STM experiment (HOPG) data analysis/image processing

In summary, the person has a great picture of carbon atoms in a graphene sample using STM technique and wants to remove noise from it using MATLAB. They have tried using fast Fourier transform and the "filter" command but with no success. They are seeking help in setting a cut-off using MATLAB to remove high-frequency noise.
  • #1
itzik26
6
0
hi,
I've got a really great picture of the carbon atoms in a graphine (HOPG) sample, using STM technique (picture attached). I would like to remove from the picture all the data came from noise etc.. I'm working with MATLAB, and I tried to use fast Fourier transform (fft2), but didn't know how to move on from there. I've also tried to use the "filter" command, but without any success. Does somebody have any idea? thanks
 

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  • #2
The picture looks pretty good as it is, why not show it without more processing?

Otherwise, you can try to Fourier transform it. Then select a cut-off and set the Fourier components above that to zero, then back-transform. This will remove high-frequency noise.
 
  • #3
it really looks good, doesn't it?? I got really excited :)

do you know how can I do this cut-off in matlab?
 
  • #4
Setting elements of a matrix to zero? You should be able to do that in Matlab...
 

1. What is an STM experiment?

An STM (Scanning Tunneling Microscopy) experiment is a scientific technique used to image and study the surface of materials at the atomic level. It involves scanning a sharp probe over the surface of a sample and measuring the electrons that tunnel between the probe and the sample. This allows for the creation of high-resolution images of the surface, which can provide information about the atomic and electronic structure of the material.

2. What is HOPG?

HOPG (Highly Oriented Pyrolytic Graphite) is a type of graphite that is commonly used as a sample in STM experiments. It has a highly ordered atomic structure, making it an ideal material for studying surface properties at the atomic level.

3. How is data analyzed in an STM experiment?

Data analysis in an STM experiment involves processing the raw data collected from the probe-sample interaction to create an image of the surface. This typically involves removing noise and artifacts, as well as adjusting the contrast and brightness to enhance the features of interest. Advanced techniques such as Fourier transformation and deconvolution may also be used to extract more detailed information from the data.

4. What is image processing in STM experiments?

Image processing is a crucial step in analyzing STM experiment data. It involves enhancing and correcting the acquired image to make it easier to interpret and extract meaningful information from. This can include adjusting the brightness and contrast, removing noise and artifacts, and applying mathematical algorithms to extract quantitative data from the image.

5. What are some common challenges in STM data analysis and image processing?

Some common challenges in STM data analysis and image processing include dealing with noise and artifacts, distinguishing between surface features and probe artifacts, and accurately interpreting the data to extract meaningful information. Additionally, STM experiments can be highly sensitive to environmental factors such as temperature and humidity, which can affect the quality of the data and require careful control and calibration.

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