Suppression of superconductivity in nanowires by bulk superconductors

Transport measurements were made on a system consisting of a zinc nanowire array sandwiched between two bulk superconducting electrodes (Sn or In). It was found that the superconductivity of Zn nanowires of 40 nm diameter is suppressed either completely or partially by the superconducting electrodes. When the electrodes are driven into their normal state by a magnetic field, the nanowires switch back to their superconducting state. This phenomenon is significantly weakened when one of the two superconducting electrodes is replaced by a normal metal. The phenomenon is not seen in wires with diameters equal to and thicker than 70 nm.Comment: 4 pages, 5 figure

Towards on-chip time-resolved thermal mapping with micro-/nanosensor arrays

In recent years, thin-film thermocouple (TFTC) array emerged as a versatile candidate in micro-/nanoscale local temperature sensing for its high resolution, passive working mode, and easy fabrication. However, some key issues need to be taken into consideration before real instrumentation and industrial applications of TFTC array. In this work, we will demonstrate that TFTC array can be highly scalable from micrometers to nanometers and that there are potential applications of TFTC array in integrated circuits, including time-resolvable two-dimensional thermal mapping and tracing the heat source of a device. Some potential problems and relevant solutions from a view of industrial applications will be discussed in terms of material selection, multiplexer reading, pattern designing, and cold-junction compensation. We show that the TFTC array is a powerful tool for research fields such as chip thermal management, lab-on-a-chip, and other novel electrical, optical, or thermal devices

Current sustainability and electromigration of Pd, Sc and Y thin-films as potential interconnects

The progress on novel interconnects for carbon nanotube (CNT)-based electronic circuit is by far behind the remarkable development of CNT-field effect transistors. The Cu interconnect material used in current integrated circuits seems not applicable for the novel interconnects, as it requires electrochemical deposition followed by chemical-mechanical polishing. We report our experimental results on the failure current density, resistivity, electromigration effect and failure mechanism of patterned stripes of Pd, Sc and Y thin-films, regarding them as the potential novel interconnects. The Pd stripes have a failure current density of (8 similar to 10)x10(6) A/cm(2) (MA/cm(2)), and they are stable when the working current density is as much as 90% of the failure current density. However, they show a resistivity around 210 mu O.cm, which is 20 times of the bulk value and leaving room for improvement. Compared to Pd, the Sc stripes have a similar resistivity but smaller failure current density of 4 similar to 5 MA/cm(2). Y stripes seem not suitable for interconnects by showing even lower failure current density than that of Sc and evidence of oxidation. For comparison, Au stripes of the same dimensions show a failure current density of 30 MA/cm(2) and a resistivity around 4 mu O.cm, making them also a good material as novel interconnects.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000208414400008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Nanoscience & NanotechnologyMaterials Science, MultidisciplinaryPhysics, AppliedSCI(E)2ARTICLE3184-189

A065: Analysis of the Prediction of Adolescent Depressive Diseases and the Mediating Effect of Exercise Factors Based on Basic Census Data

Objective: By analyzing census databases, we aimed to understand the correlation between depression and other psychological and physiological diseases. Further, we expanded our research to the correlation with physiology, lifestyle habits, and other aspects. At the same time, we attempted to use artificial intelligence methods to predict the risk of depression, obtain relevant models, and derive depression. Methods: Data from census databases were used in this research. Traditional data analysis methods were used for basic quantitative analysis, and highly correlated data with depression was selected for prediction model building. Results: (1) Analysis showed a significant correlation between depression and other diseases such as anxiety and hyperactivity disorder (=0.535, 0.231, 0.284; p \u3c 0.01). Besides, physiological and life-related issues may become the cause of depression. The prediction model established through big data can better predict and detect depressive tendencies. (2) There was a positive correlation between adolescent living standards and depressive psychology (r \u3e 0.85, r \u3e 0.87). Exercise levels and dietary levels were mediating variables between adolescent living standards and depressive psychology, with the mediating effect accounting for 33.6% of the total effect. Conclusion: Physiological trauma, personality traits, and lifestyle habits of close relationships among adolescents may become factors that trigger psychological disorders in adolescents. Teenagers\u27 exercise and dietary levels play a mediating role between their living standards and depressive psychology

In situ epitaxial MgB2 thin films for superconducting electronics

A thin film technology compatible with multilayer device fabrication is critical for exploring the potential of the 39-K superconductor magnesium diboride for superconducting electronics. Using a Hybrid Physical-Chemical Vapor Deposition (HPCVD) process, it is shown that the high Mg vapor pressure necessary to keep the MgB$_2$ phase thermodynamically stable can be achieved for the {\it in situ} growth of MgB$_2$ thin films. The films grow epitaxially on (0001) sapphire and (0001) 4H-SiC substrates and show a bulk-like $T_c$ of 39 K, a $J_c$(4.2K) of $1.2 \times 10^7$ A/cm$^2$ in zero field, and a $H_{c2}(0)$ of 29.2 T in parallel magnetic field. The surface is smooth with a root-mean-square roughness of 2.5 nm for MgB$_2$ films on SiC. This deposition method opens tremendous opportunities for superconducting electronics using MgB$_2$

A Blockchain-Empowered Cluster-based Federated Learning Model for Blade Icing Estimation on IoT-enabled Wind Turbine

Wind energy is a fast-growing renewable energy but faces the blade icing. Data-driven methods provide talented solutions for blade icing detection but a considerable amount of data need to be collected to a central server, which may lead to the leakage of sensitive business data. To address this limitation, this work proposes BLADE, a Blockchain-empowered imbalanced federated learning (FL) model for blade icing detection. With the help of Blockchain, the conventional FL is improved without worrying the failure of the single centralized server and boosts the privacy-preserving. A validation mechanism is introduced into the Blockchain to enhance the defense of poisoning attacks. In addition, a novel imbalanced learning algorithm is integrated into BLADE to solve the class-imbalance problem in the sensor data. The BLADE is evaluated on the 10 wind turbines from two wind farms. The experimental results verify the effectiveness, superiority, and feasibility of proposed BLADE.acceptedVersio