Geförderte OA-Publikationen

Reactive multilayer systems are nanostructures of great interest for various technological applications because of their high energy release rate during the self-propagating reaction of their components. Therefore, many efforts are aimed at controlling the propagation velocity of these reactions. Herein, reactive multilayer systems of Al/Ni in the shape of free-standing foils with a wavelike surface morphology prepared by using sacrificial substrates with well-aligned waves are presented and the propagation of the reaction along different directions of the reproduced waves is analyzed. During the ignition test, the propagation front is recorded with a high-speed camera, and the maximum temperature is measured using a pyrometer. The propagation of the reaction is favored in the direction of the waves, which points out the influence of the anisotropy generated by this morphology and how it affects the propagation dynamics and the resulting microstructure. Furthermore, compared to their counterparts fabricated on flat substrates, these reactive multilayers with wavelike morphology exhibit a remarkable reduction in the propagation velocity of the reaction of about 50%, without significantly affecting the maximum temperature registered during the reaction.

Ultralow expansion (ULE) glasses are of special interest for temperature stabilized systems for example in precision metrology. Nowadays, ULE materials are mainly used in macroscopic and less in micromechanical systems. Reasons for this are a lack of technologies for parallel fabricating high-quality released microstructures with a high accuracy. As a result, there is a high demand in transferring these materials into miniaturized application examples, realistic system modeling, and the investigation of microscopic material properties. Herein, a technological base for fabricating released micromechanical structures and systems with a structure height above 100 μm in ULE 7972 glass is established. Herein, the main fabrication parameters that are important for the system design and contribute thus to the introduction of titanium silicate as material for glass-based micromechanical systems are discussed. To study the mechanical properties in combination with respective simulation models, microcantilevers are used as basic mechanical elements to evaluate technological parameters and other impact factors. The implemented models allow to predict the micromechanical system properties with a deviation of only ±5% and can thus effectively support the micromechanical system design in an early stage of development.

This paper describes the experimental investigation and numerical simulation of a novel electromechanical milling principle: the direct transformation of energy into the movement of milling beads with special magnetic properties. The experimental results show that this principle is ideally suited for the finest grinding of organic agents. Anthraquinone particles with a median size of 25.5 µm were electromechanically ground to 1 µm and the magnetic field strength in the process chamber has the greatest influence on milling results. The developed model reveals that the distribution of the time- and location-dependent vector gradient of the magnetic field in the process chamber determines the energy transfer from the exciter systems to the milling beads and hence the grinding results. With a suitable characterization of the vector gradient distribution, it is possible to establish a correlation between the vector gradient and specific milling beads power. This correlation is fundamental for the design of electromechanical milling machines.

As an emerging country, Indonesia is facing many environmental problems, with some of the most critical being plastic waste, severe deforestation, and climate change. Under such conditions, communication science plays an important role in pointing to the best way to inform the public so as to stimulate engagement and action to solve these problems. In this article, a systematic literature review of papers on environmental communication published in three leading communication journals in Indonesia was conducted. The findings show that despite the severe environmental problems in Indonesia, a limited number of studies on environmental communication have been published, and only a few methods and designs have been used. Therefore, more attention from communication scholars and intellectuals in Indonesia is needed to address environmental problems in their research. Creating an environmental communication division in existing communication associations is proposed as a practical solution, among others, and is discussed in the outlook section of this study

Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks.