TY - JOUR A1 - Hoffmann, Andreas A1 - Uhl, Matthias A1 - Ceblin, Maximilian A1 - Rohrbach, Felix A1 - Bansmann, Joachim A1 - Mallah, Marcel A1 - Heuermann, Holger A1 - Jacob, Timo A1 - Kuehne, Alexander J.C. T1 - Atmospheric pressure plasma-jet treatment of PAN-nonwovens—carbonization of nanofiber electrodes JF - C - Journal of Carbon Research N2 - Carbon nanofibers are produced from dielectric polymer precursors such as polyacrylonitrile (PAN). Carbonized nanofiber nonwovens show high surface area and good electrical conductivity, rendering these fiber materials interesting for application as electrodes in batteries, fuel cells, and supercapacitors. However, thermal processing is slow and costly, which is why new processing techniques have been explored for carbon fiber tows. Alternatives for the conversion of PAN-precursors into carbon fiber nonwovens are scarce. Here, we utilize an atmospheric pressure plasma jet to conduct carbonization of stabilized PAN nanofiber nonwovens. We explore the influence of various processing parameters on the conductivity and degree of carbonization of the converted nanofiber material. The precursor fibers are converted by plasma-jet treatment to carbon fiber nonwovens within seconds, by which they develop a rough surface making subsequent surface activation processes obsolete. The resulting carbon nanofiber nonwovens are applied as supercapacitor electrodes and examined by cyclic voltammetry and impedance spectroscopy. Nonwovens that are carbonized within 60 s show capacitances of up to 5 F g⁻¹. Y1 - 2022 U6 - http://dx.doi.org/10.3390/c8030033 SN - 2311-5629 N1 - This article belongs to the Collection "Nanoporous Carbon Materials for Advanced Technological Applications" VL - 8 IS - 3 PB - MDPI CY - Basel ER - TY - JOUR A1 - Hoffmann, Andreas A1 - Rohrbach, Felix A1 - Uhl, Matthias A1 - Ceblin, Maximilian A1 - Bauer, Thomas A1 - Mallah, Marcel A1 - Jacob, Timo A1 - Heuermann, Holger A1 - Kuehne, Alexander J. C. T1 - Atmospheric pressure plasma-jet treatment of polyacrylonitrile-nonwovens—Stabilization and roll-to-roll processing JF - Journal of Applied Polymer Science N2 - Carbon nanofiber nonwovens represent a powerful class of materials with prospective application in filtration technology or as electrodes with high surface area in batteries, fuel cells, and supercapacitors. While new precursor-to-carbon conversion processes have been explored to overcome productivity restrictions for carbon fiber tows, alternatives for the two-step thermal conversion of polyacrylonitrile precursors into carbon fiber nonwovens are absent. In this work, we develop a continuous roll-to-roll stabilization process using an atmospheric pressure microwave plasma jet. We explore the influence of various plasma-jet parameters on the morphology of the nonwoven and compare the stabilized nonwoven to thermally stabilized samples using scanning electron microscopy, differential scanning calorimetry, and infrared spectroscopy. We show that stabilization with a non-equilibrium plasma-jet can be twice as productive as the conventional thermal stabilization in a convection furnace, while producing electrodes of comparable electrochemical performance. KW - batteries and fuel cells KW - electrospinning KW - fibers KW - irradiation KW - porous materials Y1 - 2022 U6 - http://dx.doi.org/10.1002/app.52887 SN - 0021-8995 (Print) SN - 1097-4628 (Online) N1 - Weitere Informationen: Bundesministerium für Bildung und Forschung, Fördernummer: 13XP5036E. Deutsche Forschungsgemeinschaft, Fördernummern: 390874152, 441209207, 327886311 VL - 139 IS - 37 SP - 1 EP - 9 PB - Wiley ER - TY - JOUR A1 - Förster, Arnold A1 - Rosenauer, A. A1 - Oberst, W. A1 - Gerthsen, D. T1 - Atomic scale analysis of the indium distribution in InGaAs/GaAs (001) heterostructures: segregation, lateral indium redistribution and the effect of growth interruptions. Rosenauer, A. ; Oberst, W. ; Gerthsen, D. ; Förster, A. JF - Thin Solid Films. 357 (1999) Y1 - 1999 SN - 0040-6090 SP - 18 EP - 21 ER - TY - JOUR A1 - Förster, Arnold A1 - Rosenauer, A. A1 - Remmele, T. T1 - Atomic scale strain measurements by the digital analysis of transmission electron microscopic lattice images / A. Rosenauer ; T. Remmele ; D. Gerthsen ... A. Förster JF - Optik : international journal for light and electron optics. 105 (1997), H. 3 Y1 - 1997 SN - 0030-4026 SP - 99 EP - 107 ER - TY - CHAP A1 - Pirovano, Laura A1 - Seefeldt, Patric A1 - Dachwald, Bernd A1 - Noomen, Ron T1 - Attitude and Orbital Dynamics Modeling for an Uncontrolled Solar-Sail Experiment in Low-Earth Orbit T2 - 25th International Symposium on Spaceflight Dynamics, 2015, Munich, Germany Y1 - 2015 ER - TY - CHAP A1 - Pirovano, Laura A1 - Seefeldt, Patric A1 - Dachwald, Bernd A1 - Noomen, Ron T1 - Attitude and orbital modeling of an uncontrolled solar-sail experiment in low-Earth orbit T2 - 25th International Symposium on Space Flight Dynamics ISSFD N2 - Gossamer-1 is the first project of the three-step Gossamer roadmap, the purpose of which is to develop, prove and demonstrate that solar-sail technology is a safe and reliable propulsion technique for long-lasting and high-energy missions. This paper firstly presents the structural analysis performed on the sail to understand its elastic behavior. The results are then used in attitude and orbital simulations. The model considers the main forces and torques that a satellite experiences in low-Earth orbit coupled with the sail deformation. Doing the simulations for varying initial conditions in attitude and rotation rate, the results show initial states to avoid and maximum rotation rates reached for correct and faulty deployment of the sail. Lastly comparisons with the classic flat sail model are carried out to test the hypothesis that the elastic behavior does play a role in the attitude and orbital behavior of the sail KW - Solar sail KW - Gossamer structures KW - Attitude dynamics KW - Orbital dynamics Y1 - 2015 N1 - 25th International Symposium on Space Flight Dynamics ISSFD October 19 – 23, 2015, Munich, Germany https://issfd.org/2015/ SP - 1 EP - 15 ER - TY - CHAP A1 - Fabo, Sabine T1 - Audio-visual hybrids : between immersion and detachment N2 - Close interrelations between sound and image are not a mere phenomenon of today’s multimedia technology. The idea of the synthesis of different media lies at the core of the concept of the Gesamtkunstwerk in the second half of the 19th century and it can also be traced back to the synaesthesia debate at the beginning of the 20th century [...]. KW - Elektronische Kunst KW - Klangkunst KW - Hybride Kunst KW - Medienkunst KW - Hybrid Art KW - Sound Art KW - Audio-visual Art Y1 - 2004 ER - TY - JOUR A1 - Beverungen, Daniel A1 - Eggert, Mathias A1 - Voigt, Matthias A1 - Rosemann, Michael T1 - Augmenting Analytical CRM Strategies with Social BI JF - International Journal of Business Intelligence Research (IJBIR) Y1 - 2013 U6 - http://dx.doi.org/10.4018/ijbir.2013070103 SN - 1947-3591 VL - 4 IS - 3 SP - 32 EP - 49 PB - IGI Global CY - Hershey ER - TY - JOUR A1 - Pietsch, Wolfram T1 - Augmenting voice of the customer analysis by analysis of belief JF - QFD-Forum Y1 - 2015 SN - 1431-6951 IS - 30 SP - 1 EP - 5 ER - TY - JOUR A1 - Schwabedal, Justus T. C. A1 - Sippel, Daniel A1 - Brandt, Moritz D. A1 - Bialonski, Stephan T1 - Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning N2 - Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle. Y1 - 2018 U6 - http://dx.doi.org/10.48550/arXiv.1809.08443 ER -