Research

Turning quantification of lithium from days to minutes of work

Dr. Shangshang Mu, Applications Engineer, Gatan/EDAX

Cipher®, the quantitative analysis of lithium system, is a shining example of the synergies brought about by the merger between Gatan and EDAX. As an application specialist involved since the beginning of this project, witnessing the evolution of the data acquisition and analysis workflow is nothing short of astounding. I vividly recall those initial moments when we tested this concept and generated our first Li measurements from an actual sample.

I conducted energy dispersive x-ray spectroscopy (EDS) data acquisition and analysis in the EDAX APEX™ software during those early stages. At the same time, my colleague focused on the quantitative backscattered electron (qBSE) work within the DigitalMicrograph® software. To analyze the lithium content in a sample from just a few locations was a painstaking process requiring the laborious process of correlating information from disparate software programs manually, checking again and again that the same area of the sample was being analyzed, and then calculating by hand the lithium content of an analysis location using a variety of different mathematical models to determine the best one.

With the release of DigitalMicrograph 3.6.0, the entire data acquisition and analysis workflow unfolds seamlessly, marking a significant advancement in efficiency and user-friendliness, not to mention making my job so much easier! A guided workflow allows a user to conduct the whole experiment using a single software package. Using the Technique Manager, data acquisition and analysis happen step-by-step as you progress from the top palette to the bottom (Figure 1).

Li quantification-related palettes within the DigitalMicrograph Technique Manager panel.

Figure 1. Li quantification-related palettes within the DigitalMicrograph Technique Manager panel.

Several steps used to be challenging experimentally, which the software now manages for you, including:

  • Ensuring that the backscattered electron signal was calibrated by atomic number (Z) and, importantly, that there were no changes to the calibration when moving between samples
  • That data that was captured sequentially could be aligned and transformed before the lithium content being calculated
  • Use of the latest models for qBSE and EDS analysis methods

For the first challenge, appropriate Z-standards are required, and the detector settings and collection geometry must remain constant between qBSE measurements. The qBSE Calibration palette (Figure 2) provides intuitive guidance through this essential process, and when using the Z-standards provided with the system, what used to take an hour or more to complete can now be done in minutes. The buttons of the qBSE calibration palette guide you through the detector setup and measurement of the Z reference samples, populating the calibration table as you go. A calibration curve can be plotted for your reference once a minimum of four reference values are acquired. Vitally, the software continuously verifies that you are at the correct working distance for qBSE. If a measurement is attempted using incorrect conditions, qBSE data cannot be generated. Furthermore, the QuickSet button becomes active, allowing the user to launch a wizard that returns the system to the appropriate conditions for qBSE analysis. This has proven invaluable for many of the customer specimens I have analyzed, as they come in all shapes and sizes!

Figure 2. qBSE Calibration palette and an example of the calibration curve used for converting BSE signals measure to atomic number.

For samples analyzed in the SEM, DigitalMicrograph 3.6 now uses the same standardless EDAX eZAF method for analysis as APEX EDS Advanced software, enabling quantified EDS measurements to be performed reliably in the same software program as used for qBSE data collection. However, to ensure that the analyzed volume is consistent between the two methods, we typically collect data for the two signals at different accelerating voltages. Previously (e.g., [1]), the complexity of registering and aligning the qBSE and EDS data was too challenging to even attempt to map the lithium distribution, with researchers instead choosing to analyze a few isolated points only.

The Cipher Analysis palette (Figure 3) simplifies the process of correlating EDS and qBSE datasets like never before, enabling lithium content to be mapped over a 2D area or 1D line scan in addition to point analyses. By simply selecting the BSE and EDS workspaces from the dropdowns and clicking on the Align button, qBSE and EDS data captured under different conditions will be automatically registered and aligned using the corresponding secondary electron images; this alignment procedure even works if the qBSE and EDS data is captured at different magnifications or pixel density.

Figure 3. Cipher Analysis palette.

Subsequently, pressing Map Low Z will generate Li maps effortlessly using the latest algorithms in EDS and qBSE analysis (Figure 4), adjusting the original elemental maps to include the Li content.

Figure 4. Lithium map (in atomic percent) of a nickel manganese cobalt oxide (NMC) sample commonly used as a cathode material in the construction of lithium-ion batteries.

Looking ahead, the streamlined workflow in DigitalMicrograph and the continued evolution of Cipher promises to revolutionize lithium analysis, empowering researchers with unprecedented insights into battery technology, energy storage, and many other fields. I’m excited to be able to be involved with the development and release of a product that turns what was once impossible into a straightforward experiment.

EDAX and Gatan Bring You Lithium

Dave Durham, Sales Manager – U.S. Western, EDAX

It has been interesting to recently witness EDAX and Gatan working together to combine the technologies in our portfolios. Although technically, Gatan was acquired by AMETEK back in late 2019, it seems like 2021 has been a year where the integration of our two companies has really begun to hit its stride.

For example, I’ve seen how Gatan’s ion polishing instruments can dramatically improve indexing success for EDAX’s Electron Backscatter Diffraction (EBSD) users compared to the conventional methods for sample preparation. And I’ve seen EDAX’s Elite T Energy Dispersive Spectroscopy (EDS) System undergo a tremendous workflow improvement and ease-of-use overhaul with the implementation of Gatan’s Microscopy Suite user interface. It has been great stuff!

However, the most recent integration between our two companies is truly groundbreaking, and I’m thrilled to see what it will do to enhance the research being done in its field.

Hopefully, you’ve already seen the news mentioned on our website. For the first time, we’ve been able to perform quantitative mapping of lithium in the Scanning Electron Microscope (SEM) by combining the power of EDAX and Gatan detectors and software! These breakthrough results will enable a new level of lithium research that was previously never possible with the SEM.

Figure 1. EDAX and Gatan bring you lithium.

So who cares about lithium? Everyone should. Lithium compounds and alloys are very important materials with significant commercial value. The compounds are being implemented into lightweight structural alloys in the aerospace and automotive industries. They’re also utilized in lithium-ion batteries for small electronic devices and vehicles. Many governments worldwide have proposed plans to reduce dependence on legacy energy sources, which makes further development of lithium-based technologies critical to the adoption of these plans. This means significant investments are currently being made in R&D, failure analysis, and quality control of these materials.

Figure 2. (left) Lithium-ion battery cross-section prepared by Ilion II broad beam argon milling system. (right) EBSD IQ + orientation map revealing the microstructure of the heat-affected zone in a lightweight structural alloy.

So what are the issues with lithium? While electron microscopy and EDS are already essential characterization tools in this industry, there is a distinct inability to map lithium distribution in the SEM. This has presented a significant obstacle, holding back research on these tools. EDS is typically a valuable technique for material characterization, with high sensitivity and spatial resolution to allow for quantitative analysis on a wide range of sample types. But it is not possible to identify lithium in commercially important materials by EDS because:

  1. There is no guarantee that lithium X-rays will be produced from the sample. The X-ray energy and the number of photons produced from the specimen depend on the lithium bonding state. So, even if you have lithium in your sample, it does not mean that lithium X-rays will be generated.
  2. Even if a sample does generate lithium X-rays, they are easily absorbed back into the sample itself, contamination or oxidation, or by the EDS detector window before they can even reach the EDS detector.

Indeed, specialized windowless EDS detectors can detect lithium, but these have drawbacks that impede their practicality and largescale adoption. Even on samples that have a high lithium fluorescence, these special detectors have a limit of detection of about 20 wt %. This is equivalent to about half of the atoms in the sample being lithium, which restricts analysis to only metallic lithium or simple lithium compounds that may not be relevant to advanced lithium research or applications.

And having a specialized windowless EDS system potentially introduces a slew of operational issues/limitations with the detector that aren’t present with a “standard” windowed EDS system. It also restricts the detector’s utility on non- lithium -research-based applications in the lab.

So what have EDAX and Gatan done? We have solved these issues by using a patent-pending technique called the Composition by Difference Method. In this method, we quantify the backscattered electron signal to determine the mean atomic mass for all elements in a particular area of a sample. And from the same region, we collect the EDS signal to quantify the non-lithium elements. From that information, we have two data points that tell us the actual mean atomic mass from the region and a calculated value based on the EDS results — when they don’t agree with one another, it tells us we are missing something in the EDS data. That something we’re missing is lithium.

Figure 3. Data from the OnPoint and the Octane Elite Super are combined and analyzed to quantify lithium.

By using this method, and specifically by combining the EDAX Octane Elite Super EDS Detector and the Gatan OnPoint Backscattered Electron Detector to collect these two signals, we can now generate lithium maps quantitatively with single-digit mass percentages of lithium with sub-micron spatial resolution. This accuracy has been verified to ~1 wt. % lithium by an external accredited laboratory using Glow-discharge Optical Emission Spectroscopy (GDOES).

Figure 4. Secondary electron image and elemental metal fraction maps (by wt. %) of the same region of the MgLiAl alloy; white pixels are regions excluded from the analysis due to the influence of topography (identified by arrows in the secondary electron image) shown here for clarity.

This is a cutting-edge capability in the SEM, and it is a huge opportunity for anyone wanting to discover where lithium exists in their specimens. Just to reiterate, this method does not use a specially designed EDS system for lithium detection! It uses EDAX’s standard (windowed) Octane Elite Super and Gatan’s OnPoint BSE detector, along with EDAX and Gatan software. Simply amazing!

Now that EDAX and Gatan have introduced the ability to provide quantitative lithium analysis, that is:

  • A substantial improvement in limits of lithium detection
  • Insensitive to the lithium bonding state
  • More tolerant to contamination and oxidation
  • Not limited to metallic materials or simple lithium compounds
  • Free from windowless detector-related limitations on the SEM

It seems that we have helped open an avenue for our customers to expand their lithium research beyond anything previously possible. We are truly beginning a very exciting new stage in lithium analysis, and I can’t wait to see how this new capability is used and what comes next!

You can find more information on this new development in our experiment brief.

Hats Off/On to Dictionary Indexing

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

Recently I gave a webinar on dynamic pattern simulation. The use of a dynamic diffraction model [1, 2] allows EBSD patterns to be simulated quite well. One topic I introduced in that presentation was that of dictionary indexing [3]. You may have seen presentations on this indexing approach at some of the microscopy and/or materials science conferences. In this approach, patterns are simulated for a set of orientations covering all of orientation space. Then, an experimental pattern is tested against all of the simulated patterns to find the one that provides the best match with the experimental pattern. This approach does particularly well for noisy patterns.

I’ve been working on implementing some of these ideas into OIM Analysis™ to make dictionary indexing more streamlined for datasets collected using EDAX data collection software – i.e. OIM DC or TEAM™. It has been a learning experience and there is still more to learn.

As I dug into dictionary indexing, I recalled our first efforts to automate EBSD indexing. Our first attempt was a template matching approach [4]. The first step in this approach was to use a “Mexican Hat” filter. This was done to emphasize the zone axes in the patterns. This processed pattern was then compared against a dictionary of “simulated” patterns. The simulated patterns were simple – a white pixel (or set of pixels) for the major zone axes in the pattern and everything else was colored black. In this procedure the orientation sampling for the dictionary was done in Euler space.
It seemed natural to go this route at the time, because we were using David Dingley’s manual on-line indexing software which focused on the zone axes. In David’s software, an operator clicked on a zone axis and identified the <uvw> associated with the zone axis. Two zone axes needed to be identified and then the user had to choose between a set of possible solutions. (Note – it was a long time ago and I think I remember the process correctly. The EBSD system was installed on an SEM located in the botany department at BYU. Our time slot for using the instrument was between 2:00-4:00am so my memory is understandably fuzzy!)

One interesting thing of note in those early dictionary indexing experiments was that the maximum step size in the sampling grid of Euler space that would result in successful indexing was found to be 2.5°, quite similar to the maximum target misorientation for modern dictionary indexing. Of course, this crude sampling approach may have led to the lack of robustness in this early attempt at dictionary indexing. The paper proposed that the technique could be improved by weighting the zone axes by the sum of the structure factors of the bands intersecting at the zone axes.
However, we never followed up on this idea as we abandoned the template matching approach and moved to the Burn’s algorithm coupled with the triplet voting scheme [5] which produced more reliable results. Using this approach, we were able to get our first set of fully automated scans. We presented the results at an MS&T symposium (Microscale Texture of Materials Symposium, Cincinnati, Ohio, October 1991) where Niels Krieger-Lassen also presented his work on band detection using the Hough transform [6]. After the conference, we hurried back to the lab to try out Niels’ approach for the band detection part of the indexing process [7].
Modern dictionary indexing applies an adaptive histogram filter to the experimental patterns (at left in the figure below) and the dictionary patterns (at right) prior to performing the normalized inner dot-product used to compare patterns. The filtered patterns are nearly binary and seeing these triggered my memory of our early dictionary work as they reminded me of the nearly binary “Sombrero” filtered patterns– Olé!
We may not have come back full circle but progress clearly goes in steps and some bear an uncanny resemblance to previous ones. I doff my hat to the great work that has gone into the development of dynamic pattern simulation and its applications.

[1] A. Winkelmann, C. Trager-Cowan, F. Sweeney, A. P. Day, P. Parbrook (2007) “Many-Beam Dynamical Simulation of Electron Backscatter Diffraction Patterns” Ultramicroscopy 107: 414-421.
[2] P. G. Callahan, M. De Graef (2013) “Dynamical Electron Backscatter Diffraction Patterns. Part I: Pattern Simulations” Microscopy and Microanalysis 19: 1255-1265.
[3] S.I. Wright, B. L. Adams, J.-Z. Zhao (1991). “Automated determination of lattice orientation from electron backscattered Kikuchi diffraction patterns” Textures and Microstructures 13: 2-3.
[4] Y.H. Chen, S. U. Park, D. Wei, G. Newstadt, M.A. Jackson, J.P. Simmons, M. De Graef, A.O. Hero (2015) “A dictionary approach to electron backscatter diffraction indexing” Microscopy and Microanalysis 21: 739-752.
[5] S.I. Wright, B. L. Adams (1992) “Automatic-analysis of electron backscatter diffraction patterns” Metallurgical Transactions A 23: 759-767.
[6] N.C. Krieger Lassen, D. Juul Jensen, K. Conradsen (1992) “Image processing procedures for analysis of electron back scattering patterns” Scanning Microscopy 6: 115-121.
[7] K. Kunze, S. I. Wright, B. L. Adams, D. J. Dingley (1993) “Advances in Automatic EBSP Single Orientation Measurements.” Textures and Microstructures 20: 41-54.

Teaching is learning

Dr. René de Kloe, Applications Specialist, EDAX

Figure 1. Participants of my first EBSD training course in Grenoble in 2001.

Everybody is learning all the time. You start as a child at home and later in school and that never ends. In your professional career you will learn on the job and sometimes you will get the opportunity to get a dedicated training on some aspect of your work. I am fortunate that my job at EDAX involves a bit of this type of training for our customers interested in EBSD. Somehow, I have already found myself teaching for a long time without really aiming for it. Already as a teenager when I worked at a small local television station in The Netherlands I used to teach the technical things related to making television programs like handling cameras, lighting, editing – basically everything just as long as it was out of the spotlight. Then during my geology study, I assisted in teaching students a variety of subjects ranging from palaeontology to physics and geological fieldwork in the Spanish Pyrenees. So, unsurprisingly, shortly after joining EDAX in 2001 when I was supposed to simply participate in an introductory EBSD course (fig 1) taught by Dr. Stuart Wright in Grenoble, France, I quickly found myself explaining things to the other participants instead of just listening.

Teaching about EBSD often begins when I do a presentation or demonstration for someone new to the technique. And the capabilities of EBSD are such that just listing the technical specifications of an EBSD system to a new customer does not do it justice. Later when a system has been installed I meet the customers again for the dedicated training courses and workshops that we organise and participate in all over the world.

Figure 2. EBSD IPF map of Al kitchen foil collected without any additional specimen preparation. The colour-coding illustrates the extreme deformation by rolling.

In such presentations, of course we talk about the basics of the method and the characteristics of the EDAX systems, but then it always moves on to how it can help understand the materials and processes that the customer is working with. There, teaching starts working the other way as well. With every customer visit I learn something more about the physical world around us. Sometimes this is about a fundamental understanding of a physical process that I have never even heard of.

At other times it is about ordinary items that we see or use in our daily lives such as aluminium kitchen foil, glass panes with special coatings, or the structure of biological materials like eggs, bone, or shells. Aluminium foil is a beautiful material that is readily available in most labs and I use it occasionally to show EBSD grain and texture analysis when I do not have a suitable polished sample with me (fig 2) and at some point, a customer explained to me in detail how it was produced in a double layer back to back to get one shiny and one matte side. And that explained why it produces EBSD patterns without any additional preparation. Something new learned again.

Figure 3. IPF map of austenitic steel microstructure prepared by additive manufacturing.

A relatively new development is additive manufacturing or 3D printing where a precursor powdered material is melted into place by a laser to create complex components/shapes as a single piece. This method produces fantastically intricate structures (fig 3) that need to be studied to optimise the processing.

With every new application my mind starts turning to identify specific functions in the software that would be especially relevant to its understanding. In some cases, this then turns into a collaborative effort to produce scientific publications on a wide variety of subjects e.g. on zeolite pore structures (1, fig (4)), poly-GeSi films (2, fig (5)), or directional solidification by biomineralization of mollusc shells (3).

Figure 4. Figure taken from ref.1 showing EBSD analysis of zeolite crystals.

Figure 5. Figure taken from ref.2 showing laser crystallised GeSi layer on substrate.

Such collaborations continuously spark my curiosity and it is because of these kinds of discussions that after 17 years I am still fascinated with the EBSD technique and its applications.

This fascination also shows during the EBSD operator schools that I teach. The teaching materials that I use slowly evolve with time as the systems change, but still the courses are not simply repetitions. Each time customers bring their own materials and experiences that we use to show the applications and discuss best practices. I feel that it is true that you only really learn how to do something when you teach it.

This variation in applications often enables me to fully show the extent of the analytical capabilities in the OIM Analysis™ software and that is something that often gets lost in the years after a system has been installed. I have seen many times that when a new system is installed, the users invest a lot of time and effort in getting familiar with the system in order to get the most out of it. However, with time the staff that has been originally trained on the equipment moves on and new people are introduced to electron microscopy and all that comes with it. The original users then train their successor in the use of the system and inevitably something is lost at this point.

When you are highly familiar with performing your own analysis, you tend to focus on the bits of the software and settings that you need to perform your analysis. The bits that you do not use fade away and are not taught to the new user. This is something that I see regularly during the training course that I teach. Of course, there are the new functions that have been implemented in the software that users have not seen before, but people who have been using the system for years and are very familiar with the general operation always find new ways of doing things and discover new functions that could have helped them with past projects during the training courses. During the latest EBSD course in Germany in September a participant from a site where they have had EBSD for many years remarked that he was going to recommend coming to a course to his colleagues who have been using the system for a long time as he had found that the system could do much more than he had imagined.

You learn something new every day.

1) J Am Chem Soc. 2008 Oct 15;130(41):13516-7. doi: 10.1021/ja8048767. Epub 2008 Sep 19.
2) ECS Journal of Solid State Science and Technology, 1 (6) P263-P268 (2012)
3) Adv Mater. 2018 Sep 21:e1803855. doi: 10.1002/adma.201803855. [Epub ahead of print]

One, Two, Three Times an Intern

Kylie Simpson, Summer Intern at EDAX

Kylie ‘at home’ in the Applications Lab.

This summer was my third working for the EDAX Applications Team. It has been an amazing opportunity to be directly involved with research, customer support, and software testing here in Mahwah. I was able to continue with the APEX™ software testing that I worked on last summer which I found incredibly interesting because I’ve been able to observe the software evolve to best meet customer needs and improve in overall performance. I also had the chance to attend the Microscopy and Microanalysis (M&M) show in Baltimore, MD. This was an incredible experience for an undergraduate student, like me, interested in Materials Science and Microscopy. I was able to connect with people in the field, attend talks on topics at the forefront of Microscopy research, and present a poster that I have been helping out with this summer here at EDAX.

EDS and VP-SEM: Practical considerations and challenges – M&M 2018 Poster

The majority of my time this year has been focused on helping Dr. Jens Rafaelsen, the head of the Mahwah Applications Team, with the data collection and analysis for a paper on the effects of Variable Pressure on EDS. Although Variable Pressure is an incredibly useful tool for studying SEM samples that are susceptible to charging, the introduction of gas to the specimen chamber has implications that must be considered when collecting EDS spectra. Additional gas particles in the SEM chamber lead to a scattering of the electron beam, known as beam spread or beam skirting.

In order to study and quantify this phenomenon, we used a double insulated Faraday cup with a 10 µm aperture, pictured below, to measure the unscattered beam at different pressures and working distances. We also modeled this beam scattering using Monte Carlo simulations that consider the SEM geometry as well as the type of gas in the chamber, which vary based on the type of SEM. Based on our experimental and theoretical results, we determined that as much as 85% of the electron beam is scattered outside of the 10 µm diameter high pressures of 130 Pa. This is much more scattering than we had anticipated, based on previous papers on this subject, making these results incredibly important for anyone using variable pressure in the SEM.

Double insulated Faraday cup with a 10 µm aperture.


Unscattered Beam Percentage vs. Pressure: Theoretical

Unscattered Beam Percentage vs. Pressure: Experimental

Overall, I am very thankful for the opportunities that EDAX has given me this summer and in the past. As a member of the Applications Team, I was able to work alongside the Engineering, Software Development, Customer Support, and Sales teams in order to help provide customers with the best analysis tools for their needs. I also gained a deeper understanding of the research, data collection, and analysis processes for writing a paper to be published: a truly incredible experience for an undergraduate student. Above all, the plethora of knowledge and experience of those here at EDAX and their willingness to share this information with me and others has been the most valuable aspect of my time here.

A Little Background on Backgrounds

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

If you have attended an EDAX EBSD training course, you have seen the following slide in the Pattern Indexing lecture. This slide attempts to explain how to collect a background pattern before performing an OIM scan. The slide recommends that the background come from an area containing at least 25 grains.

Those of you who have performed re-indexing of a scan with saved patterns in OIM Analysis 8.1 may have noticed that there is a background pattern for the scan data (as well as one of the partitions). This can be useful if re-indexing a scan where the raw patterns were saved as opposed to background corrected patterns. This background pattern is formed by averaging 500 patterns randomly selected from the saved patterns. 500 is a lot more than the minimum of 25 recommended in the slide from the training lecture.

Recently, I was thinking about these two numbers – is 25 really enough, is 500 overkill? With some of the new tools (Callahan, P.G. and De Graef, M., 2013. Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations. Microscopy and Microanalysis, 19(5), pp.1255-1265.) available for simulating EBSD patterns I realized this might be provide a controlled way to perhaps refine the number of orientations that need to be sampled for a good background. To this end, I created a set of simulated patterns for nickel randomly sampled from orientation space. The set contained 6,656 patterns. If you average all these patterns together you get the pattern at left in the following row of three patterns. The average patterns for 500 and 25 random patterns are also shown. The average pattern for 25 random orientations is not as smooth as I would have assumed but the one with 500 looks quite good.

I decided to take it a bit further and using the average pattern for all 6,656 patterns as a reference I compared the difference (simple intensity differences) between average patterns from n orientations vs. the reference. This gave me the following curve:
From this curve, my intuitive estimate that 25 grains is enough for a good background appears be a bit optimistic., but 500 looks good. There are a few caveats to this, the examples I am showing here are at 480 x 480 pixels which is much more than would be used for typical EBSD scans. In addition, the simulated patterns I used are sharper and have better signal-to-noise ratios than we are able to achieve in experimental patterns at typical exposure times. These effects are likely to lead to more smoothing.

I recently saw Shawn Bradley who is one of the tallest players to have played in the NBA, he is 7’6” (229cm) tall. I recognized him because he was surrounded by a crowd of kids – you can imagine that he really stood out! This reminded me that these results assume a uniform grain size. If you have 499 tiny grains encircling one giant grain, then the background from these 500 grains will not work as a background as it would be dominated by the Shawn Bradley grain!

Thoughts from a Summer Intern

Kylie Simpson, Summer Intern 2017, EDAX

This summer at EDAX, I have had the opportunity not only to build upon the skills that I acquired here last summer and throughout my academic year, but also to acquire new skills enabling me to better understand energy dispersive spectroscopy (EDS), materials science, and applied physics. Having access to state-of-the-art microscopes, detectors, and literature has certainly played a large role in my take-away from this summer, but the most valuable aspect of my time at EDAX is the expertise of those around me. Working with the applications team provided me with the opportunity to work alongside the different groups, including the engineering, sales and marketing, and technical support groups, as well as with customers via demos, training courses, and webinars. Not to mention the plethora of knowledge within the applications team itself. The willingness of other EDAX employees not only to help me, but also to explain and teach me how to solve the problems I encountered was extremely helpful.

The major projects I worked on this summer were compiling a user manual for the EDAX APEX™ software, collecting data for a steel library, and tuning a PID system for the thermoelectric cooler used in EDAX detectors. Creating a user manual for APEX™ enabled me to fully understand the software and describe it in a clear and useful way for our customers. I used LaTeX™ software to compile the manual, which exposed me to a very powerful typesetting tool while optimizing the layout and accessibility of the manual. Because I was not involved in the design of APEX™, I was able to write the user manual from the perspective of a new user. As a student and a newer user of EDAX software, I have recognized how useful APEX™ is for beginners and hope that the user manual will help to complement its value.

Figure 1: The EDAX APEX™ User Manual.

The steel library project that I worked on was very interesting because I compiled data that will simplify and aid customers working with steel samples. I collected spectra for nearly 100 steel standards and compared the quant results to the known values to confirm the accuracy of the data. This data will soon be available for purchase by customers who would like to compare the spectra from unknown samples to those of known standards using the spectrum match feature.

Figure 2: Me using one of our scopes to collect data.

Additionally, I was able to work with the engineering team to tune a PID system for the thermoelectric cooler inside all EDAX detectors. The module of each detector must reach a set point temperature in a set period of time and remain stable. By making small changes to the parameters and determining their impact, I ran tests over several weeks to optimize the cooling of the detector. These parameters will be used in future development of EDAX detectors, enabling them to work even more accurately.

Figure 3: The PID system I worked with and me.

Overall, my experience at EDAX has been very positive, providing me with the skills and knowledge to succeed and excel in both academics and my career.

XRF: Old Tech Adapting to New Times

Andrew Lee, Senior Applications Engineer, EDAX

X-rays were only discovered by Wilhelm Roentgen in 1895, but by the early 1900’s, research into X-rays was so prolific that half the Nobel Prizes in physics between 1914 to 1924 were awarded in this relatively new field. These discoveries set the stage for 1925, when the first sample was irradiated with X-rays. We’ve immortalized these early founders by naming formulas and coefficients after them. Names like Roentgen and Moseley seem to harken back to a completely different era of science. But here we are today a century later, still using and teaching those very same principles and formulas when we talk about XRF. This is because the underlying physics has not really changed much, and yet, XRF remains as relevant today as it ever was. You can’t say that for something like telephone technology.

XRF has traditionally been used for bulk elemental analysis, associated with large collimators, and pressed pellet samples. For many decades, these commercial units were not the most sophisticated instruments (although Apollo 15 and 16 in 1971 and 1972 included bulk XRF units). Modern hardware and software innovations to the core technique have allowed XRF to adapt to its surroundings in a way, becoming a useful instrument in many applications where XRF previously had little to offer. Micro-XRF was born this way, combining the original principles with newer hardware and software advancements. In fact, micro-XRF is included on the new NASA rover, scheduled for launch to Mars in 2020.

Biological/life sciences is one of those fields where possibilities are now opening as XRF technology progresses. A great example that comes to mind for both professional and personal reasons is the study of neurodegenerative diseases. Many such diseases, such as Parkinson’s, Alzheimer’s, and amyotrophic lateral sclerosis (ALS), exhibit an imbalance in metal ions such as Cu, Fe, and Zn in the human body. While healthy cells maintain “metal homeostasis”, individuals with these neurodegenerative diseases cannot properly regulate, which leads to toxic reactive oxygen species. For example, reduced Fe and Cu levels can catalyze the production of hydroxyl radicals which lead to damaged DNA and cell death. Imaging the distribution of biological metals in non-homogenized tissue samples is critical in understanding the role of these metals, and hopefully finding a cure. The common language between the people who studied physics versus the people who studied brain diseases? Trace metal distribution!

A few years ago, I had the opportunity to analyze a few slices of diseased human tissue in the EDAX Orbis micro-XRF (Figure 1 and 2), working towards proving this concept. Although the results were not conclusive either way, it was still very interesting to be able to detect and see the distribution of trace Cu near the bottom edge of the tissue sample. XRF provided unique advantages to the analysis process, and provided the necessary elemental sensitivity while maintaining high spatial resolution. This potential has since been recognized by other life science applications, such as mapping nutrient intake in plant leaves or seed coatings.

Figure 1. Stitched montage video image of the diseased human tissue slice, with mapped area highlighted in red. Total sample width ~25 mm.

Figure 2. Overlaid element maps: Potassium{K(K) in green} and Copper {Cu(K) in yellow} from mapped area in Figure 1, showing a clear area of higher Cu concentration. Total mapped width ~7.6 mm.

Sometimes, the application may not be obvious, or it may seem completely unrelated. But with a little digging, common ground can be found between the analysis goal and what the instrument can do. And if the technology continues to develop, there seems to be no limit to where XRF can be applied, whether it be outwards into space, or inwards into the human biology.

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

‘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.

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.