Enhanced photoresponse in MoTe2 photodetectors with asymmetric graphene contacts

Atomically thin two dimensional (2D) materials are promising candidates for miniaturized high-performance optoelectronic devices. Here, we report on multilayer MoTe2 photodetectors contacted with asymmetric electrodes based on n- and p-type graphene layers. The asymmetry in the graphene contacts creates a large (Ebi ~100 kV cm-1) built-in electric field across the short (l = 15 nm) MoTe2 channel, causing a high and broad (? = 400 to 1400 nm) photoresponse even without any externally applied voltage. Spatially resolved photovoltage maps reveal an enhanced photoresponse and larger built-in electric field in regions of the MoTe2 layer between the two graphene contacts. Furthermore, a fast (~10 ?s) photoresponse is achieved in both the photovoltaic and photoconductive operation modes of the junction. Our findings could be extended to other 2D materials and offer prospects for the implementation of asymmetric graphene contacts in future low-power optoelectronic applications

Interlayer Band-to-Band Tunneling and Negative Differential Resistance in van der Waals BP/InSe Field-Effect Transistors

© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Atomically thin layers of van der Waals (vdW) crystals offer an ideal material platform to realize tunnel field-effect transistors (TFETs) that exploit the tunneling of charge carriers across the forbidden gap of a vdW heterojunction. This type of device requires a precise energy band alignment of the different layers of the junction to optimize the tunnel current. Among 2D vdW materials, black phosphorus (BP) and indium selenide (InSe) have a Brillouin zone-centered conduction and valence bands, and a type II band offset, both ideally suited for band-to-band tunneling. TFETs based on BP/InSe heterojunctions with diverse electrical transport characteristics are demonstrated: forward rectifying, Zener tunneling, and backward rectifying characteristics are realized in BP/InSe junctions with different thickness of the BP layer or by electrostatic gating of the junction. Electrostatic gating yields a large on/off current ratio of up to 108 and negative differential resistance at low applied voltages (V ≈ 0.2 V). These findings illustrate versatile functionalities of TFETs based on BP and InSe, offering opportunities for applications of these 2D materials beyond the device architectures reported in the current literature

Nitrogen Removal Characteristics of a Cold-Tolerant Aerobic Denitrification Bacterium, <i>Pseudomonas</i> sp. 41

Nitrogen pollution of surface water is the main cause of water eutrophication, and is considered a worldwide challenge in surface water treatment. Currently, the total nitrogen (TN) content in the effluent of wastewater treatment plants (WWTPs) is still high at low winter temperatures, mainly as a result of the incomplete removal of nitrate (NO3−-N). In this research, a novel aerobic denitrifier identified as Pseudomonas sp. 41 was isolated from municipal activated sludge; this strain could rapidly degrade a high concentration of NO3−-N at low temperature. Strain 41 completely converted 100 mg/L NO3−-N in 48 h at 15 °C, and the maximum removal rate reached 4.0 mg/L/h. The functional genes napA, nirS, norB and nosZ were successfully amplified, which provided a theoretical support for the aerobic denitrification capacity of strain 41. In particular, the results of denitrification experiments showed that strain 41 could perform aerobic denitrification under the catalysis of NAP. Nitrogen balance analysis revealed that strain 41 degraded NO3−-N mainly through assimilation (52.35%) and aerobic denitrification (44.02%), and combined with the gene amplification results, the nitrate metabolism pathway of strain 41 was proposed. Single-factor experiments confirmed that strain 41 possessed the best nitrogen removal performance under the conditions of sodium citrate as carbon source, C/N ratio 10, pH 8, temperature 15–30 °C and rotation speed 120 rpm. Meanwhile, the bioaugmentation test manifested that the immobilized strain 41 remarkably improved the denitrification efficiency and shortened the reaction time in the treatment of synthetic wastewater

A Deep Convolutional Generative Adversarial Networks-Based Method for Defect Detection in Small Sample Industrial Parts Images

Online defect detection in small industrial parts is of paramount importance for building closed loop intelligent manufacturing systems. However, high-efficiency and high-precision detection of surface defects in these manufacturing systems is a difficult task and poses a major research challenge. The small sample size of industrial parts available for training machine learning algorithms and the low accuracy of computer vision-based inspection algorithms are the bottlenecks that restrict the development of efficient online defect detection technology. To address these issues, we propose a small sample gear face defect detection method based on a Deep Convolutional Generative Adversarial Network (DCGAN) and a lightweight Convolutional Neural Network (CNN) in this paper. Initially, we perform data augmentation by using DCGAN and traditional data enhancement methods which effectively increase the size of the training data. In the next stage, we perform defect classification by using a lightweight CNN model which is based on the state-of-the-art Vgg11 network. We introduce the Leaky ReLU activation function and a dropout layer in the proposed CNN. In the experimental evaluation, the proposed framework achieves a high score of 98.40%, which is better than that of the classic Vgg11 network model. The method proposed in this paper is helpful for the detection of defects in industrial parts when the available sample size for training is small

Study on Runoff and Infiltration for Expansive Soil Slopes in Simulated Rainfall

In order to understand the hydrological process of expansive soil slopes, simulated rainfall experiments were conducted to study the effects of slope gradient and initial soil moisture content on runoff and infiltration for expansive soil slopes located in south China. The field program consisted of four neighboring slopes (70%, 47%, 32%, and 21%) instrumented by a runoff collection system and moisture content sensors (EC-5). Results from the monitored tests indicate that there was delay in the response of surface runoff. The runoff initiation time decreased with initial soil water content and increasing slope gradient. After the generation of runoff, the cumulative runoff per unit area and the runoff rate increased linearly and logarithmically with time, respectively. The greater the initial soil moisture content was, the smaller the influence of slope gradient on runoff. A rainfall may contribute from 39% to about 100% of its total rainfall as infiltration, indicating that infiltration remained an important component of the rainwater falling on the slope, despite the high initial soil water content. The larger the initial sealing degree of slope surface was the smaller the cumulative infiltration per unit area of the slope. However, the soil moisture reaction was more obvious. The influence of inclination is no longer discernible at high initial moisture levels. The greater the initial soil moisture content and the smaller the slope gradient, the weaker was the change of soil water content caused by simulated rainfall. The influence of initial soil moisture content and slope gradient on the processes of flow and changes of soil water content identified in this study may be helpful in the surface water control for expansive soil slopes

A Deep Convolutional Generative Adversarial Networks-Based Method for Defect Detection in Small Sample Industrial Parts Images

Online defect detection in small industrial parts is of paramount importance for building closed loop intelligent manufacturing systems. However, high-efficiency and high-precision detection of surface defects in these manufacturing systems is a difficult task and poses a major research challenge. The small sample size of industrial parts available for training machine learning algorithms and the low accuracy of computer vision-based inspection algorithms are the bottlenecks that restrict the development of efficient online defect detection technology. To address these issues, we propose a small sample gear face defect detection method based on a Deep Convolutional Generative Adversarial Network (DCGAN) and a lightweight Convolutional Neural Network (CNN) in this paper. Initially, we perform data augmentation by using DCGAN and traditional data enhancement methods which effectively increase the size of the training data. In the next stage, we perform defect classification by using a lightweight CNN model which is based on the state-of-the-art Vgg11 network. We introduce the Leaky ReLU activation function and a dropout layer in the proposed CNN. In the experimental evaluation, the proposed framework achieves a high score of 98.40%, which is better than that of the classic Vgg11 network model. The method proposed in this paper is helpful for the detection of defects in industrial parts when the available sample size for training is small

Unraveling the Role of Endothelial Dysfunction in Osteonecrosis of the Femoral Head: A Pathway to New Therapies

Osteonecrosis of the femoral head (ONFH) is a disabling disease characterized by the disruption of the blood supply to the femoral head, leading to the apoptosis and necrosis of bone cells and subsequent joint collapse. Total hip arthroplasty is not optimal since most patients are young. Multiple risk factors contribute to osteonecrosis, including glucocorticoid (GC) usage, excessive alcohol intake, hypercholesterolemia, and smoking. Continuous stimulation by many variables causes a chronic inflammatory milieu, with clinical repercussions including endothelial dysfunction, leading to thrombosis, coagulopathy, and poor angiogenesis. Immune cells are the primary regulators of inflammation. Innate and adaptive immune cells interact with endothelial cells to hinder the regeneration and repair of bone lesions. An in-depth examination of the pathological drivers of ONFH reveals that endothelial dysfunction may be a major cause of osteonecrosis. Understanding the involvement of endothelial dysfunction in the chronic inflammation of osteonecrosis could aid in the development of possible therapies. This review summarizes the role of endothelial cells in osteonecrosis and further explains the pathophysiological mechanism of endothelial dysfunction in this disease from the perspective of inflammation to provide new ideas for the treatment of osteonecrosis