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Measurements for different PEDOT:PF6 fiber networks
(Technische Universität Dresden, 2024)Each folder represents one or more unique PEDOT:PF6 fiber networks. Different types of measurement might be found for each network, including impedance measurements ("Autolab" folder), transistor measurements ("SMU" folder) ... -
Python source code, text corpora and automatically generated datasets for Machine Learning
(Technische Universität Dresden, 2023)This is supplementary information to the report: "Entwicklung eines Input-Algorithmus zur Erzeugung von Lerndatensätzen für Maschinelles Lernen mittels Natural Language Recognition" -
Supplemental material to "Maximal Anderson Localization and Suppression of Surface Plasmons in Two-Dimensional Random Au Networks"
(Leibniz-Institut für Festkörper- und Werkstoffforschung Dresden, 2023)Two-dimensional random metal networks possess unique electrical and optical properties, such as almost total optical transparency and low sheet resistance, which are closely related to their disordered structure. Here we ... -
Supplemental material for publication: "Chaotic resonance modes in dielectric cavities: Product of conditionally invariant measure and universal fluctuations"
(Technische Universität Dresden, 2022)The supplemental material to the paper (Physical Review Letters 2022, arXiv:2203.09752) presents a gallery of individual modes in position space from short-lived modes to modes with longest lifetime and a mode with high ... -
Data for NCOMMS-22-00582A, Rahn et al.
(Technische Universität Dresden, 2022)This dataset contains the necessary data and minimal Matlab scripts to reproduce the Figures in "Kondo quasiparticle dynamics observed by resonant inelastic x-ray scattering" (Rahn et al., 2022) -
Training and Predicted Data for Machine Learning on Thermoelectric Materials
(Technische Universität DresdenIFW Dresden, 2022)This is supplementary information to the manuscript: "Sustainable Thermoelectric Materials Predicted by Machine Learning" -
Research data supporting the paper "Organic bipolar transistors"
(Technische Universität Dresden, 2022)Supplemental experimental data to the paper "Organic bipolar transistors". For the experimental details, please refer to the original paper. -
Jupyter notebook code and example file for the evaluation of XRR data via FFT
(Technische Universität DresdenIFW Dresden, 2020)This is supplementary information to the publication: "Fast fourier transform and multi-Gaussian fitting of XRR data to determine the thickness of ALD grown thin films within the initial growth regime"
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