JEOL Resources

A Guide to Scanning Microscope Observation

Thu, 02 Apr 2020 12:53:57 GMT

We included in this book as many application examples as possible so that they can be used as criteria for judging what causes unsatisfactory image factors (hereinafter referred to as image disturbances). Although this edition does not describe all about image disturbances, it carries application photos to allow you to consider their causes. It is also important to correctly select the optimum observation conditions for various specimens. For instance, this book carries matters which are considered to be useful for using the instrument, such as the accelerating voltage, probe current and working distance (hereinafter abbreviated to WD).

We shall be pleased if this publication is of help to people who are now using or going to use SEMS.

A Note on Magnification

Mon, 02 Nov 2020 12:04:09 GMT

JEOL Technical Note

Magnification is defined as the ratio of the size of the rastered area on the sample to the size of the rastered area of the output, as is shown in Figure 1. Traditionally, the output size was defined as a Polaroid 4x5 film size by all vendors and results were easy to compare. However, since images are now collected digitally and can be output at various sizes, this “output size” is ill-defined. SEM manufacturers can choose different output sizes for their images, making magnification a very deceptive number when comparing SEM micrographs from different SEM manufacturers. Because of this fact, the best way to compare images is to compare the length of the micron bar or field of view.

Figure 1: A small raster on the specimen leads to a large magnification for the same output size.

The SEM images of hematite from two different SEM manufacturers below illustrate this point. The left image is from a JEOL SEM and has a magnification labeled as 75,000 X with a 100 nm micron bar. The right image is from another SEM manufacturer and has a magnification labeled as 150,000 X with the exact same length 100 nm scale bar (highlighted in red). This shows that the enlargement of the sample is identical in the two images, even though the magnification value stated by the other SEM manufacturer is twice that of the JEOL image.

Figure 2: SEM images of hematite with the same enlargement of the sample despite having different magnification values stated. Left image: From a JEOL SEM Right image: From a different SEM manufacturer

A sneak peek inside tomorrow’s lithium-ion batteries

Sat, 21 May 2022 17:50:43 GMT

To build better lithium-ion batteries, scientists are using advanced imaging and analysis tools to fine-tune battery materials.

New sample analysis and visualization tools are yielding clear images of what happens when lithium-ion batteries charge and discharge, such as this 250X scanning electron micrograph of a sample created using a cryo-cooled cross-section polisher.

Demand for rechargeable batteries is soaring, driven by electric vehicles, renewable energy storage, and portable electronics. Lithium-ion batteries attract the most attention because of their combination of longevity, rechargeability and low cost. Yet today’s lithium-ion systems are far from perfect — just ask anyone whose mobile phone died by mid-afternoon, or whose electric car took hours to recharge.

Tomorrow’s batteries will have to do better, and that presents a challenge. “People want to have batteries with longer cycle life. We want higher energy density. We want higher capacity,” says Grace Gorman, senior director for characterization and failure analysis at Enovix, a company in Fremont, California, that’s developing new batteries for a variety of uses.

Researchers have many potential ways to improve lithium-ion battery performance, ranging from new materials for electrodes to replacing liquid with solid electrolytes. But developing high-performing batteries means understanding lithium’s behavior inside them, and lithium has been loath to give up its secrets. Thanks to new imaging and analysis techniques, however, researchers are learning more about how the lightest of metals behaves in complex battery systems.

Replacement anodes

Whether it’s an iPhone, a laptop, or a car drawing power, what happens in a lithium-ion battery that’s discharging is similar to what happens in the familiar AA battery that has been powering devices for years. Positively charged ions move from the anode to the cathode through an electrolyte — a substance that allows the flow of charge. In lithium-ion batteries, positively charged lithium ions move from anode to cathode through a solution of lithium salts in a mixture of organic solvents. At the same time, electrons from the anode pass to the cathode through the laptop or whatever is drawing power, completing the circuit. When the battery is charging, the process reverses.

In most of today’s lithium-ion batteries, the anode is made of graphite, a heat-resistant, crystalline form of carbon that can store lithium ions. The cathode consists of a lithium-containing compound, such as lithium cobalt oxide. Since lithium is the lightest metal and the third-lightest element, after hydrogen and helium, a lithium-ion battery can store 50% more energy per unit weight than older rechargeable battery chemistries that use heavier metals, such as nickel-cadmium or nickel-metal hydride batteries.

The Enovix team is one of many that seek to increase energy density further by replacing the graphite anode with silicon, which can hold approximately 10 times as many lithium ions as graphite. This would offer a boost of between 15 and 30%, depending on the anode’s design.

Such gains, however, come at a price: anodes expand as they absorb lithium ions, and as they move through charge-discharge cycles, this swelling generates mechanical forces that crack and degrade the anodes over time. But while graphite expands by about 10%, silicon anodes can quadruple in size, making them more vulnerable to degradation.

Visualizing lithium

Battery performance also depends on how lithium ions move through the electrolyte, and where precisely lithium ions sit within the cathode and anode. For that reason, battery makers seek detailed information about lithium’s behavior so they can fine-tune the device’s microstructure.

Enovix’s researchers do this by imaging samples of a battery system at different points during the charge-discharge cycle. But here lithium’s small size becomes a disadvantage. Techniques for localizing atoms often rely on measuring how x-rays scatter as they interact with the electrons of a material. With just three electrons, lithium does not scatter X-rays as strongly as heavier atoms, and those scattered X-rays have very low energy, causing the signal they produce to be drowned out by stronger signals from other materials in the battery.

“It's always a challenge to figure out where the lithium is,” Gorman says. “Lithium is very hard to detect, and a lot of techniques struggle with either the spatial resolution or the quantity.”

What’s more, the same X-rays that visualize lithium ions can also knock lithium and other atoms out of place, damaging the battery material. To avoid this, Enovix researchers combine two techniques that together cause less damage and help find the hidden lithium.

The first method, called soft X-ray emission spectroscopy (SXES), uses X-rays that have longer wavelengths than conventional, or hard, X-rays and are less energetic. These soft X-rays do not illuminate a material’s atoms as intensely as hard X-rays, reducing the background noise in SXES. This makes it easier for researchers to observe the weak lithium signal, says Patrick Phillips, assistant product manager for transmission electron microscopy at JEOL USA, a manufacturer of electron microscopes and other analytical instruments, including the soft X-ray spectrometer that Gorman used.

The second method, called optimum bright-field imaging, was developed at the University of Tokyo and commercialized by JEOL. It uses a modified X-ray detector that separately visualizes images obtained using different X-ray frequencies and recombines them into a high-resolution image. This lowers the overall X-ray dose, protecting the material, while improving the signal-to-noise ratio.

“The [optimum bright-field] imaging technique allows the researchers to look at a sample and to pick out exactly where the lithium atoms are,” says Tom Isabell, a materials scientist and vice president at JEOL.

Air-free analysis

Lithium-ion batteries are typically closed systems that react when exposed to air. To spot atoms of lithium and other elements in experimental batteries, Yan Yao, a materials scientist at the University of Houston, studies samples from batteries in an air-free environment.

Yao is experimenting with different battery chemistries, such as replacing the transition metals used in cathodes with organic materials, which are cheaper and more readily available. To test each battery, he charges and discharges it, while watching how its materials change. He prepares samples with a JEOL ion-beam cross-section polisher that slices through various layers of the battery, leaving an even surface without imperfections. Then he uses a special sample holder he designed to move the sample from, say, a scanning electron microscope to a transmission electron microscope, without exposing it to air.

Yao is also working on devices made with solid, rather than liquid, electrolytes. He has observed how defects along the interface between the electrode and the solid electrolyte develop cracks as the electrode expands and contracts. This causes lithium atoms to form finger-like crystalline growths called dendrites that eventually grow large enough to short-circuit the device. Visualizing dendrite growth is critical to understanding the failure mechanisms that researchers must address. “It’s very hard to predict where the cell will fail,” Yao says. “But now, with this tool, we actually are able to pinpoint the source of the failure.”

And when it comes to understanding battery materials, imaging tools that predict failure may be the key to success. “These detectors are becoming faster,” JEOL’s Phillips says. “The cameras are getting better. Almost every aspect of the research is improving constantly.”

To learn more about imaging methods that are fueling advanced battery research, click here.

Advantages of Benchtop Scanning Electron Microscopy vs. Optical Microscopy for Pharmaceutical Applications

Sat, 20 Jan 2024 20:32:17 GMT

Optical microscope image (left) vs. Scanning Electron Microscope (SEM) image (right) of pharmaceutical tablet.

Throughout the discovery and manufacturing phases of bringing pharmaceuticals to market, scanning electron microscopy (SEM) plays a pivotal role in design and quality control. For visual inspection, the benchtop SEM far surpasses the capabilities of traditional optical or light microscopy with its large depth of field and functionality.

Quality Imaging and Resolution

With SEM it is possible to observe the compositional contrast that cannot be seen on an optical image. Examination of a pharmaceutical tablet or powder sample in the benchtop SEM reveals greater detail and compositional contrast than can be achieved with optical microscopes, even at the same magnification. With magnification up to 100,000X and versatile, automated settings, the SEM makes it possible to easily inspect the microstructure of tablets and powders, textures and coatings, foreign particles, and their chemical composition. Using the benchtop SEM, it is possible to identify the source of contamination from manufacturing processes.

SEM Image and EDS Analysis of Foreign Particle Contaminants (Metal and Glass)

The JEOL benchtop SEM offers 100,000X magnification and selectable settings for imaging: backscattered electrons to reveal morphology and topography and give insight as to composition, or secondary electrons to reveal surface topography.

Adding to these characterization capabilities, the JEOL NeoScope SEM has a 3D imaging feature for surface reconstruction using the multi-segmented BSE detector and automated montaging for high resolution view of a larger area.

Live 3D Surface Reconstruction – Pharmaceutical Tablet

Specimen: Pharmaceutical Tablet
Montage – Composite Image
Signal BED-C
Landing Voltage 10.0 kV
FOV 5.803 x 4.352 mm
Number of Fields 4 x 4
Field Magnification x75

When configured with analytical capabilities, the SEM conducts real-time chemical analysis using Energy Dispersive Spectroscopy (EDS). The operator can view EDS spectra in real time, set the analysis points, areas of interest, and map position.

EDS composite map of elements in Lantoprazole, a heartburn medication.

In most pharmaceutical products, drug molecules are present in a particulate, crystalline form. The Benchtop SEM can analyze the size, shape, purity, and other characteristics of a drug crystal to help predict its behavior in large-scale production. The characterization of these crystals can be used to guide the optimization of process parameters, minimizing manufacturing costs.

Insulin particles Au coated and imaged with the JEOL NeoScope Benchtop SEM.

JEOL’s NeoScope Benchtop SEM features simple navigation software, auto functions, selectable High and Low Vacuum modes, and integrated management of data collected through imaging and elemental analysis.

JEOL 4th generation NeoScope Benchtop SEM.

With the JEOL NeoScope Benchtop SEM, pharmaceutical companies gain a robust and versatile tool for analyzing substance morphology, topography and composition right from within their own laboratory environment. The small footprint and intuitive operation make it easy for any lab personnel to conduct the high resolution imaging and analysis that only an SEM can deliver.

Air Isolated Transfer System

Mon, 02 Nov 2020 12:16:34 GMT

Latest Innovation in our FE SEMs

SMART – POWERFUL – FLEXIBLE

There are a number of applications where scientists and engineers are faced with air or moisture sensitive samples that require imaging and analysis using a scanning electron microscope (SEM). Applications include: components in rechargeable batteries, fuel cells, and catalysts among others. Any exposure to oxygen or moisture in the air can completely alter or destroy the structure of these highly reactive materials. JEOL has built a special air-lock system that can handle the transfer of air-sensitive specimens to be imaged in the SEM without atmospheric exposure.

The sample can be prepared, mounted on the holder, and covered with a cap while inside a glove box. The cap seals and isolates the sample from the environment.

The sample holder (w/cap) is then removed from the glovebox and transferred into the load lock of the SEM. After the airlock is evacuated, the user can open the cap and put the sample into the FE-SEM without air exposure.

Aperture Angle Control Lens

Mon, 02 Nov 2020 12:32:11 GMT

JEOL FE-SEM – Innovative Design

SMART – POWERFUL – FLEXIBLE

The ability to increase the probe current for fast microanalysis, while still maintaining a small spot size and small volume of excitation for high resolution, has been the holy grail of microanalysis in SEM. One of the unique features of JEOL’s FE-SEMs is the patented Aperture Angle Control Lens (ACL). This lens automatically optimizes for both high resolution imaging at low probe currents and high spatial resolution X-ray analysis at high probe currents with a seamless transition between the two. This is essential for rapid analysis and optimized image quality and is particularly true for low kV microanalysis. The ACL works by taking into account effects of all aberrations (such as spherical aberration and diffraction limitations) on spot size and optimizing the convergence angle accordingly in an automatic fashion.

Figure 1: Beam current density on a Gaussian plane. ACL optimization at high beam currents for (a) image resolution, and (b) analytical resolution. Nano gold images and EDS analysis completed at 5 kV, 20kX, and 73 nA.

When the SEM is optimized for the smallest spot size (largest convergence angle) there is some beam tailing that produces X-rays from areas “not in the spot”. For low beam current applications this is insignificant. The best analytical data comes from the smallest convergence angle. Therefore, when the ACL is optimized for image resolution, the resulting high current image (large convergence angle) has somewhat ‘hazy’ background but shows great resolution. However, when the ACL is optimized for analytical work the ultimate resolution is slightly decreased, yet the analytical signal is no longer affected by the beam tailing resulting in smaller analytical signal delocalization (Figure 1).

The automatic optimization of the ACL maintains high resolution imaging at a wide range of accelerating voltages and probe currents, from a few picoamps to hundreds of nanoamps. This beam resolution allows very fast acquisition (using high beam current) of EDS or WDS data at low kV with high spatial resolution.

Figure 2: EDS map of nickel pillars on ITO which has sub-25nm resolution. The map was taken at 8kV with 5 nA of beam current.

Implementation of the ACL function allows collection of very fast EDS maps at high spatial resolution using low kVs. One such example is shown in Figure 2. This is an EDS map of nickel pillars on ITO with sub-25nm resolution. The map was collected at 8 kV using a beam current of 5 nA.

The ability to deliver high beam current also impacts WDS and CL (cathodoluminescence) analyses where large beam currents are needed for sufficient signal to noise collection. In the JEOL FE-SEMs, the ability of the ACL to keep the spot size small when the beam current is high results in efficient WDS mapping with high spatial resolution even at lower kV's. This is also true for CL analysis – high spatial resolution of CL collection requires a combination of high current/low voltage/small spot size.

Shown in Figure 3 is an example of slip planes in diamond imaged with panchromatic KE Centaurus CL detector at 2 kV.

Figure 3: CL image of diamond grains collected with KE Centaurus CL.

Automated Imaging Solutions for SEM

Wed, 03 Jan 2024 16:50:43 GMT

Automation of routine imaging in Scanning Electron Microscopy (SEM) has gained significant popularity over recent years. Automation provides users with additional levels of flexibility, including unattended and remote operation, as well as repeatability of their measurements. This ability maximizes productivity and sample throughput and significantly lowers the level of expertise required to proficiently operate SEMs. JEOL now offers both simple and advanced automation solutions, giving users the capability to develop protocols that fit their exact imaging needs. When paired with best-in- class AI-driven auto-function technology (auto focus, auto astigmatism correction, auto brightness/contrast), JEOL’s automation solutions are fast, reliable, reproducible, and applicable to a wide range of applications.

Simple Automation with Simple SEM

Simple SEM, JEOL’s latest advancement in automated imaging solutions, is a fully-integrated interface for creating and implementing imaging routines (Figure 1) without the need for programing experience. Users have the ability to develop custom automated workflows, including acquisition of SEM images and EDS data at a series of magnifications and locations on the sample surface and with varying operating conditions (accelerating voltage, probe current). Simply checking a box enables JEOL’s best-in-class auto-functions, with the added flexibility to control how often these functions are utilized within the workflow. Once routines are created, they are automatically saved and can be quickly implemented by simply selecting the area(s) on the sample that the user wants to characterize directly from a live image or ZeroMag view.

Simple SEM is available as part of the standard software package on JEOL’s JSM-IT210, JSM-IT510 and JSM-IT710 SEM models.

Figure 1. Simple SEM is fully integrated within JEOL’s SEM control software, creating an intuitive environment for users to develop automation workflows without any need for programing experience.

Compatible Instruments: JSM-IT210, JSM-IT510, JSM-IT710HR
Options: integration with JEOL EDS

Advanced Automation with Python and C#

For customers with more unique or challenging imaging demands, JEOL continues offers advanced external SEM control using Python or C# (Figure 2). This gives users the flexibility to fully develop and customize imaging protocols and interfaces, optimize acquisition at any operating conditions, automate image processing, and even integrate machine learning (Figure 3).

Figure 2. JEOL offers full external microscope control using Python and C#, allowing users to develop custom interfaces and automation programs. A full library of functions is available upon request.

Figure 3. External control allows users to develop custom interfaces and programs and integrate complex automation routines.

External control with Python (3.5.1 or later) and C# available with all current JEOL SEM models. Additional compatible SEM models are available upon request.

Automation of High‑Magnification Observation of NMC Cathode Active Material Particles Using an SEM External Control API and Python®

Thu, 07 May 2026 19:10:16 GMT

Introduction

For stable battery performance, it is essential to control the surface condition and particle shape of NMC (lithium nickel manganese cobalt oxide), which is used as a cathode active material in lithium‑ion rechargeable batteries. However, operators must manually measure large numbers of NMC particles and observe their surfaces at high magnification with scanning electron microscopes (SEMs), which requires significant time and effort. Recent SEMs are equipped with fast and accurate automatic contrast/brightness (ACB), auto focus (AF), and auto astigmatism correction (AS). They also provide external control APIs, allowing users to develop their own automation programs. In this study, to address the issues above, we automated high‑magnification observation of NMC particles by using the SEM external control API and Python®, based on an optimized sample preparation suitable for automatic measurement.

SEM automation with API and Python®

API for external control has been released to allow SEM operation using Python® or C#. Therefore, SEM routine workflows can be automated according to each research or observation purpose. In Python®, packages such as OpenCV, which is suitable for image processing, and matplotlib, which is useful for graph drawing, are available and easy to use. By combining these tools, it is possible to automate the entire process, including SEM operation, image analysis, graph creation, and report generation.

Integration of SEM External Control API and Python® for Automation

Sample preparation and substrate evaluation

Sample preparation greatly affects the accuracy of automatic adjustment and particle detection during automated observation. Therefore, we evaluated the substrate on which the sample is placed based on whether the substrate does not cause charging, provides sufficient contrast for binarization, and offers high throughput with good reproducibility. The NMC powder particles were dispersed in the preparation solution and dropped onto the substrate. As the preparation solution, we selected ethanol because it dries quickly and has high hydrophilicity. For the SEM conditions during comparison, we used a probe current of 400 pA and a landing voltage of 5 kV. The threshold for binarization was determined using the cv2.threshold function, which performs Otsu’s binarization [1] via the OpenCV library.

	Secondary Electron Image	Backscattered Electron Image	Result
Carbon tape with Al substrate			▲Charging of tape is observed in SEI image ●High compositional contrast ▲Deformation of the tape surface caused by ethanol is observed.
Nonwoven carton tape			▲Charging of tape is observed in SEI image ●High compositional contrast ▲Deformation of the tape surface caused by ethanol is observed
Carbon paste			●No charging ●High compositional contrast ●Easy to use; it only needs to be applied to the substrate
Si substrate			●No charging ▲The contrast difference between particles and Si substrate is not visible in the BSE image ●The Si substrate is disposable and easy to handle.

*Python is a trademark or registered trademark of Python Software Foundation.

High magnification observation automation condition

Flow chart of automation observation

SEM	JSM-IT810SHL
Landing voltage	5 kV
WD	7 mm
Probe current	400 pA
Detector	SED (Secondary electron detector) BED (Backscattered electron detector)

Sample	NMC811
Number of stubs	29 stubs
Field of view ratio per stub	3 field of views
Magnifications	x200, x500, x5,000, x50,000
Total number of acquired images	696

Python®	Version 3.10.17
OpenCV	Version 4.11.0
pythonnet	Version 3.0.5

Experiment result

A total of 696 images from all 29 stubs were acquired in 1 hour, 49 minutes, and 17 seconds. It was confirmed that all images were captured with appropriate brightness, focus, and astigmatism adjustment. The secondary electron (SE) images and backscattered electron (BSE) images of a selected field of view at each magnification are shown below. Because the images were acquired while keeping the particle within the field of view even at high magnification, the fine structure of the NMC surface could be observed. The particle shape at ×5,000 and the surface morphology at ×50,000 were clearly visualized. In addition, the size and shape of the submicron primary particles were distinctly observed.

SEM image acquisition result from x200 to x50,000

Using a backscattered electron compositional image at a magnification of ×200, the particle lengths in each field of view for each stub were measured simultaneously during observation. The measurement results from all fields of view across the 29 stubs were combined to calculate the particle size distribution and statistical values. The analysis quantitatively confirmed a broad particle size distribution, with a median size of 11.34 μm and a standard deviation of 7.1 μm.

When operators perform the same procedure manually, including sample preparation, it requires a full day. In contrast, the automated SEM not only completes the entire process in a shorter time but also allows the saved time to be used for other tasks during measurement.

Summary

We examined the preparation of samples and developed automated observation functions to enable the acquisition of large amounts of NMC particle data. It was found that carbon paste is suitable as a substrate material for mounting NMC particles. Furthermore, by combining an SEM external control API with Python®, we were able to construct a purpose‑specific automated SEM system. In the future, such customized automated SEM systems are expected to further improve efficiency in quality control and materials development, not only for NMC particles but also across various fields.

Reference

[1] Otsu, N. (1979). IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.

Bio Photographs: SEM biological photo album

Thu, 14 Nov 2024 14:34:22 GMT

Morphological observation is one of the important evaluations of substances. Morphology of biological specimens is often observed by optical microscopy, and a variety of information can be obtained by applying different staining or fluorescent labeling.

Since the scanning electron microscope (SEM) operates under vacuum, it is necessary to properly prepare a specimen in order to observe the biological specimen, and it is near-impossible to observe living organisms. However, in addition to observing the specimen surface at high magnification, by detecting various signals generated from the specimen, itis possible to obtain a lot of information by element analysis and other methods. Recently, methods for three-dimensional reconstruction and for observing waterdrops have been developed, and biological observation applications of SEM are becoming increasingly diverse.

We want you to know what SEM can do. We hope you will discover the microscopic beauty of the biological world. We hope that you will find this photo album we created as useful if you wish to observe biological samples in the future.

Can I Trust My Quantitative EDS Data?

Mon, 02 Nov 2020 17:48:37 GMT

NOTES ON STANDARDLESS QUANTITATIVE EDS IN SEM

Scanning electron microscopes (SEM) coupled with an energy dispersive X-ray detector (EDS) are used extensively to provide insight into a sample’s chemical makeup. This SEM-EDS technique can provide information on the elements present, their relative concentrations and spatial distribution over very small volumes (micron and some instances nanometer scale).

EDS, in general, is considered a semi quantitative elemental analysis technique. We are often asked how reliable are the quantitative results using SEM-EDS. This is a pretty broad question as it is dependent on a variety of factors including the sample matrix and morphology in addition to instrument considerations.

So… what can be detected and how much? Modern systems are capable of detecting elements from Be to U. Detection limits are typically considered to be ≥1% for low atomic number elements (F to Be) and ≥0.1% (1000 ppm) for higher atomic number elements.

One of the most common techniques used for quantitative EDS analyses is a method often described as Standardless quantitative EDS. With this method, the user does not use physical standards but instead uses a ratio of peak intensities to determine the relative abundance of the elements detected. The peak intensities are corrected for background and matrix effects and the results are then normalized to 100% based on the elements detected. This normalization can hide errors in the analysis results. With that said, if all criteria are met, one can expect around ±2% to ±5% relative for major components. However, this error can increase significantly for particles or rough surfaces.

So, what are the criteria to consider when performing EDS quantitative analysis? Several assumptions are made with this technique regardless of whether the quantitative method is ‘Standardless’ or with physical standards. First, the sample is polished and flat. It is also homogeneous and infinitely thick relative to the beam interaction volume. If the sample is not homogeneous with respect to the beam interaction volume, the results may vary based on the contribution of neighboring components (Figure 1).

Figure 1: A: Homogeneous sample within the beam scattering volume. B: Heterogeneous sample, a particle within the scattering volume will contribute to EDS quantitative results

On the other hand, it may be particles or inclusions that you are trying to identify and quantify. By placing the beam on the particle, you may get a contribution from the surrounding matrix if the scattering volume is larger than the particle itself. For non-uniform materials it is good practice to collect spectra from several different areas and average the results.

Figure 2: A: X-rays are blocked from reaching the detector by sample topography. B: Rotating the stage presents the region of interest in sight of the EDS detector allowing the X-rays to be detected.

Only those X-rays that are within line of sight to the EDS detector are collected. If the sample has significant topography, the X-rays can be blocked entirely and not reach the detector. Or, in some instances, low energy X-rays may be absorbed by the sample matrix more than higher energy X-rays contributing to error in the quantitative results.

When dealing with a topographic sample, it is important to understand the sample position with respect to the EDS detector position. It is often possible to position the region of interest so that it has direct line of sight to the detector (Figure 2).

Figure 3: Example of C X-ray Intensity Map of Ink on Paper taken from EDS detector position 2. The ink is raised on surface of paper and the result is a shadow where C X-rays are blocked by the topography of the sample from reaching the detector.

Finally, the accelerating voltage must be high enough for efficient excitation of the X-ray lines for the elements present in the sample and there should be sufficient probe current to generate a statistically significant X-ray count rate. What is typical is to choose an accelerating voltage that is 1.5 to 2 times higher in energy than the energy of the X-Ray lines that is of interest. For an unknown sample, 15kV to 20kV is recommended. Deviation from any of these conditions will contribute to errors in the quantitative analysis results.

Figure 4: Example: EDS Standardless Quantitative Results – Gold Alloy

Acquisition Condition
Volt : 20.00 kV
Live time : 203.01 sec.
Real Time : 244.76 sec.
DeadTime : 17.00 %
Count Rate : 11546.00 CPS