Electric-field-induced strong enhancement of electroluminescence in multilayer molybdenum disulfide.

The layered transition metal dichalcogenides have attracted considerable interest for their unique electronic and optical properties. While the monolayer MoS2 exhibits a direct bandgap, the multilayer MoS2 is an indirect bandgap semiconductor and generally optically inactive. Here we report electric-field-induced strong electroluminescence in multilayer MoS2. We show that GaN-Al2O3-MoS2 and GaN-Al2O3-MoS2-Al2O3-graphene vertical heterojunctions can be created with excellent rectification behaviour. Electroluminescence studies demonstrate prominent direct bandgap excitonic emission in multilayer MoS2 over the entire vertical junction area. Importantly, the electroluminescence efficiency observed in multilayer MoS2 is comparable to or higher than that in monolayers. This strong electroluminescence can be attributed to electric-field-induced carrier redistribution from the lowest energy points (indirect bandgap) to higher energy points (direct bandgap) in k-space. The electric-field-induced electroluminescence is general for other layered materials including WSe2 and can open up a new pathway towards transition metal dichalcogenide-based optoelectronic devices

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

Causal inference permits us to discover covert relationships of various variables in time series. However, in most existing works, the variables mentioned above are the dimensions. The causality between dimensions could be cursory, which hinders the comprehension of the internal relationship and the benefit of the causal graph to the neural networks (NNs). In this paper, we find that causality exists not only outside but also inside the time series because it reflects a succession of events in the real world. It inspires us to seek the relationship between internal subsequences. However, the challenges are the hardship of discovering causality from subsequences and utilizing the causal natural structures to improve NNs. To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme. We evaluate the MCNS framework and impregnation NN with MCNS on time series classification tasks. Experimental results illustrate that our impregnation, by refining attention, shape selection classification, and pruning datasets, drives NN, even the data itself preferable accuracy and interpretability. Besides, MCNS provides an in-depth, solid summary of the time series and datasets.Comment: 9 pages, 6 figure

Non-destructive testing for carbon-fiber-reinforced plastic (CFRP) using a novel eddy current probe

Abstract(#br)Carbon-fiber-reinforced plastic (CFRP) is of low conductivity and has a layered structure. High-frequency transmitter-receiver (T-R) probes are widely chosen to inspect CFRPs using eddy current testing (ECT). However, in these works, the variation in the distance between the probe and test sample can cause a larger signal than that caused by defects and may cover up the defect. The detection sensitivity was also reduced by random noise resulting from lift-off change. To address these issues, it is meaningful to design a probe which can overcome the effect of lift-off variation and meanwhile offer high sensitivity to defects in CFRPs. In this study, a T-R probe with a special structure for detection of CFRPs was developed. The probe contains an 8-shaped transmitter coil (TX coil) and a circular receiver coil (RX coil), which is placed on a line equidistant from the two parts of the transmitter coil. Theoretically, regardless of how the lift-off changes, the output signal is always 0 if the azimuth of the probe agrees with one of the fiber orientations of an intact CFRP. Experimental studies demonstrate that the proposed probe is insensitive to lift-off compared with a traditional T-R probe and offers high sensitivity to defects. For defect detection, in-plane waviness can be detected with the proposed probe. Quantitative experiments for crack detection were performed. The cracks were clearly visualized in the scanning images. The length and location of the cracks can also be estimated from the scanning images

Gate-induced insulator to band-like transport transition in organolead halide perovskite

Understanding the intrinsic charge transport in organolead halide perovskites is essential for the development of high-efficiency photovoltaics and other optoelectronic devices. Despite the rapid advancement of the organolead halide perovskite in photovoltaic and optoelectronic applications, the intrinsic charge carrier transport in these materials remains elusive partly due to the difficulty of fabricating electrical devices and obtaining good electrical contact. Here, we report the fabrication of organolead halide perovskite microplates with monolayer graphene as low barrier electrical contact. A systematic charge transport studies reveal an insulator to band-like transport transition. Our studies indicate that the insulator to band-like transport transition depends on the orthorhombic-to-tetragonal phase transition temperature and defect densities of the organolead halide perovskite microplates. Our findings are not only important for the fundamental understanding of charge transport behavior but also offer valuable practical implications for photovoltaics and optoelectronic applications based on the organolead halide perovskite.Comment: 18 pages, 5 figure

Contribution of Gray and White Matter Abnormalities to Cognitive Impairment in Multiple Sclerosis

Patients with multiple sclerosis (MS) commonly exhibit cognitive impairments (CI). However, the neural mechanisms underlying CI remain unclear. The current study applied diffusion tensor imaging (DTI) and voxel-based morphometric (VBM) magnetic resonance imaging (MRI) techniques to evaluate differences in white matter (WM) integrity and gray matter (GM) volume between MS patients with CI and MS patients with cognitive preservation (CP). Neuropsychological assessment and MRI were obtained from 39 relapsing-remitting MS (RRMS) patients and 29 healthy controls (HCs). Patients were classified as CI or CP according to cognitive ability, and demographic characteristics and MRI images were compared. Compared with HCs, MS patients exhibited widespread damage in WM integrity, and GM loss in several regions. Compared with CP patients, CI patients exhibited more extensive WM impairments, particularly in the corpus callosum, cerebellar peduncle, corona radiata, optic radiation, superior longitudinal fasciculus, anterior limb of the internal capsule, and cingulate, as well as decreased GM volume in the bilateral caudate, left insula and right temporal lobe. MS patients with CI exhibited more significant structural abnormalities than those with CP. Widespread impairments of WM integrity and selective GM atrophy both appear to be associated with impaired cognition in RRMS

MARTE/pCCSL: Modeling and Refining Stochastic Behaviors of CPSs with Probabilistic Logical Clocks

Best Paper AwardInternational audienceCyber-Physical Systems (CPSs) are networks of heterogeneous embedded systems immersed within a physical environment. Several ad-hoc frameworks and mathematical models have been studied to deal with challenging issues raised by CPSs. In this paper, we explore a more standard-based approach that relies on SysML/MARTE to capture different aspects of CPSs, including structure, behaviors, clock constraints, and non-functional properties. The novelty of our work lies in the use of logical clocks and MARTE/CCSL to drive and coordinate different models. Meanwhile, to capture stochastic behaviors of CPSs, we propose an extension of CCSL, called pCCSL, where logical clocks are adorned with stochastic properties. Possible variants are explored using Statistical Model Checking (SMC) via a transformation from the MARTE/pCCSL models into Stochastic Hybrid Automata. The whole process is illustrated through a case study of energy-aware building, in which the system is modeled by SysML/MARTE/pCCSL and different variants are explored through SMC to help expose the best alternative solutions