Applications of AFM
The nGauge AFM has many different capabilities:
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An AFM produces a 3-dimensional representation of the surface that it scans over. Unlike optical or electron microscopes, AFM collects topographic data. That means that you can look at the shape and size of individual features, such as the pits on a DVD, or determine the particle density, such as the number of nanoparticles in a given area.
The image on the left is an AFM topography image of a calibration sample. The size of this scan is 10 µm × 10 µm: the scale bars are on the top and left sides of the image.
The colour is not the real colour of the surface. The contrast is used to differentiate between high (light gold) and low (dark) points. The scale bar on the right is for the vertical dimension. It shows that the tallest feature on the scan is about 1.2 µm tall. The image on the right is a 3-dimensional representation of the surface, using the topography data from the image on the left.
The nGauge AFM can be used to investigate surfaces where the maximum step height is up to 10 µm tall. It's tricky to pinpoint a lower limit, but the RMS noise in the vertical (z) direction of the nGauge is 1 nanometre (nm). Features as small as 10 nm can be imaged with the nGauge with acceptable accuracy.
The line profile provides the height information (z) of the surface along a user-selected line. On the left is a 3 µm x 3 µm AFM image of DVD. On the right is a graph of the line profiles across the lines shown on the AFM image on the left. These line profiles were generated in Gwyddion.
The depth of the pits of a DVD is 120 nm. The line profiles show that the AFM topography data is in good agreement with the actual value. Because the vertical resolution of an AFM is so high, the height of the features is highly accurate. For instance, the noise in the vertical direction of the nGauge is ~ 1 nm RMS. It's good to keep in mind that post-processing of the AFM topography image, with techniques such as levelling, will have a considerable effect on the final result.
Another useful measurement is the distance between features in an AFM image. It’s not easy to say how wide the DVD pits are based on the line profile above: where exactly do the pits begin and end?
Using the distance tool, we can see that the distance between the centre of the pits, also known as pitch, using Line 1 is 741 nm. This is in good agreement with the real pitch (740 nm).
The width of the pits is a bit trickier to measure exactly. The geometry of the tip affects the lateral measurement. This is clear in the illustration below on the left: the red dashed line shows the profile that the tip tracks. Because the tip isn’t perfectly sharp and doesn’t have an infinitely large aspect ratio, the tip does not track the walls of the pit exactly. This artifact is the reason why the actual width of the pits (320 nm) is shown by Lines 2 and 3 (322 nm), which look like they extend outside the pit in the topography image.
The illustration on the right shows how an AFM tip tracks a particle.
The tip radius of an nGauge AFM is maximum 80 nm. A nanoparticle with a 50-nm diameter will show up as ~175 nm-wide hemisphere on an AFM image. This phenomenon is known as tip convolution artifact and affects all AFMs. There are a few ways to account for tip convolution:
(1) If the particles are spherical, the particle size can be determined from the height of the particle and the width can be corrected or ignored.
(2) If the tip radius is known, a correction can be made using post-processing software. More information is available in the Gwyddion article on Tip Convolution Artifacts.
Surface roughness is a component of the texture of a surface: a higher value means that the surface is rougher. Surface roughness is also known as surface finish. The arithmetic roughness (Ra) and the root-mean-squared roughness (Rq) are common parameters used to describe roughness.
Many types of devices, such as profilometers or optical profilers, use data from a line scan to calculate surface roughness. Because AFM collects topographic data in two dimensions, the surface roughness can be calculated from the entire 2-d scan area rather than just 1-d data. The 2-d/area roughness parameters are arithmetic (Sa) and RMS (Sq).
The nGauge AFM can be used to accurately determine the roughness of a surface between 10 nm and 4 µm (0.39 microinch to 150 microinch). The surface roughness of a sample can easily be determined using the statistical quantities tool in Gwyddion. An AFM image of a titanium-aluminum alloy is shown on the left with the corresponding output from the statistical quantities tool in Gwyddion.
The maximum area scan size is 20 µm x 20 µm. To get a representative result of a large surface, it’s best to take multiple scans of a surface and compute the average surface roughness from these scans.
To give an example application of surface roughness measurement, a study published in December 2017 by Prof. Richard Price's group in collaboration with Prof. Laurent Kreplak at Dalhousie University in Halifax, Canada used the nGauge AFM to determine the effect of tooth brushing on the surface roughness of dental composites in the Journal of Esthetic and Restorative Dentistry.
Particle Analysis and Counting
Particle analysis is a very common application of AFM. A line profile can be used to look at individual particles, but for more than a few particles, it’s best to automate the routine somehow. A particle segmentation routine does just that by separating the particles from the flat surface that they are on. Segmentation routines can also be used for particle counting. Software such as ImageJ can be used for segmentation.
It is possible to complement the topography images with the phase images to determine whether the particles are indeed separate and different from the substrate. Phase imaging is described in detail below.
When operating in a mode called Tapping Mode, AFMs generate two different images: the topography image and the phase image. The phase image comes from the phase shift of the signal: the phase shift is the lag between the driving signal and the feedback signal. This lag is caused by the interaction between the probe tip and the surface, which can be affected by adhesive forces, frictional forces and viscoelastic forces. (More information in our Phase Images blog post.)
Regions with different material properties can be distinguished using the contrast of the phase image. Since the topography image and the phase image are generated at the same time, analysis of both images side by side can reveal information that might be hidden from just the topography image alone.
Below is an example of the topography (left) and phase (right) images of a silica-polymer composite. On the left, you can see a few particles that are protruding from the surface. They are bright in the topography image because they are the tallest features. It’s unclear what the structure or morphology is of the rest of the image. The phase image on the right makes it clear which areas are silica: the dark areas represent regions with a low phase shift (around -50°). The light areas possess a much higher phase shift (around 30°) and they represent the polymer matrix. This is because the silica particles possess very different properties compared to the polymer matrix.
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