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.