Data underlying the PhD thesis: "Superconducting contacts for atomically precise graphene nanoribbons"
doi: 10.4121/0240a853-27a0-47fe-8270-c346f0288580
QCoDeS databases and .txt/.dat data files underlying the PhD thesis "Superconducting contacts for atomically precise graphene nanoribbons".
The thesis consists of electrical measurements on graphene nanoribbon field effect transistors (FETs) and superconductor-normal metal-superconductor (SNS) junctions.
The data is organised per chapter. All measurements performed are electrical measurements in which either a voltage or a current is measured as a function of independent parameters (bias voltage, bias current, temperature, magnetic field, etc.). For each set of independent parameter values, the dependent parameter is registered.
Types of data included:
- QCoDeS databases (.db files) can be opened using a python installation with QCoDeS installed. The database contains datasets, as well as metadata for each measurement. See "https://qcodes.github.io/Qcodes/" for instructions on how to read these database files. All units used are standard SI (V, A, K, T)
- .dat files (in Chapter 1 only) are text files containing single measurements that can be opened with notepad, notepad++ and other text editing software, or imported in Python using numpy's genfromtxt command: https://numpy.org/doc/stable/reference/generated/numpy.genfromtxt.html
- Also included: folders of (SEM and optical microscopy) image data (.png, .tiff, .bmp).
- Still to be included: folders with data analysis scripts for reproduction of thesis figures
Three folders per chapter:
- Data
- Images (unedited images. Edited versions (zoomed in and/or enhanced contrast) in thesis)
- Scripts (data analysis)
Data analysis scripts can be run in a jupyter notebook capable python environment in which QCoDeS is installed in order to produce the figures in the thesis. (I recommend an anaconda installation with jupyter, qcodes and/or kwant installed in an environment)
For the tight binding simulations in the thesis, a python environment with kwant should be installed: See "https://kwant-project.org/".
- 2024-04-17 first online, published, posted
DATA
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README.txt - 1,089,234,120 bytesMD5:
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Thesisdata.zip -
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