Characterisation, Measurement & Analysis
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  • Sample degradation during SEM analysis: what causes it and how to slow down the process

    When using a scanning electron microscope (SEM), the electron beam can, over time, permanently alter or degrade the sample that is being observed. Sample degradation is an unwanted effect as it can alter — or even destroy — the details you want to see, and consequently change your results and conclusions. In this blog, I will explain what can cause sample degradation, and how you can slow down the process.

    In a SEM, a focused electron beam is used to scan the surface of your sample to create an image. Electrons are generated by an electron source and accelerated through the column by an electric field. This field varies from 1 kV to 30 kV with typical beam currents in the nano-Ampere range.

    Accelerated electrons interact within the sample and, when analysing beam sensitive materials, this interaction can damage and degrade the sample. The degradation can be seen in the form of cracks on the surface, or it can appear that the material is melting or boiling. The speed at which the degradation becomes visible varies with accelerating voltage, beam current and magnification level.

    Sample degradation of different kinds of non-conductive materials

    In samples where the material appears to be melting or boiling, you might assume that the material is being heated by the electron beam. However, simulations of samples show that the melting point of materials can only be reached in extreme cases. These extreme cases are samples with a very low heat transfer coefficient, high beam current or high zoom level. Degradation can also set in at low electron beam currents and low magnification levels, but it will just occur over a longer time frame.

    What is causing the sample degradation?

    Depending on the accelerating voltage, electrons from the electron beam can interact with electrons in the atoms of the sample. If a valence electron — an electron that can participate in the formation of a chemical bond — happens to be knocked out of the atom, it will leave an electron hole. This hole must be filled by another electron within 100 femtoseconds (i.e. the typical time period of an atomic vibration), or the bond will be broken.

    In conductive materials, this is not a problem as the electron hole is filled within 1 femtosecond (fs). But for non-conductive materials it can take up to several microseconds to fill up the electron hole, potentially breaking the bond and chemically altering the material and its morphology.

    How to slow sample degradation down

    The speed at which the degradation becomes visible varies according to the material. With some samples, you might not see it at all. If samples do degrade and this interferes with your results, then here are a few tips to slow degradation down:

    • Coat the samples with a conductive (gold) layer to slow down the degradation. The thicker the layer, the better the effect. But be careful not to cover up details with the (conductive) gold layer.
    • Lower the beam current and acceleration voltage.
    • Limit viewing time by adjusting your image settings such as focus and contrast on a non-important part of your sample. When these are correct, move to the area of interest, immediately take a picture and move away again.

    Topics: Sample Preparation, Sample Degradation

    About the author:
    Karl Kersten is head of the Application team at Phenom-World, world’s no 1 supplier of desktop scanning electron microscopes. He is passionate about the Phenom product and likes converting customer requirements into product or feature specifications so customers can achieve their goals.

  • Guidelines for small script development: image acquisition

    Scripts are small software tools that help a Scanning Electron Microscopy (SEM) operator in their daily work. It can be used to automate a repetitive task, to scan large areas quickly, or to obtain a higher repeatability between measurements. To do this a software script must be developed. In this blog we will give guidelines how to develop a small script.

    SEM workflows

    • Typically, the workflow for SEM measurements consists of the following steps:
    • Sample prep and loading
    • Image acquisition
    • Analysis
    • Evaluation
    • Reporting
    • Conclusion/Action

    This workflow is illustrated in Figure 1. Steps 1 to 4 can be automated using scripts. Sample preparation has been extensively covered in our previous blogs on sample preparation techniques and on how sputter coating assists your SEM imaging.

    Image acquisition, step 1, is mostly self-descriptive and concerns all steps that are necessary to acquire a high-quality image. It starts with moving the sample to the right position under the microscope and setting image parameters. The task is completed by acquiring and saving the image.

    Image analysis concerns the processing of images to obtain the physical quantities from the images. This could, for example, be:

    • Thresholding BSD images to quantify compositional differences in the image
    • Segment the image to find particles
    • Measure the working distance to detect a height step in your sample
    Figure 1: Scanning Electron Microscopy workflow

    In the evaluation step, the physical quantities are evaluated and categorized. This can be done by:

    • Counting particles based on their morphology
    • Determining the coverage on a sample
    • Counting the number of defects on a sample

    In the reporting step, a report is made (automatically) that contains all relevant information to make a well-informed decision. The report could be:

    • The acquired images that contain useful information
    • Histograms showing information about the sample such as coverage or size distributions
    • A complete PDF report with tables, graphs and images

    If a script is made in an effective way, all these steps can be made with a single click of a button and a report will roll out once acquisition and processing is finished. Then all the operator has to do is check the report and decide what action is appropriate.

    In this blog we will focus on the first step of script development and explain how to acquire images efficiently and with the most suitable quality. In future blogs we will explore the other steps in more detail.

    Acquiring images with the Phenom through PPI
    In my previous blog, I have already shown how easy it is to capture an image with the Phenom through PPI. This time I’ll show the full potential of the image acquisition methods in PPI, and how easy it is to get these images in Python.

    Image acquisition is a method of the Phenom class. To acquire an image with the preferred settings we have to create an object containing the image acquisition parameters. This class is called: ScanParams. The scan parameters class contains the following attributes:

    • size: The dimensions (resolution) of the image to scan.
    • detector: The detector configuration.
    • nFrames: The number of frames to average for signal to noise improvement.
    • hdr: The option to use the High Dynamic Range mode, to create 16-bit images.
    • scale: The scale of the acquisition within the field of view.

    The detector is a separate class (called: detectorMode) that needs to be provided to the scan parameters. This class has the following options:

    • All: Use all BSD detector segments
    • NorthSouth: Subtract the bottom two BSD segments from the top two (Topo A)
    • EastWest: Subtract the left two BSD segments from the right two (Topo B)
    • A: Select only BSD segment A
    • B: Select only BSD segment B
    • C: Select only BSD segment C
    • D: Select only BSD segment D
    • Sed: Select SE detector

    This might all seems a bit intimidating, but it is very easy to use in Python with PPI. To show this, I created a little code snippet that will acquire an image using the Topo A mode of the Phenom. The other settings will be: resolution: 1024x1024 pixels; number of frames: 16; high definition mode: off (an 8-bit image will be acquired); and the image is not scaled. The code is:

    Figure 2: Acquiring images in PPI

    In the first line, PPI is loaded into Python. After that the Phenom object is created and the connection to the Phenom is set up. The scanParams are initialized and filled out according to the settings described in the previous paragraph. Acquiring the image is completed by calling the phenom.SemAcquireImage method while passing the scan parameters into it and the image is obtained.

    Developing a real-life small script
    To illustrate how a script is developed, we will show how a real-life script is developed. This script will image a copper-aluminum sample and find the boundary between these elements. The script will first determine what the characterising parameters of copper and aluminum are. It will use these characteristics to move the sample to a position in which both aluminum and copper are visible. The copper-aluminum stub is used in the calibration of energy-dispersive X-ray spectroscopy, Figure 3 shows an image of the stub.

    Figure 3: The copper aluminium sample

    First, it is important to know how you can differentiate between copper and aluminum in a SEM. In this blog it is explained that the gray-value of the BSD-image is directly correlated to the z-value of the atoms on the sample. This can be used to differentiated between copper and aluminum. Copper is lighter than aluminum and will therefore appear darker on the image.

    The contrast information is best obtained using 16-bit images. In 16-bit images entire registry from the ADC (analog-digital converter) is used instead of a sub-selection of this information. This is better than using 8-bit images because no auto-contrast/brightness needs to be applied between the images. Therefore, no information is lost, and images can be directly compared.

    Figure 3 shows that the centre of the stub is copper and its outer edge is aluminum. This geometrical information can be used in the process. For this example, we position the sample in the centre stub position in the Phenom XL, or in the normal stub position in the P-series. To determine the gray-level of the copper part we take an image at the centre of the stub. The gray level of the aluminum is determined by moving the stage by 0.5 cm to the right. To ensure that there is no overlap, the horizontal flied of view is set to 500µm.

    The images that have been acquired are plotted to validate they are correct. Matplotlib is used for plotting, it is a versatile plotting tool that is commonly used in Python. More information on Matplotlib can be found here.

    To acquire the images, move the stage and plot using the following code:

    Figure 4: PPI script to acquire an image of the copper and aluminum part of the stub

    The script in Figure 4 first loads PPI and Matplotlib into Python and sets-up the connection to the Phenom. It then moves the sample to the central location, assuming that the sample is loaded into the Phenom and is in focus (this can, of course, also be automated, but I’ll leave that as an exercise for the reader). The horizontal field of view is set to 500µm with phenom.SetHFW.

    The image settings can be set to a relatively low quality because the average of the gray value is barely influenced by it. A resolution of 256x256 is plenty for statistics (there are still more than 65,000 data points!). The BSD-detector is used with the ppi.detectorMode.All. A short integration time is also acceptable as the noise in the image is dominated by white noise. White noise will only increase the width of the gray-value spectrum (or in other words increase the standard deviation) but it will not change the average. I chose a very aggressive approach here by taking just 1 frame. After the first image is saved, the stage is moved by 0.5 centimeters to the right and a second image is acquired. These images are plotted using the plt commands, which call the Matplotlib package for plotting.

    These speed-tweaks in the scripts are often rather important to consider. In many well written scripts, the acquisition time dominates the execution time of a script. For example, if we had taken the acquisition parameters as shown in Figure 2, the acquisition time would be a factor of 20 slower - from about 4 seconds per image to 0.2 seconds. For scripts that acquire many images, it is especially important that the right set of image parameters is chosen.

    Figure 5: Acquired images of the copper and aluminum parts of the stub; plotted with Matplotlib

    To discover more about SEM and see if it fits your research requirements, you can take a look at our desktop SEM comparison sheet. It will give you a quick overview of the capabilities and specifications of several Phenom scanning electron microscopes.

    Click here to download our comparison sheet.


    Take a closer look at our SEMs and you will realise that they have a multitude of interesting specifications worth investigating, like their advanced light and electron optical magnification, resolution and digital zoom.

    Topics: Automation, PPI, Automated SEM Workflows

    About the author:
    Wouter Arts is Application Software Engineer at Phenom-World, the world’s no 1 supplier of desktop scanning electron microscopes. He is interested in finding new smart methods to convert images to physical properties using the Phenom. In addition, he develops scripts to help companies in using the Phenom for automated processes.

  • Battery research with a SEM: inspecting one layer at a time

    Batteries revolutionised the world of electronics by enabling us to carry an energy reserve in our pockets. Miniaturisation and efficiency are the two key words when it comes to new developments in this field, impacting with the battery materials’ properties and stretching their limits. Let’s take a look at how researchers characterise materials and gather relevant information about batteries using scanning electron microscopy (SEM).

    The structure of a battery consists of three main components: two electrodes made of different materials and an insulation membrane in between them. The different chemical composition of the electrodes makes them available for chemical interaction and during the reduction-oxidation processes that subsequently takes place, energy is released. The chemical energy stored in the electrodes is therefore converted into electrical energy and can be employed to power up our electronic devices.

    To go from the original battery concept (which would fit on a table) to the small and long-lasting battery of a smartwatch, some improvements were made. These mostly affected the materials used for the battery construction, rather than the working principle, which remained conceptually unchanged.

    Engineering batteries: what matters?
    When designing a new battery structure, the specification of the product that will be powered by it are crucial to achieve a good match in terms of size and capacity. There are some parameters that are commonly found in the battery research and development process:

    • Nominal voltage: This is an index of what voltage the battery can supply. A car and a watch require different amounts of energy and these values are obtained using different types of electrodes.
    • Self-discharge rate: Batteries cannot keep their charge forever and sometimes they just lose it. This can be tolerable for some applications, but can become extremely annoying if, for example, it happens with the battery of a remote controller, which requires very small amounts of energy with long time intervals in between. Temperature typically plays a dominant role in this context (ever wondered why your phone battery dies faster when it’s cold?).
    • Charging cycles: IF the battery can be recharged, it is very likely that it must be done quite often. Charge and discharge cycle will damage the battery components over time (specifically the electrodes) and the total amount of energy that can be accumulated will decrease over time. Optimising the material shape and composition helps to produce batteries that can withstand thousands of charging cycles and lose less than 10% of their nominal capacity.
    • Energy density: As its name suggests, this defines the amount of energy that can be accumulated per volume unit. This is improved not just by engineering the composition of the electrodes, but also their shape, to optimise the use of space with regard to the available reaction surface. In addition, the components’ size has been drastically reduced.
    • Safety: Reducing the size of components raises an important safety issue: the proper insulation of the electrodes. It is not a mystery that batteries can explode (you probably recall how some smartphone producers have actually struggled with this issue). This can happen, for example, when the insulating membranes that separate the electrodes break due to a mechanical stress (in other words, if the battery is bent too much).

    Improving battery quality with SEM
    All these parameters have, as mentioned, a strong dependency on the material composition and structure. These parameters can be easily monitored, but appropriate analysis instrumentation is required.

    Figure 1: left and right: SEM images of raw powders used in the production of cathodes. SEMs are ideal tools for investigating small particles in the range of micrometers or nanometers.

    SEM gives you the opportunity to improve battery research by enabling you to magnify your sample hundreds of thousands of times, making features of a few nanometers clearly visible. In this way it is possible to measure the cross section of layers, as well as the size of the small features on the electrode’s surface that improve the contact surface.

    In addition, it is possible to apply both thermal and mechanical stress to a membrane and observe its behaviour on a microscopic level, thus allowing battery researchers to understand the cause of an eventual rupture.

    Energy-dispersive X-ray microanalysis is often combined with SEM to locally identify the chemical composition of the sample accurately and with an outstanding, sub-micron, spatial resolution. And the analysis only takes few seconds!

    Figure 2: An example of how EDS can be used to trace how the sample composition changes along a line. Spot analysis, line scan or area map can be used to monitor the distribution of different phases in a specific region of the sample.

    To discover more about SEM and see if it fits your research requirements, you can take a look at our desktop SEM comparison sheet. It will give you a quick overview of the capabilities and specifications of several Phenom scanning electron microscopes.

    Click here to download our comparison sheet.


    Take a closer look at our SEMs and you will realise that they have a multitude of interesting specifications worth investigating, like their advanced light and electron optical magnification, resolution and digital zoom.

    Topics: Research Productivity, Sample Preparation, Scanning Electron Microscope, Batteries

    About the author:
    Luigi Raspolini is an Application Engineer at Phenom-World, the world’s no 1 supplier of desktop scanning electron microscopes. Luigi is constantly looking for new approaches to materials characterization, surface roughness measurements and composition analysis. He is passionate about improving user experiences and demonstrating the best way to image every kind of sample.

  • What is SEM? Scanning electron microscope technology explained

    Scanning electron microscopy (SEM) has become a powerful and versatile tool for material characterisation. This is especially so in recent years, due to the continuous shrinking of the dimension of materials used in various applications. In this blog, we explain what SEM is and describe the main working principles of a SEM instrument.

    What is SEM?
    SEM stands for scanning electron microscope. As the name suggests, electron microscopes use electrons for imaging, in a similar way that light microscopes use visible light. The optimal resolution of an imaging instrument depends mainly on the wavelength of the medium. Since the wavelength of electrons is much smaller than the wavelength of light, the resolution of an electron microscope is superior to that of a light microscope. In fact, it is usually more than 1,000 times better.

    There are two main types of electron microscopes:

    1. The transmission electron microscope (TEM), which detects electrons that pass through a very thin specimen;
    2. The scanning electron microscope (SEM), which uses the electrons that are reflected or knocked off the near-surface region of a sample to create an image.


    How does SEM technology work?
    Let’s focus on a SEM. A schematic representation of the technology of a SEM is shown in Figure 1 below. In this type of electron microscope, the electron beam scans the sample in a raster pattern. But first, electrons are generated at the top of the column by the electron source. These are emitted when their thermal energy overcomes the work function of the source material. They are then accelerated and attracted by the positively-charged anode. You can find a more detailed description of the different types of electron sources and their characteristics in this guide.


    Figure 1: schematic representation of the basic SEM components

    The entire electron column needs to be under vacuum. Like all the components of an electron microscope, the electron source is sealed inside a special chamber in order to preserve vacuum and protect it against contamination, vibrations or noise. Although the vacuum protects the electron source from being contaminated, it also allows the user to acquire a high-resolution image. In the absence of vacuum, other atoms and molecules can be present in the column. Their interaction with electrons causes the electron beam to deflect and reduces the image quality. Furthermore, high vacuum increases the collection efficiency of electrons by the detectors that are in the column.

    How is the path of electrons controlled?
    In a similar way to optical microscopes, lenses are used to control the path of the electrons. Because electrons cannot pass through glass, the lenses that are used here are electromagnetic. They simply consist of coils of wires inside metal pole pieces. When current passes through the coils, a magnetic field is generated. As electrons are very sensitive to magnetic fields, their path inside the microscope column can be controlled by these electromagnetic lenses - simply by adjusting the current that is applied to them. Generally, two types of electromagnetic lenses are used:

    The condenser lens is the first lens that electrons meet as they travel towards the sample. This lens converges the beam before the electron beam cone opens again and is converged once more by the objective lens before hitting the sample. The condenser lens defines the size of the electron beam (which defines the resolution), while the main role of the objective lens is to focus the beam onto the sample.

    The scanning electron microscope’s lens system also contains the scanning coils, which are used to raster the beam onto the sample. In many cases, apertures are combined with the lenses in order to control the size of the beam. These main components of a typical SEM instrument are shown in Figure 1.

    What kind of electrons are there?
    The interaction of electrons with a sample can result in the generation of many different types of electrons, photons or irradiations. In the case of SEM, the two types of electrons used for imaging are the backscattered (BSE) and the secondary electrons (SE).

    Backscattered electrons belong to the primary electron beam and are reflected back after elastic interactions between the beam and the sample. On the other hand, secondary electrons originate from the atoms of the sample: they are a result of inelastic interactions between the electron beam and the sample.

    BSE come from deeper regions of the sample (Figure 2), while SE originate from surface regions. Therefore, BSE and SE carry different types of information. BSE images show high sensitivity to differences in atomic number: the higher the atomic number, the brighter the material appears in the image.

    Figure 2: Different types of signals used by a SEM and the area from which they originate

    SE imaging can provide more detailed surface information — something you can see in Figure 3. In many microscopes, detection of the X-rays, which are generated from the electron-matter interaction, is also widely used to perform elemental analysis of the sample. Every material produces X-rays that have a specific energy; X-rays are the material’s fingerprint. So, by detecting the energies of X-rays that come out of a sample with an unknown composition, it is possible to identify all the different elements that it contains.

    Figure 3: a) BSE and b) SE image of the FeO2 particles

    How are electrons detected?
    The types of electrons mentioned above are detected by different types of detectors. For the detection of BSE, solid state detectors are placed above the sample, concentrically to the electron beam, in order to maximise the BSE collection.

    On the other hand, for the detection of SE, the Everhart-Thornley detector is mainly used. It consists of a scintillator inside a Faraday cage, which is positively charged and attracts the SE. The scintillator is then used to accelerate the electrons and convert them into light before reaching a photomultiplier for amplification. The SE detector is placed at the side of the electron chamber, at an angle, in order to increase the efficiency of detecting secondary electrons. These secondary electrons are used to form a 3D-image of the sample, which is shown on a PC monitor.

    SEM: magic but meticulous
    As you can see, there are different processes that the electrons must go through before an image can be shown on your monitor - Figure 4. Of course, you don’t have to wait for the electrons to finish their journey; the whole process is almost instantaneous, in the range of nanoseconds (10-9 seconds). However, every “step” of an electron inside the column needs to be pre-calculated and controlled with precision in order to obtain a high-quality image. Scanning electron microscopes are continuously improved, and new applications are still arising, making them fascinating instruments with lots of undiscovered capabilities.

    Figure 4: Backscattered electron image of Tungsten particles

    To discover more about SEM and see if it fits your research requirements, you can take a look at our desktop SEM comparison sheet. It will give you a quick overview of the capabilities and specifications of several Phenom scanning electron microscopes.

    Click here to download our comparison sheet.


    Take a closer look at our SEMs and you will realise that they have a multitude of interesting specifications worth investigating, like their advanced light and electron optical magnification, resolution and digital zoom.

    Topics: Electrons, EDX/EDS Analysis, Scanning Electron Microscope

    About the author:
    Antonis Nanakoudis is Application Engineer at Phenom-World, the world’s no 1 supplier of desktop scanning electron microscopes. Antonis is extremely motivated by the capabilities of the Phenom SEM on various applications and is constantly looking to explore the opportunities that it offers for innovative characterisation methods.


  • SEM working principle: the detection of backscattered electrons

    Backscattered electrons (BSEs) are high-energy electrons that are produced by the elastic scattering of the primary beam electrons with the atom nuclei. The yield of BSEs, that is the ratio of the number of emitted BSEs and the amount of primary beam electrons, depends on the atomic number: the higher the atomic number, or the heavier the element, the brighter the contrast. In the Phenom SEM, BSEs are detected using four-quadrant semiconductor detectors placed above the sample. In this blog, we will explain what a semiconductor detector is and how backscattered electrons are detected in a scanning electron microscope.

    Emission of backscattered electrons
    When the primary beam hits the surface of a sample, the incident electrons can interact with the nuclei of the atoms and their trajectories are deviated, as shown in Figure 1.

    Figure 1: Schematic of the incident electron being backscattered by the interaction with the atom nuclei.

    If the conditions are just right, the incident electron can be scattered back and emerge on the surface of the sample, preserving its high energy. Typically, heavier elements, because of their bigger nuclei, can deflect incident electrons more strongly than lighter elements. Hence, heavy elements like silver, which has the atomic number Z=47, appear bright in a SEM image compared to light elements, such as silicon, that has atomic number Z=14, because more backscattered electrons are emitted from the sample surface.

    An example of the different BSE contrast between silver and silicon in shown in Figure 2. This image shows a region of a solar cell, where the white area is silver and the dark area is silicon.

    Figure 2: SEM image of a solar cell, where the white area is silver, and the dark area is silicon.

    But how are the backscattered electrons detected in a SEM?

    Physics of a semiconductor detector
    The detection of BSEs is often carried out by solid state (or semiconductor) detectors. These consist of a doped semiconductor material (typically silicon) and are placed directly above the sample, as shown in Figure 2. The principle of semiconductor detectors is based on the generation of electron-hole pairs in a semiconductor by the incident BSE electrons. In simpler words: BSE electrons that hit the detectors excite the silicon electrons, creating an electron-hole pair.

    To form an electron-hole pair in silicon, an energy of 3.6 eV is required and the number of electron-hole pairs that are generated is proportional to the energy of the incident electrons. Moreover, semiconductor detectors are only sensitive to electrons with high energy, which is the reason why they are only used for the detection of backscattered electrons.

    Figure 3: schematic of a solid state (semiconductor) detector¹

    The electronic circuit of semiconductor detectors in an SEM
    The free electrons and pairs that are generated from incident backscattered electrons can be separated before their recombination, generating a current. This current can be measured by an electronic circuit that can be schematically described as an operational amplifier with an input resistance and a feedback resistor, as shown in Figure 3.

    Here the detector is shown as a charge collection current generator (Icc), in parallel with the resistance and capacitance of the depletion layer formed at the p-n junction of the doped silicon (Rd and Cd), in series with the internal bulk resistance of the semiconductor (Rs). Because the amplifier can become unstable for large values of RF/Re, an additional capacitance is added in the feedback loop to prevent the amplifier from oscillating.

    Figure 4: Electronic circuit of a semiconductor detector system¹

    Noise of semiconductor detectors in SEM
    The main sources of noise in semiconductor detectors are the shot noise of the incident BSE current and the noise of the preamplifier. Shot noise in the primary beam is caused by the fluctuations in the amount of emitted primary beam electrons. The number of primary electrons hitting the specimen in a certain amount of time is statistically distributed and follows the Poisson distribution¹. The statistics also increase the noise in the SE and BSE emissions. The noise in the preamplifier is thermal noise, called also Johnson-Nyquist noise, and it is proportional to the Boltzmann’s constant and the temperature of the resistor.

    To discover more about SEM and see if it fits your research requirements, you can take a look at our desktop SEM comparison sheet. It will give you a quick overview of the capabilities and specifications of several Phenom scanning electron microscopes.

    Click here to download our comparison sheet.

    Take a closer look at our SEMs and you will realise that they have a multitude of interesting specifications worth investigating, like their advanced light and electron optical magnification, resolution and digital zoom.

    1. Scanning Electron Microscopy, Physics of Image Formation and Microanalysis, L. Reimer, Springer edition, 1998.

    Topics: Sample Preparation, Electrons, Detectors

    About the author:
    Marijke Scotuzzi is an Application Engineer at Phenom-World, the world’s no 1 supplier of desktop scanning electron microscopes. Marijke has a keen interest in microscopy and is driven by the performance and the versatility of the Phenom SEM. She is dedicated to developing new applications and to improving the system capabilities, with the main focus on imaging techniques.

  • PicoScope 5000D Series: “The complete all-rounder”

    New FlexRes® oscilloscopes deliver flexible resolution, deep capture memory and mixed signal capability in a USB 3.0 PC connected instrument.

    Pico Technology, market leader in PC oscilloscopes and data loggers, today introduced the PicoScope 5000D Series FlexRes oscilloscopes and MSOs that feature up to 16 bits of vertical resolution with up to 200 MHz bandwidth and 1 GS/s sampling speed. FlexRes hardware employs multiple high-resolution ADCs at the input channels in different time-interleaved and parallel combinations to optimise either the sampling rate to 1 GS/s at 8 bits, the resolution to 16 bits at 62.5 MS/s, or other combinations in between.

    PicoScope 5000D MSO models add 16 digital channels, providing the ability to accurately time-correlate analog and digital channels. Digital channels may be grouped and displayed as a bus with each bus value displayed in binary, hex, decimal or level (for DAC testing). Advanced triggers can be set across both the analog and digital channels.

    PicoScope 5000D Series oscilloscopes have waveform capture memory up to 512 megasamples - many times larger than competing scopes. Deep memory enables the capture of long-duration waveforms at maximum sampling speed. PicoScope’s DeepMeasure™ tool uses the deep memory to analyse every cycle contained in each triggered waveform acquisition. It displays results in a table, with the parameter fields shown in columns and waveform cycles shown in rows. The current version of the tool includes twelve parameters per cycle, and can display up to a million cycles.

    Serial decoding and analysis is included as standard. Decoding helps users to see what is happening in their design to identify programming errors and check for signal integrity issues. Key applications are addressed with support for 18 protocols:

    • Automotive: CAN, CAN-FD, FlexRay, LIN, SENT
    • Embedded: 1-Wire, I2C, I2S, SPI
    • Avionics: ARINC 429
    • Computer: Ethernet 10 & 100BASE-T, PS/2, UART / RS-232, USB
    • Industrial: IMODBUS RTU & ASCII
    • Lighting: DMX512
    • Hobby: DCC

    PicoScope 5000D Series oscilloscopes feature a SuperSpeed USB 3.0 connection, providing lightning-fast saving of waveforms while retaining compatibility with older USB standards. The PicoSDK® software development kit supports continuous streaming to the host computer at rates up to 125 MS/s.

    “The 5000D builds on the success of PicoScope 5000A/B Series flexible resolution oscilloscopes that were introduced back in 2013. The 5000D gives designers and test engineers the versatility they need to make measurements on the wide range of waveforms encountered in today’s embedded systems,” said Trevor Smith, Business Development Manager, Test & Measurement, at Pico Technology. “This allows users to capture and decode fast digital signals and to look for distortion in sensitive analog signals, all using the same oscilloscope.”

    PicoScope software takes advantage of modern PC processing power with an equation editor that allows users to define complex waveform mathematical functions. These include filters (lowpass, highpass, bandpass and bandstop), trigonometry, exponentials, logarithms, statistics, integrals and derivatives. Waveform maths can also be used to plot live signals alongside historic peak, averaged or filtered waveforms.

    Software Development Kit

    The PicoSDK software development kit enables users to write their own applications for the PicoScope 5000D hardware. Drivers for Microsoft Windows, Apple Mac (macOS) and Linux (including Raspberry Pi and Beaglebone) are included. Example code, hosted on the Pico Technology GitHub pages, shows how to interface to third-party software packages such as Microsoft Excel, National Instruments LabVIEW and MathWorks MATLAB and programming languages like C, C#, C++, and Visual Basic .NET.

    If you would like more information, arrange a demonstration or receive a quote for the PicoScope 5000D Series; you can contact us via email, through our website or call us on 01582 764334.

  • An introduction to Electron Microscopy

    Electron Microscopy is a technique that makes use of the interactions between a focused electron beam and the atoms composing the analysed sample to generate an ultra-high magnification image. This technique, when compared to normal light microscopy, has the advantage of breaking the limit of resolution that comes with light microscopy and allows for resolution that can reach the atomic level.

    Light microscopy is, in fact, limited by the wavelength of light, which is physically set in a defined range. When going below the lower limit of this range, the image becomes blurry and it is no longer possible to distinguish details.

    Click here to download the complete E-Book from Phenom World.

    For further information, application support, demo or quotation requests  please contact us on 01582 764334 or click here to email.

  • How a desktop SEM saves lab operators a lot of time

    Is it true that as a lab operator, you work under constant time pressure? Do you find it challenging to deliver output quickly? And does it take hard work to maintain your high standard of quality? This blogs explains how a desktop scanning electron microscope (SEM) can be used to increase your research productivity and therefore to save a lot of time.

    These challenges might be the result of the technology you use. Chances are that you work with an optical microscope, or better yet — with an SEM. And that makes sense: because of its electron imaging, SEM produces a higher image resolution and magnification, greater image depth, and a visualisation of the three-dimensional external shape of an object.

    But a traditional SEM is also expensive, takes up a lot of space, and requires intensive training to master. And that is where a desktop SEM comes in (to replace the traditional floor model SEM). Are you familiar with a desktop SEM? Do you know what separates a desktop SEM from a traditional floor model SEM? In this blog, we will explain the differences to you.

    Desktop SEM vs. traditional floor model SEM
    A desktop SEM — also referred to as a personal or benchtop SEM — is a smaller, more handy version of the traditional floor model SEM. Because of its simplified user interface, a desktop SEM is easier to use than a floor model SEM — and that is why it will benefit you greatly.

    In fact, a desktop SEM is so manageable that even researchers with none or very basic lab skills can operate it. Which means they can now process their samples themselves. Simply said, a desktop SEM helps you delegate your work to researchers. The result: instead of focusing on all the SEM output you are supposed to deliver, you now have more time for your other lab responsibilities. Which means less pressure — and that is a big benefit.

    Other advantages of a desktop SEM
    Now that we have addressed the greatest benefit for you, let's take look at the other advantages of a desktop SEM:

    • A desktop SEM is significantly more affordable than a traditional floor model SEM.
    • Because of its smaller size, a desktop SEM is portable and can be placed on your workstation.
    • A desktop SEM requires less setup time and maintenance than a traditional floor model SEM.
    • A desktop SEM is much faster; it reduces loading times to as much as 80%, compared to a traditional floor model SEM. It does so by using an innovative loading technology that combines a sliding plate lock and a small evacuated volume.

    So, a desktop SEM is a more affordable, smaller, handier SEM that provides a push-of-a-button experience — and quick and qualitative results. And since it is so manageable that researchers themselves can operate it, it is a major time-saver for you.

    To discover more about SEM and see if it fits your research requirements, you can take a look at our desktop SEM comparison sheet. It will give you a quick overview of the capabilities and specifications of several Phenom scanning electron microscopes.

    Click here to download our comparison sheet.

    Take a closer look at our SEMs and you will realise that they have a multitude of interesting specifications worth investigating, like their advanced light and electron optical magnification, resolution and digital zoom.

    Topics: research productivity, materials science, scanning electron microscope

    About the author
    Karl Kersten is head of the Application team at Phenom-World, world’s no 1 supplier of desktop scanning electron microscopes. He is passionate about the Phenom product and likes converting customer requirements into product or feature specifications so customers can achieve their goals.

  • How SEM helps research polymers characteristics, properties and uses

    Polymers have many uses and applications: engineered combinations of monomers produce a nearly infinite number of molecules with different properties, which are determined by the chemical composition and structure of the molecule. The form of the molecule has a big influence on how the polymer will behave when exposed to different external forces. In this blog, you’ll find practical examples of how Scanning Electron Microscopes (SEMs) can provide unexpected results.

    Characteristics of thermoplastic polymers & investigation of their properties with SEM
    First, I’ll focus on what kind of information SEMs provide on thermoplastic polymers.

    These materials have a very linear chemical structure and weak interactions binding the molecules together. In these polymers, the bonds are easily broken when the polymer is heated up, which results in the material deforming. They have a good resistance to high temperatures, and are also characterised by a high chemical inertia and impressive resistance to abrasion.

    Thermoplastic polymers can undergo different kinds of industrial processes, such as printing or extrusion, making them the perfect crafting materials for items with the most complex of shapes.

    Fig. 1: A SEM image of a meltblown fiber. The diameter of the fiber can easily be measured at this magnification.

    To give a few examples of their applications, thermoplastic polymers are widely used in the production of fibres, electrical and electronic parts, packaging films, but also for daily use items, such as oven-proof kitchenware. SEMs can be used to investigate their properties and quality, but also to improve the processes and investigate how different forces affect these materials.

    Polymer properties: what does a SEM tell me about my polymer?
    After an abrasion test, a close look at the surface of the polymer can show the real consequences of the stress applied to the material. This allows for further development of the material or for quality controls at the end of the production chain.

    In this case, the interesting techniques are roughness analysis via stereoscopic reconstructions or shape from shading, which enable researchers to measure the depth of the scratches on the material.

    Fig 2.: A SEM image of a wax. SEM with EDS analysis was used to investigate the distribution and composition of particles dispersed in the polymeric matrix.
    Fig 3.: A semiconductor imaged with a SEM can be easily inspected to find defects in the production process.

    Diameters of fibres and particles can be measured very accurately on a picture taken at high magnification. These can provide different kinds of information, from fluidynamic properties, to the maximum particle size that can be caught in a filter, to how well a powder can be dispersed in a solution.

    Automated procedures are also available to instruct SEMs to autonomously collect pictures of the sample and measure important parameters like diameter, axis size, aspect ratios or areas. These results provide a huge amount of data easily and quickly, saving valuable time that researchers can invest in a more productive and efficient way.

    How SEM can help improve manufacturing processes like 3D printing
    SEMs can also be used to investigate new and trending manufacturing processes like 3D printing, where a polymer is extruded and manipulated to create a real-life version of a digital 3D drawing. The resolution and quality of the print, as well as the components of the printer itself, can be measured and investigated to dramatically boost the performance of the device.

    Fig 4.: A SEM image of a 3D-printed rabbit. SEM was used to investigate the object for defects.

    Polymers & EDS
    When analysing the distribution of particles in a film, knowing the composition of the different phases can help improve the dispersion process. This analysis can be easily performed using energy dispersive x-ray spectroscopy (EDX or EDS) — the most used microanalysis technique available on SEMs. In a couple of seconds, the chemical composition of the analysed sample is displayed on screen.

    Can I load my polymer in a Scanning Electron Microscope?
    Analysing a polymer with an electron microscope raises different problems. But as the polymer industry is one of the biggest players among SEM users, a number of simple solutions are available to obtain the desired results.

    For example, SEMs image electrons on the sample at a very high voltage. On the other hand, the current intensity is very small to avoid damage to the sample. On top of that, the observed sample has to be in a confined environment, in high vacuum. This can lead to several consequences for the material, depending on its chemical and physical resistance.

    The main problem is the accumulation of electrons on the surface of the sample, also known as the charging effect. This issue can be avoided by creating a conductive bridge linking the surface of the material to a part of the device which is at ground potential.

    An easier alternative is to change the vacuum level in the microscope according to the material specifications, which will lead to a massive discharging of the sample.

    The final option is a sputter coating device that can cover the material with a thin layer of gold or other conductive material. This will make it suitable for SEM analysis without meaningfully altering the structure of the sample.

    Polymers are generally very sensitive materials. The electron beam can damage them, especially when a very high voltage is applied. The electron emitted by the microscope can, in fact, interact with the delicate inter-molecular bonds and break them.

    Some SEMs provide a low emission current option that makes it possible to image the sample without damaging it.

    A company that already uses SEM to improve its products is SABIC, a main player in the polymers market. SABIC operates at several levels on the most sophisticated and advanced techniques in polymers production.

    Topics: sample preparation, 3D printing, materials science

    About the author
    Luigi Raspolini is an Application Engineer at Phenom-World, the world’s no 1 supplier of desktop scanning electron microscopes. Luigi is constantly looking for new approaches to materials characterisation, surface roughness measurements and composition analysis. He is passionate about improving user experiences and demonstrating the best way to image every kind of sample.


  • Preparation and property assessment of neat lignocellulose nanofibrils (LCNF) and their composite films

    Thomas Horseman, Mehdi Tajvidi , Cherif I. K. Diop , Douglas J. Gardner


    Lignocellulose nanofibrils (LCNF) were produced from thermo-mechanical pulp (TMP) using a micro-grinder and were characterised with respect to fibre diameter and thermal stability. The initial water content in the TMP affected the defibrillation process and longer grinding time was necessary for the airdried TMP, resulting in LCNF with higher fibril diameter. As compared to the reference cellulose nanofibrils (CNF) produced through a refining process, LCNF was less thermally stable and started to degrade at a temperature that was 30C lower than that of CNF. LCNF obtained from the never-dried TMP was combined with various additives (10 wt%) to produce composite films. The neat LCNF and composite films did not reach the mechanical properties of the neat CNF film that was evaluated as reference. However, the addition of poly(vinyl alcohol) (PVA) at 10 wt% on a dry basis did cause a 46 and 25% increase in tensile strength and elastic modulus, respectively. Other additives including cellulose nanocrystals, bentonite and CNF were also found to increase to some extent the Young’s modulus and ductility of the LCNF composite films whereas the addition of talc did not improve the film performance. Water absorption of neat LCNF films was lower than the reference CNF and was negatively affected by the addition of PVA.

    Click here to download the entire article


    ©  Springer Science+Business Media Dordrecht 2017

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