EBSD

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!

The Hough Transform – An Amazing Tool.

Shawn Wallace, Applications Engineer, EDAX

Part of my job is understanding and pushing the limits of each part of our systems. One of the most fundamental parts of the EBSD system is the Hough Transform. The Hough Transform role is finding the lines on an EBSD pattern. This is the first step in indexing a pattern (Fig. 1). If this step is not consistent, the quality of any indexing and any derivative data is questionable. A normal user does not really need to understand all the intricacies of every part of the system, but it still is worthwhile to understand how your data and data quality can be affected.

Figure 1: On the left are the overlaid lines found via the Hough Transform. On the right is the Indexed solution overlaid based on the Hough. The quality of the indexed solution is based on the quality of the Hough.

Figure 1: On the left are the overlaid lines found via the Hough Transform. On the right is the Indexed solution overlaid based on the Hough. The quality of the indexed solution is based on the quality of the Hough.

With that in mind, I ran an experiment on a steel sample to see how far the Hough could be pushed and still give consistent indexing. For this experiment, I used our Hikari Super at a series of different binnings between its native resolution of 640X480 Pixels at 1×1 binning down to 35×26 pixels at 18×18 binning. All pixel resolutions are noted in Table 1. I kept my Hough Settings and beam settings consistent. My only other variable was exposure to get the camera to be equally saturated at around 0.85 saturation.

I expected the lower binning Patterns to be consistent and they were (Fig. 2). All three Euler Angles between the 1×1, 2×2, 4×4, and 8×8, were within 0.4 degrees of each other. Pushing the camera and the Hough even further really surprised me though.

Figure 2: Indexed Pattern for the lower binning showed a remarkable consistency in indexing.

Figure 2: Indexed Pattern for the lower binning showed a remarkable consistency in indexing.

Figure 3: The indexing results still held their consistency even for highest binning settings used.

Figure 3: The indexing results still held their consistency even for highest binning settings used.

I expected some drop off with the consistency of the orientation when I dropped my binning to 10×10, 16×16, and even 18×18 and it did not fully materialize (Fig. 3). The range did broaden in the Euler Angles, specifically ᶲ₂’s range increased to 3 degrees, but that is change of <1% given the entire range for ᶲ₂ is 360 degrees. Table 1 shows the data is the raw form. Overall, the data is great, from low to high binning with minimal loss in in our indexing metrics (CI and Fit) and consistency in Euler Angles except for the 18×18 binning. That is where we have found our limit, specifically when it comes to indexing metrics. We see a sharp drop off in the CI. The pixilation of the pattern has gotten to a point where it is difficult to find a unique solution. This drop off is why we tell our customer that 16×16 is the limit of binning they should use for reliable, high quality data.

Table 1. Indexing Metrics and Euler Angles for all data points.

Table 1. Indexing Metrics and Euler Angles for all data points.

With all that said, most EBSD work is not on a single orientation, but a map. Does this hold true on a map? It does. In Figure 4 and Figure 5, we can see the mapping results for 2×2 binning and 10×10 binning. Both indexed at 99.9% with their average CI’s being 0.89 and 0.84 respectively, with very little change in orientations. This level of data quality across binnings is why EDAX uses the Hough. It is an amazing little tool.

Figure 4. This map was taken at 2x2 binning. Internal deformation of the grains is visible, with inclusions between relatively undeformed.

Figure 4. This map was taken at 2×2 binning. Internal deformation of the grains is visible, with inclusions between relatively undeformed.

Figure 5. This map was taken at 10x10 binning in approximately the same area as Figure 4. Again, internal deformation is showed in the larger grain, while the inclusions are undeformed.

Figure 5. This map was taken at 10×10 binning in approximately the same area as Figure 4. Again, internal deformation is showed in the larger grain, while the inclusions are undeformed.

Molecular Machines are the Future…

René Jansen, Regional Manager, Europe

The ground in the north of Holland was recently shaking and not because of an earthquake, but because Professor Ben Feringa from the University of Groningen has won the 2016 Nobel Prize in Chemistry for his work on the development of molecular machines.
Feringa discovered the molecular motor — a light-driven rotary molecular motor – which is widely recognized as a spectacular scientific breakthrough.

Electrically driven directional motion of a four-wheeled molecule on a metal surface

Electrically driven directional motion of a four-wheeled molecule on a metal surface

‘Building a moving molecule is not that difficult in itself, but being able to steer it, have control over it, is a different matter.’, he said. Years ago he already presented the first molecular motor, consisting of a molecule, part of which performed a full rotation under the influence of light and heat. He has designed many different engines since, including a molecular ‘4-wheel drive’ car. By fixating the engine molecules to a surface, he developed a nano ‘mill park’ in which the mills rotate when exposed to light. And last year he described the world’s first symmetrical molecular engine. Feringa also succeeded in putting these molecular engines to work, having them turn a glass cylinder 10,000 times their size. Amazing.

Feringa is internationally recognized as a pioneer in the field of molecular engines. One of the potential applications of his engines is the delivery of medication inside the human body.
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I recently heard an interview with him, in which he promoted the idea that universities should be playgrounds, where scientists must be able to do whatever they want to create real breakthroughs. Today, the ability of universities to create these playgrounds is limited due to a constant reduction of budgets over recent years. It would be interesting to know how the University of Groningen has managed to do this.

Another, less famous, department at the University of Groningen is working on the formation/deformation of materials which are exposed to high temperature (> 1000 degrees Celsius). Measuring EBSD patterns while temperature increases, shows that new crystals are formed at a certain temperature. Now my hopes are that this “playground” too will end up in a few years from now with a Nobel prize for a breakthrough in Materials Science.

My Turn

Dr. Stuart Wright, Senior Scientist, EDAX

One of the first scientific conferences I had the good fortune of attending was the Eighth International Conference on Textures of Materials (ICOTOM 8) held in 1987 in Santa Fe, New Mexico. I was an undergraduate student at the time and had recently joined Professor Brent Adams’ research group at Brigham Young University (BYU) in Provo, Utah. It was quite an introduction to texture analysis. Most of the talks went right over my head but the conference would affect the direction my educational and professional life would take.

Logos of the ICOTOMs I've attended

Logos of the ICOTOMs I’ve attended

Professor Adams’ research at the time was focused on orientation correlation functions. While his formulation of the equations used to describe these correlations was coming along nicely, the experimental side was quite challenging. One of my tasks for the research group was to explore using etch pits to measure orientations on a grain-by-grain basis. It was a daunting proposition for an inexperienced student. At the ICOTOM in Santa Fe, Brent happened to catch a talk by a Professor from the University of Bristol named David Dingley. David introduced the ICOTOM community to Electron Backscatter Diffraction (EBSD) in the SEM. Brent immediately saw this as a potential experimental solution to his vision for a statistical description of the spatial arrangement of grain orientations in polycrystalline microstructures.

At ICOTOMs through the years

At ICOTOMs through the years

After returning to BYU, Brent quickly went about preparing to get David to BYU to install the first EBSD system in North America. Instead of etch pits, my Master’s degree became comparing textures measured by EBSD and those measured with traditional X-Ray Pole Figures. I had the opportunity to make some of the first EBSD measurements with David’s system. From those early beginnings, Brent’s group moved to Yale University where we successfully built an automated EBSD system laying the groundwork for the commercial EBSD systems we use today.

I’ve had the good fortune to attend every ICOTOM since that one in Santa Fe over 30 years ago now. The ICOTOM community has helped germinate and incubate EBSD and continues to be a strong supporter of the technique. This is evident in the immediate rise in the number of texture studies undertaken using EBSD immediately after EBSD was introduced to the ICOTOM community.

The growth in EBSD in terms of the percentage of EBSD related papers at the ICOTOMs

The growth in EBSD in terms of the percentage of EBSD related papers at the ICOTOMs

Things have a way of coming full circle and now I am part of a group of three (with David Fullwood of BYU and my colleague Matt Nowell of EDAX) whose turn it is to host the next ICOTOM in St George Utah in November 2017. The ICOTOM meetings are held every three years and generally rotate between Europe, the Americas and Asia. At ICOTOM 18 we will be celebrating 25 years since our first papers were published using OIM.
icotom-2017
It is a humbling opportunity to pay back the texture community, in just a small measure, for the impact my friends and colleagues within this community have had both on EBSD and on me personally. It is exciting to consider what new technologies and scientific advances will be germinated by the interaction of scientists and engineers in the ICOTOM environment. All EBSD users would benefit from attending ICOTOM and I invite you all to join us next year in Utah’s southwest red rock country for ICOTOM 18! (http://event.registerat.com/site/icotom2017/)

Some of the spectacular scenery in southwest Utah (Zion National Park)

Some of the spectacular scenery in southwest Utah (Zion National Park)

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.

Help!

Dr. René de Kloe, Applications Specialist EDS, EBSD, EDAX

The job of an applications engineer is to help people. Help sales people to explain to customers what a system can do. Help customers to get the most out of their system and help them to understand their materials better. Help the marketing group with nice examples. And help the development team to devise applications that have not been tried before.

One thing you need in order to be able to help is knowing the EDAX analysis systems inside-out. But the other thing you need is samples. Lots of samples. Every function or analysis tool in the software, regardless if it is for EDS, EBSD, WDS, or XRF is best shown with a specific material or combination of elements or phases. Some of these, like chemical standards with known composition, you have to make or perhaps buy. Others you have to collect yourselves, but from where? A great source for new materials are our customers. People often send me materials to evaluate our systems, or for help on how to best analyse their samples. When I then get permission to keep a bit of the material it goes directly into my collection, together with valuable information on the current analysis requirements in different scientific disciplines.

Eight phase FeSi alloy Brass with NiMnSi particles 

This goes a long way in getting good example materials, but I always keep my eyes open for new interesting things. When I see a metal strip in an anti-theft label in clothing I keep it (after buying the item of course), when a droplet of lead-tin solder falls on the floor, I stick it in the microscope to see if it looks good. I also scrutinize things that get thrown away, ranging from the lid of a vegetable jar to a damaged bellows of an EBSD system. That has given me beautiful cast aluminium samples for EDS mapping, multiphase brass alloys for ChI-Scan EDS-EBSD analysis, and recently an unexpected copper-plated zinc-aluminium-silicon alloy for EBSD phase identification from a broken belt buckle.

Grain structure of a staple Grain structure of a key ring 

Luckily I don’t always have to go dumpster diving to get my example materials. One of my favorite sample mounts contains different types of heavily deformed ferrite, duplex stainless steel, and also martensitic structures. That sounds perhaps complicated, but on the outside the same sample just looks like staples, a paperclip, a key ring, and a screw.

The screw, for example, I polished after doing some DIY work at home and because a certain type of screw kept breaking off when I tightened it, I wanted to take a close look why that happened. It turned out that there were lots of small cracks along the thread, which then also lined up with trails of carbides further inside the screw. That turned out to be a really bad combination and when you tighten the screw, the cracks propagate, connect with the carbide trails and the screw head snaps off. The replacement screws that I used instead had a much finer structure without any cracks and that is what is still holding things together in the house. This shows how microstructures literally shape our daily life. And it also provides a beautiful example to help illustrate the importance of microstructural characterization to new EBSD users.

Weak screw Strong screw

The huge variation in materials and microstructures makes the collection of demonstration samples the most important tool for an application scientist and from this place I hereby want to thank all people who have given me a piece of some material during my years at EDAX to use to help others.

By the way, I would appreciate it very much if the person who briefly “borrowed” my marble sample last year gives it back soon …

Metals, Minerals and Gunshot Residue

Dr. Jens Rafaelsen, Applications Engineer EDAX

During a recent visit to a customer facility I was asked what kind of samples, and applications I typically see. It would seem that this would be a pretty easy question to answer but I struggled to narrow it down to anything “typical”. Over the past three weeks I have spent a couple of days each week at customer facilities and I think a brief description of each of them will explain why I had a hard time answering the question.

The first facility I went to was a university in the process of qualifying an integrated EDS/EBSD system on a combined focused ion beam (FIB) and scanning electron microscope (SEM). A system like this allows one to remove material layer by layer and reconstruct a full 3D model of the sample. The dataset in Figure 1 illustrates why this information can be crucial when calculating material properties based on the grain structure from an EBSD scan. If one looks at the image on the left in the figure, it seems obvious that there are a few large grains in the sample with the area between them filled by smaller grains. However, the reconstructed grain on the right shows that several of these smaller “grains” seen in the single slice are actually interconnected and form a very large grain that stretches outside the probed volume.

Figure 1: Single slice EBSD map (left) and single reconstructed grain from 3D slice set (right).

Figure 1: Single slice EBSD map (left) and single reconstructed grain from 3D slice set (right).

While we spent a good amount of time documenting exactly what kind of speed, signal-to-noise, resolution and sensitivity we could get out of the system, one of the customer’s goals was to measure strain to use as a basis for material modelling. We also discussed a potential collaboration since our EBSD applications engineer, Shawn Wallace, has access to meteorite samples through his previous position at the American Museum of Natural History in New York and a 3D measurement of the grains in a meteorite could make a very compelling study.

Next up was a government agency where the user’s primary interest was in mineral samples but also slag and biological materials retained in mineral matrices. Besides the SEM with EDS they had a microprobe in the next room and they would often investigate the samples in the SEM first before going to the microprobe for detailed analysis (when and if this is required is a different discussion, I would recommend Dale Newbury and Nicholas Ritchie’s article for more details: J Mater Sci (2015) 50:493–518 DOI 10.1007/s10853-014-8685-2).

A typical workflow would be to map out an area of the sample and identify the different phases present to calculate the area fraction and total composition. Since the users of the facility work with minerals all the time, they could easily identify the different parts of the sample by looking at the spectra and quantification numbers, but I have a physics background and will readily admit that I would be hard pressed to tell the difference between bustamite and olivine without the help of Google or a reference book. However, this specific system had the spectrum matching option, which eliminates a lot of the digging in books and finding the right composition. The workflow is illustrated in Figure 2, where one starts by collecting a SEM image of the area of interest and, when the EDS map is subsequently collected, the software will automatically identify areas with similar composition and assign colors accordingly. The next step would then be to extract the spectrum from one area and match it up against a database of spectra. As we can see in the spectrum of Figure 2, the red phase of the EDS map corresponds to a obsidian matrix with slightly elevated Na, Al, and Ca contributions relative to the standard.
Figure 2a

Figure 2: Backscatter electron image (top left) and corresponding phase map (top right) showing different compositions in the sample. The bottom spectrum corresponds to the red phase and has an obsidian spectrum overlaid.

Figure 2: Backscatter electron image (top left) and corresponding phase map (top right) showing different compositions in the sample. The bottom spectrum corresponds to the red phase and has an obsidian spectrum overlaid.

The last facility I visited was a forensic lab, where they had purchased an EDS system primarily for gunshot residue (GSR) detection. The samples are usually standard 12.7 mm round aluminum stubs with carbon tabs. The sticky carbon tabs are used to collect a sample from the hands of a suspect, carbon coated and then loaded into the SEM. The challenge is now to locate particles that are consistent with gunshot residue amongst all the other stuff there might be on the sample. The criteria are that the particle has to contain antimony, barium and lead, at least for traditional gunpowder. Lead free gunpowder is available but it is significantly more expensive and when asking how often it is seen in the labs, I was told that apparently the criminal element is price conscious and not particularly environmentally friendly!

The big challenge with GSR is that the software has to search through the entire stub, separate carbon tape from particles down to less than 1 micron, and then investigate whether a particle is consistent with GSR based on the composition. The workflow is illustrated in Figure 3 and is done by collecting high resolution images, looking for particles based on greyscale value in the image, collecting a spectrum from each particle and then classifying the particle based on composition. Once the data is collected, the user can go in and review the total number of particles and specifically look for GSR particles, relocate them on the sample, and collect high resolution images and spectra for documentation in a potential trial.

Figure 3: Overview showing the fields collected from the full sample stub (top left), zoomed image corresponding to the red square in the overview image (top right) and gunshot residue particle from the red square in the zoomed image (bottom).

Figure 3: Overview showing the fields collected from the full sample stub (top left), zoomed image corresponding to the red square in the overview image (top right) and gunshot residue particle from the red square in the zoomed image (bottom).

Three weeks, three very different applications and a very long answer to the question of what kind of samples and applications I typically see. Each of these three applications is typical in its own way although they have little in common. This brings us to the bottom line: most of the samples and applications we come by might be based in the same technique but often the specifics are unique and I guess the uniqueness is really what is typical.