Month: October 2019

Things That Change The Way We Use EDS

Dr. Shangshang Mu, Applications Engineer, EDAX

I got into the EDS world about 10 years ago, when I started my PhD study at Boston University. In my first project, I needed to quantify the elemental composition of my experimental samples. My advisor told me that ideally, we should use an electron microprobe and that the nearest one was at MIT, which is literally on the other side of the Charles River. But after we heard of the hourly fee and estimated the number of samples I would need to analyze, we started to plan an alternative. We found out that there was a field emission SEM equipped with an EDS detector in our own Photonics Center, just across the street and most importantly it was free of charge. At that time, I knew neither microprobe nor EDS and decided to give EDS a try first. Even if it did not work, I had nothing to lose except wasting some time. Graduate students have plenty of time to waste. So finally, I didn’t cross the beautiful Charles River but Commonwealth Avenue for the EDS.

As a result, I was introduced to the EDS world by the EDAX Apollo 40 SDD detector and EDAX was the only EDS manufacturer I knew as a beginner. It is such a good brand name standing for Energy Dispersive Analysis of X-rays so in the first couple of years I was under the impression that it was the term of this technique. For quantitative analysis, I used known standards that were close to my experimental samples in composition to standardize and got pretty good results fast and easy. In the subsequent projects, I collected a lot of EDS maps and the Apollo 40 never disappointed me. Since the EDS detector worked well for my PhD projects, I no longer considered other techniques. By the way, although I missed the opportunity to learn how to use the microprobe across the river due to the budget issue, my first full-time job in the states was to manage a much fancier microprobe acquired by a former user of the MIT’s microprobe. He received his PhD in geochemistry from MIT and I got most of my microprobe skills from him.

As an entry level EDAX user in graduate school, I had no way to imagine that I turned myself into an EDAX applications engineer and right now I am celebrating my one-year anniversary. After I joined EDAX, I got to know that the Apollo was the first generation SDD detector series of EDAX and our current EDS detectors have much better all-around performance. At the beginning of my second year with EDAX, I looked back into the EDS data I collected in graduate school and noticed that they were collected at a much slower amp time (the time the detector processes one X-ray count) compared to current ones and the EDS maps looked kind of noisy in comparison to my current perspective. Prompted by these findings, I wanted to initiate the discussion with how advancements in detector technology shaped the way we use EDS.

Apollo 40 SDD at Boston University
EDAX Octane Elite SDD in Draper, UT
Figure 1. The EDAX Apollo 40 SDD attached to the SEM I used at Boston University (left) and the EDAX Octane Elite SDD I am currently using (right).

 

I believe all EDS users have heard of this rule of thumb: keep the dead time between 20% and 40%, or something like this. At least I was taught to keep the percentage within this range and as far as I know a lot of EDS users are following this rule. This is a traditional perspective that came out in the past when detector amp times were much slower. The dead time is all about throughput and affected by the amp time. Historically, if the dead time is below 20%, it means the detector either doesn’t receive enough X-ray counts per second to ensure high data quality or the amp time is too fast to maintain an optimal detector resolution. On the other hand, if the dead time is over 40%, the Input Counts Per Second (ICPS) is too high to be handled optimally by the current amp time and may lead to excessive summed peaks. We can get the dead time back in the range by either decreasing the beam current to lower the count rate or choosing a faster amp time.

In the past, we usually did not consider the second option since it would sacrifice the detector resolution a lot for throughput. However, the current generation of EDAX EDS detectors is equipped with CMOS based pre-amplifiers that allow much faster amp times ranging from 0.12 μs to 7.68 μs, while keeping very good resolution and high throughput. For example, if I have the EDAX Octane Elite detector in my lab, I can run eight times faster by moving the amp time from the slowest 7.68 μs to the intermediate 0.96 μs, the resolution is only decreased by roughly 4 eV (from 124 eV to 128 eV). This intermediate amp time can handle at least 80% of the job, however under this condition it is hard to make the dead time go over 20% unless the detector is receiving over 100K ICPS. Even if I use the fastest amp time at 0.12 μs, the resolution is still below 150 eV, which is not a significant decrease in resolution if you know that the detector resolution for those equipped with traditional JFET pre-amplifiers is about 250 eV at this fastest amp time. Since resolution is no longer a limiting factor, feel free to open your aperture to increase the count rate and choose a faster amp time to lower the dead time. In return, you will get a higher throughput, which means more statistics and higher quality of data. Although we can go below 20% dead time to have a throughput improvement, it still makes sense to apply the 40% upper bound since the detector will not convert X-rays efficiently once the dead time is beyond this maximum percentage.

When do we want to hit hundreds of thousands of ICPS and take advantage of the fast amp time and high throughput brought by current EDS detectors? While I was using the Apollo 40 at Boston University to collect EDS maps, I stuck to 12.8 μs amp time believing the higher the detector resolution, the better the map quality. Now, I have realized that the major limitation to the quality of EDS maps is not the detector resolution but the limited statistics at the pixel level. Not to mention for our current detectors, the degradation in resolution when running fast is very little. The quality of peak deconvolution is primarily determined by the level of statistics, even when dealing with tricky peak overlaps.

I did a quick study on a piece of floor tile in my lab that contains both calcium phosphate and zirconium silicate, so P and Zr are in distinct phases. P K and Zr L peaks are heavily overlapped with only 29 eV of energy difference. I used the Octane Elite detector to do a quick five-minute net intensity map on this floor tile at 26 nA beam current and 0.96 μs amp time. This combination gave me 160K ICPS and 28% dead time. For the purpose of comparison, I used the slowest amp time at 7.68 μs to yield the highest detector resolution and recollected the maps for 6.5 minutes. To keep the dead time at 28% I had to lower the beam current to 3.2 nA to constrain the ICPS at 20K. Obviously, in the first run Zr and P were separated out nicely in the sharp images (Figure 2 left), as it built up eight times more statistics than the second run in roughly the same amount of time. In the second run at the highest detector resolution, the separation was not quite as good (Figure 2 right). As we can see, the images are kind of pixelated and the coral pixels are mixed within the green in the overlay, so the slightly better detector resolution did not help at all. If I were able to know this trick as a rookie, I would be able to get higher quality maps in the same amount of time or get the same quality of maps faster.

Net intensity maps of P/Zr overlay and P collected at 0.96 μs amp time (left) and 7.68 μs amp time (right) for roughly 5 minutes.
Figure 2. Net intensity maps of P/Zr overlay and P collected at 0.96 μs amp time (left) and 7.68 μs amp time (right) for roughly 5 minutes.

From my point of view, the advancements in detector technology, the experience we gain, or a combination of the two change the way we use EDS.

Texture on the Greens

Matt Nowell, EBSD Product Manager, EDAX

For better or worse, I am a golfer. The story of how I became a golfer helps explain my love for EBSD, and evolution as a material scientist. While I was at the University of Utah trying to decide on a major, I enjoyed fishing. Here in Utah, we have lovely access to many rivers, streams, and lakes with great fishing potential. When I started investigating Materials Science and Engineering as a degree, I thought it would be interesting to learn about the graphite used in fishing rods, and how the different processes used improved the performance of the rods. With this interest, I enrolled in the Materials Science department. Fortunately (for the fish I like to think), two things happened that changed my recreational and professional focus.

Tate Nowell catching a Utah trout

My son catching a nice Utah trout.

First, I enrolled in Organic Chemistry. Memorizing different molecules was not something that I excelled at. This dampened my enthusiasm for all things polymer-based, like composite fishing rods. Second, I was hired to work in the Electron Microscopy lab for the Materials Science department. This gave me lots of direct exposure to SEM, TEM, EDS, and even XRD instruments. It also helped to build a strong appreciation for sample preparation, but that’s the topic for another blog. All these things pushed me in the direction of materials characterization, and when I graduated, it led me to TSL, EDAX, and EBSD.

How does all this relate to golf? Soon after I started working, I played a round of golf with some friends. I had played a few casual rounds over the years, but nothing serious. Looking back, I consider myself lucky because the parking lot for the EM characterization facility was also the parking lot for the University golf course, and if I had been bitten by the golf bug at that point, things might have occurred differently. After this round, which I greatly enjoyed, I went to the local golf store thinking about different golf clubs. I found a set where the shafts of the club were manufactured by the same company that made my favorite fishing rod. This seemed like a sign to me, so I bought the clubs and jumped right into becoming a golfer.

It was great timing. EBSD scans were slow. To collect a 20,000-point scan, which was our typical target at the time, took 5-6 hours. It was easy to fit in 9 holes during some of these scans. Today, with our new Velocity™ cameras, it takes about 5 seconds to collect this data. Sometimes there is barely enough time to hit a practice putt down the hall in the lab. Many of us in the office enjoyed playing together. We even commissioned an annual tournament called the Burrito Open, where we combined golf with Mexican food. If you examine the picture of the 2008 tournament closely, you may notice it is an International event, with participants from Europe (Rene de Kloe) and Japan (Suzuki-san).

2008 Burrito Open

2008 Burrito Open

This tournament also allowed us to indulge and combine golf with work a little bit. The first trophy was constructed from an old port cover we no longer needed. The second trophy was of course a crystal trophy. As you might imagine, there was some discussion over what crystal structure would be most appropriate.

Burrito Open Trophies

Burrito Open Trophies

There is plenty of time to think during a round of golf, and one of the things I’ve thought about is how the club heads are produced. When thinking about this, we are considering either woods (or more accurately metal woods) or irons. Irons are typically either cast or forged. Forged clubs are generally positioned for the better players, but what caught my eye is that some manufacturers started marketing the idea of microstructure and the role of grains in the material. One brand even pushes what they call Grain Flow Forged.

Grain Flow Forged Iron

Grain flow forged iron

Of course, just like the fishing rods, the processing of the club affects the properties. The key link is that the processing changes the microstructure, and the microstructure defines the properties. With that in mind, it’s a fun experiment to compare the microstructure of cast clubs versus forged clubs. Stuart Wright and I first did this experiment about 20 years ago for a conference on Materials in Sports, but given the increases in capability, it would be fun to reevaluate.

I sectioned and prepared EBSD samples from both forged and cast irons. These were both made with an unknown steel alloy and prepared with my standard metallographic preparation approach. Enough coarse-grained polishing was used to remove plating from the club surface. The resulting Image Quality + Orientation Maps (IPF relative to the surface normal direction) are shown below. These were collected from the face of the golf club. I prepared cross-sections for further analysis. I was a little surprised by both microstructures. The cast iron had a dual-component microstructure, with both generally equiaxed grains and needle shaped grain constituents. It looks a lot like a dual phase Ferrite-Martensite microstructure, but with martensite being tricky to directly identify with EBSD relative to ferrite, I indexed both regions with a BCC Ferrite material file. The local misorientations were higher in the needle shaped regions, and there were also some austenitic grains present in these regions. The forged iron microstructure has a bimodal grain size distribution, with larger equiaxed grains decorated and intermixed with smaller equiaxed grains. A second scan was collected at higher magnification and with a finer step size to better resolve these fine grains.

IQ and IPF ND Orientation Map from cast golf club

IQ and IPF ND Orientation Map from cast golf club

IQ and IPF ND Orientation Map from forged golf club

IQ and IPF ND Orientation Map from forged golf club

Higher magnification IQ and IPF ND Orientation Map from forged golf club

Higher magnification IQ and IPF ND Orientation Map from forged golf club

As expected, the microstructures of the forged and cast clubs are different. As a Materials Scientist and EBSD guy, I tend to think forging is a more interesting materials processing option. In this case though, both casting and forging have produced interesting microstructures that could use further investigation. I once bought a set of forged clubs, and it was with this set I made my only hole-in-one. I’m pretty sure there is a direct correlation, and when I buy my next set, I’ll continue my experimentations.