Geförderte OA-Publikationen

Identifying an effective electrolyte is a primary challenge for hybrid ion capacitors, due to the intricacy of dual-ion storage. Here, this study demonstrates that the electrochemical behavior of graphite oxide in ether-solvent electrolyte outperforms those in ester-solvent electrolytes for the cathode of potassium-ion hybrid capacitor. The experimental and theoretical assessments verify that the anion and cation are isolated effectively in dimethyl ether, endowing a weaker interaction between cations and anions compared to that of ester-solvent electrolytes, which facilitates the dual-ion diffusion and thus enhances the electrochemical performance. This result provides a rational strategy to realize high-rate cations and anions storage on the carbon cathode. Furthermore, a new low-cost and high-performance capacitor prototype, modified graphite oxide (MGO) cathode versus pristine graphite (PG) in ether-solvent electrolyte (MGOǁDMEǁPG), is proposed. It exhibits a high energy density of 150 Wh kg^−1cathode at a high power density of 21443 W kg^−1cathode (calculation based on total mass: 60 Wh kg^−1 at 8577 W kg^−1).

The threshold for the onset of thermocapillary flow in a planar liquid layer heated from below is increased by a vertical magnetic field when the liquid is a good electric conductor. The magnetic damping effect is reduced when the induced eddy currents are blocked by insulating side walls. Neutral conditions for this specific Bénard-Marangoni stability problem with a vertical field and side walls are obtained numerically for three-dimensional perturbations assumed periodic in one horizontal direction. The domain is bounded by a free-slip wall at the bottom, a free surface at the top and two free-slip lateral walls in the other horizontal direction. Buoyancy forces and surface deformations are neglected and a constant heat flux is imposed on the free surface. Upon increasing the magnetic induction, the least stable modes become localized near the side walls and the convective threshold increases at a lower rate than for the least stable bulk mode.

With more than 50% of the world’s population living in urban areas, the smart city concept has been introduced as a solution to urbanization problems, with a focus on technological and social innovation. However, critics argue that the concept is more about marketing than actual benefits for citizens. Given the limitations of conventional and formalized e-participation and smart city procedures, we highlight the value of shared citizen knowledge and the potential of e-interaction in this context by analyzing city-related informal social media communication, following recent calls to embrace citizens’ opinions in the smart city framework. This work focuses on major German cities with more than 100,000 inhabitants. The authors identify nine categories of interest in citizens’ discussions. Unlike official channels, citizens tend to focus on social and societal issues. The results of this study can complement existing tools by including citizens’ perspectives in smart city decision-making processes.

The present study explores the relation between media use and knowledge in the context of the energy transition. To identify relevant knowledge categories, we relied on the expertise of an interdisciplinary research team. Based on this expertise, we identified awareness-knowledge of changes in the energy system and principles-knowledge of hydrogen as important knowledge categories. With data obtained from a nationwide online survey of the German-speaking population (n = 2,025) conducted in August 2021, we examined the level of knowledge concerning both categories in the German population. Furthermore, we studied its associations with exposure to journalistic media and direct communication from non-media actors (e.g., scientists). Our results revealed a considerable lack of knowledge for both categories. Considering the media variables, we found only weak, and in some cases even negative, relations with the use of journalistic media or other actors that spread information online. However, we found comparably strong associations between both knowledge categories and the control variables of sex, education, and personal interest. We use these results to open up a general discussion of the role of the media in knowledge acquisition processes.

Deep learning is increasingly being proposed for detecting neurological and psychiatric diseases from electroencephalogram (EEG) data but the method is prone to inadvertently incorporate biases from training data and exploit illegitimate patterns. The recent demonstration that deep learning can detect the sex from EEG implies potential sex-related biases in deep learning-based disease detectors for the many diseases with unequal prevalence between males and females. In this work, we present the male- and female-typical patterns used by a convolutional neural network that detects the sex from clinical EEG (81% accuracy in a separate test set with 142 patients). We considered neural sources, anatomical differences, and non-neural artifacts as sources of differences in the EEG curves. Using EEGs from 1140 patients, we found electrocardiac artifacts to be leaking into the supposedly brain activity-based classifiers. Nevertheless, the sex remained detectable after rejecting heart-related and other artifacts. In the cleaned data, EEG topographies were critical to detect the sex, but waveforms and frequencies were not. None of the traditional frequency bands was particularly important for sex detection. We were able to determine the sex even from EEGs with shuffled time points and therewith completely destroyed waveforms. Researchers should consider neural and non-neural sources as potential origins of sex differences in their data, they should maintain best practices of artifact rejection, even when datasets are large, and they should test their classifiers for sex biases.