasets, and wanted to write a little bit about it in case you may also have some use for this functionality.