Erscheinungsjahr 2023

A deeper understanding of interfaces comes after the rapid development of nano-hybrids. Atomic interfaces with atomic-level thickness, intimate bonds, inferior charge-transport resistance, and robust stability have received escalating interest in the field of photocatalysis. Taking into account the fact that the carrier dynamics and spectrum response of candidate photocatalysts like chalcogenides remain suffering, sustained efforts are devoted. Hybridization, which is accompanied by interface designing, behaves as a supportive strategy to enlarge the photocatalytic output. Hence, the comprehensive survey for recent empirical studies on atomic interfaces in chalcogenides is highly desirable. Precisely, the fundamental of atomic interfaces, the devised approaches to design atomic interfaces in chalcogenides and their feasible roles for maneuvering photocatalysis, and the auxiliary advanced characterization are enumerated and summarized. The multifarious interaction of structure, chemical environment, optical and electric properties, and photocatalytic performance in chalcogenides with atomic interfaces is highlighted. Meanwhile, perspectives of atomic interfaces benefiting photocatalysis are given with a summary, and outlooks related to controllable architecture, nucleation mechanism, calculation, and the correlation between atomic interfaces and amended photocatalysis are presented discreetly. Herein, the review is meant to provide the first systematic account of designing atomic interfaces in chalcogenides served for ultimate photocatalytic applications.

Two-dimensional (2D) materials catalysts provide an atomic-scale view on a fascinating arena for understanding the mechanism of electrocatalytic carbon dioxide reduction (CO2 ECR). Here, we successfully exfoliated both layered and nonlayered ultra-thin metal phosphorous trichalcogenides (MPCh3) nanosheets via wet grinding exfoliation (WGE), and systematically investigated the mechanism of MPCh3 as catalysts for CO2 ECR. Unlike the layered CoPS3 and NiPS3 nanosheets, the active Sn atoms tend to be exposed on the surfaces of nonlayered SnPS3 nanosheets. Correspondingly, the nonlayered SnPS3 nanosheets exhibit clearly improved catalytic activity, showing formic acid selectivity up to 31.6 % with -7.51 mA cm^-2 at -0.65 V vs. RHE. The enhanced catalytic performance can be attributed to the formation of HCOO* via the first proton-electron pair addition on the SnPS3 surface. These results provide a new avenue to understand the novel CO2 ECR mechanism of Sn-based and MPCh3-based catalysts.

The cognitive performance of the crew has a major impact on mission safety and success in space flight. Monitoring of cognitive performance during long-duration space flight therefore is of paramount importance and can be performed using compact state-of-the-art mobile EEG. However, signal quality of EEG may be compromised due to the vicinity to various electronic devices and constant movements. We compare noise characteristics between in-flight extraterrestrial microgravity and ground-level terrestrial electroencephalography (EEG) recordings. EEG data recordings from either aboard International Space Station (ISS) or on earth’s surface, utilizing three EEG amplifiers and two electrode types, were compared. In-flight recordings showed noise level of an order of magnitude lower when compared to pre- and post-flight ground-level recordings with the same EEG system. Noise levels between ground-level recordings with actively shielded cables, and in-flight recordings without shielded cables, were similar. Furthermore, noise level characteristics of shielded ground-level EEG recordings, using wet and dry electrodes, and in-flight EEG recordings were similar. Actively shielded mobile dry EEG systems will support neuroscientific research and neurocognitive monitoring during spaceflight, especially during long-duration space missions.

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.

Brake-wear particle emissions are the result of the components of a friction brake being in tribological contact, and they are classified as non-exhaust emissions. Since most of the emitted particles belong to the size classes of particulate matter (≤10 μm) and differ significantly in terms of their physico-chemical properties from automotive exhaust emissions, this source is of particular relevance to human health and, therefore, the focus of scientific studies. Previous studies have shown that coated brake discs offer significant wear and emission reduction potential. Nevertheless, no studies are available that describe the specific particle formation process, the contact conditions, the structure of the friction layer and the differences compared to conventional grey cast iron discs. The aim of this study is to describe those differences. For this purpose, the tribological behaviour, the structure of the friction layer and the associated particle dynamics within the friction contact between a laser cladding coated disc and a conventional grey cast iron disc are compared. The required investigations are carried out both ex situ (stationary) and in situ (dynamic). Parallel to the tribological investigations, the particle emission behaviour is determined on an inertia dynamometer using a constant volume sampling system (CVS) and equipment for particle number and particle size distribution measurement. The results show that, for two different brake pads, the laser cladding brake disc has lower wear and less particulate emissions than the grey cast iron brake disc. The wear behaviour of the coating varies significantly for the two brake pads. By contrast, the grey cast iron brake disc shows a significantly lower influence.