Composites containing 25 vol% Ag were compressed at room temperature to over 110% at 850/spl deg/C in air. Measurement of the strain rate sensitivity yielded a value of 0.5, characteristic of superplastic deformation. As deformed materials had sub-micron grain size and significant c-axis texture parallel to the pressing direction. TEM examination showed that the grains were highly defected and that the grain boundaries were clean. The T/sub c/ was however low with an onset of 50 K and a width of /spl sim/10 K. Annealing studies were carried out with an aim to "fully oxygenate" the material and anneal out a minimal number of defects to obtain higher transition temperatures, at the same time retaining a significant defect density for enhanced flux-pinning. Magnetization measurements were performed after most anneals in order to evaluate intragranular and intergranular properties. Results indicate the presence of unusually high J/sub c/'s at low temperatures after the final anneal (T/sub c/ onset /spl sim/90/spl deg/K). The observations may be explained by highly superior intragranular properties coupled with increased local current loop size.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Crystalline nanobelts of ZnO and SnO2 were prepared from a thermal evaporation of oxide powders inside an alumina tube in the absence of catalysts. Typical dimensions of the nanobelt samples ranged from approximately 10 to 100 microns in length, 30 to 300 nm in width, and 6 to 30 nm in thickness. Room temperature Raman spectra were obtained on pressed mats of nanobelt samples and compared with the corresponding spectra of the starting oxide powders and bulk materials. Collectively, our Raman data indicated that the as-prepared nanobelt samples used in this study were oxygen deficient. Upon annealing at 900 degrees C in flowing oxygen for 1 h, the nanobelt samples exhibited Raman features that corresponded to those expected in respective bulk semiconducting oxides. The dimensions of the nanobelts were a bit too large to expect significant quantum size effects on the phonon structure similar to those observed in carbon nanotubes and short-period semiconductor superlattices.
Abstract Water is crucial for various physicochemical processes at the liquid–solid interfaces. In particular, the interfacial water, mediating the electric field and solvation effect along with the solid, corporately determine the electrochemical properties. Understanding the interaction between solid properties and the interface water holds significant importance in interfacial dynamics. However, the impact of alterations in the charged state of solid surfaces induced by contact electrification on interfacial water remains unknown. Here, the evolution of atomic‐level resolution maps of hydration layers are reported on charged surfaces using 3D atomic force microscopy (3D‐AFM). These findings demonstrate that electrostatic interactions can reinforce, distort, or collapse the characteristic structure of hydration layers. More importantly, these interactions exhibit interlayer differences and sample specificity in hydration layer structures of different substrates. In addition, similar oscillations of the hydration layer are observed at the electrochemical interface under different voltage biases. This suggests that contact‐electrification has the potential to serve as a novel method for manipulating and regulating chemical reactions at the interface.
Semiconducting oxides and sophides have attracted considerable attention in scientific research and technological applications.In this paper, by thermal evaporating a mixture of ZnO and SnO 2 , self-assembled nanowire-nanoribbon junction arrays of ZnO have been synthesized (Fig. 1) [1].The growth is dominated by the vapor-liquid-solid (VLS) mechanism and Sn particles reduced from SnO 2 serve as the catalyst for the growth.The axial nanowires (the "rattans") are the result of fast growth along [0001], and the surrounding "tadpole-like" nanoribbons are the growth along 〈10-10〉 .An isotropic growth along six 〈10-10〉 results in the ordered radial distribution of the nanoribbons around the axial nanowire.The junction arrays of ZnO structures reported here are likely to have ultra-high surface sensitivity due to the unique structure, and they are a candidate for building sensors with ultra-high sensitivity.
A nonclassical light source is essential for implementing a wide range of quantum information processing protocols, including quantum computing, networking, communication, and metrology. In the microwave regime, propagating photonic qubits that transfer quantum information between multiple superconducting quantum chips serve as building blocks of large-scale quantum computers. In this context, spectral control of propagating single photons is crucial for interfacing different quantum nodes with varied frequencies and bandwidth. Here we demonstrate a deterministic microwave quantum light source based on superconducting quantum circuits that can generate propagating single photons, time-bin encoded photonic qubits and qudits. In particular, the frequency of the emitted photons can be tuned in situ as large as 200 MHz. Even though the internal quantum efficiency of the light source is sensitive to the working frequency, we show that the fidelity of the propagating photonic qubit can be well preserved with the time-bin encoding scheme. Our work thus demonstrates a versatile approach to realizing a practical quantum light source for future distributed quantum computing.
ABSTRACT Gliomas are the most common primary brain tumors within the central nervous system, typically observed through magnetic resonance imaging (MRI). Precise segmentation of brain tumor in MRI is highly significant for both clinical diagnosis and treatment. However, due to complexity of tumor structures, existing deep‐learning‐based methods for brain tumor segmentation still face challenges in accurately delineating tumor core (TC) and enhancing tumor (ET) regions, which are primary targets for actual treatment. To address this problem, this work proposes dual‐path fusion attention‐based UNet (DPFA‐UNet) that leverages a dual‐path attention block (DPA) and a concurrent attention fusion block (CAF) within a U‐shaped architecture. Specifically, DPA enhances adaptability to lesions of varying sizes by using multi‐scale branches that capture fine details and global features. CAF fuses high‐ and low‐level semantic features using a parallel attention mechanism, effectively focusing on the focal regions. It also incorporates a mask generated by deep supervision mechanism to further guide feature fusion. Additionally, to reduce demand for hardware resources, we incorporate depthwise separable convolution into the model. Experiments are conducted on public BraTS 2021 and BraTS 2019 datasets. The results verify that DPFA‐UNet outperforms existing brain tumor segmentation methods, particularly in segmenting TC and ET regions.
