26 research outputs found

    Excitons at the B K edge of boron nitride nanotubes probed by x-ray absorption spectroscopy

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    We have performed a near-edge x-ray absorption fine-structure (NEXAFS) investigation of multi-walled boron nitride nanotubes (BNNTs). We show that the one-dimensionality of BNNTs is clearly evident in the B K edge spectrum, while the N K edge spectrum is similar to that of layered hexagonal BN (h-BN). We observe a sharp feature at the Ã* onset of the B K edge, which we ascribe to a core exciton state. We also report a comparison with spectra taken after an ammonia plasma treatment, showing that the B K edge becomes indistinguishable from that of h-BN, due to the breaking of the tubular order and the formation of small h-BN clusters

    Fabrication of Eutectic Ga-In Nanowire Arrays Based on Plateau–Rayleigh Instability

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    We have developed a simple method of fabricating liquid metal nanowire (NW) arrays of eutectic GaIn (EGaIn). When an EGaIn droplet anchored on a flat substrate is pulled perpendicular to the substrate surface at room temperature, an hourglass shaped EGaIn is formed. At the neck of the shape, based on the Plateau–Rayleigh instability, the EGaIn bridge with periodically varying thicknesses is formed. Finally, the bridge is broken down by additional pulling. Then, EGaIn NW is formed at the surface of the breakpoint. In addition, EGaIn NW arrays are found to be fabricated by pulling multiple EGaIn droplets on a substrate simultaneously. The average diameter of the obtained NW was approximately 0.6 μm and the length of the NW depended on the amount of droplet anchored on the substrate. The EGaIn NWs fabricated in this study may be used for three-dimensional wiring for integrated circuits, the tips of scanning probe microscopes, and field electron emission arrays

    Light-Driven Flying Balloons Based on Hybrids of Carbon Nanotubes and Cellulose Nanofibers

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    We have fabricated nanocarbon-based palm-sized cubic paper balloons that can be levitated by light irradiation. These paper balloons are composed of carbon nanotube (CNT) freestanding films and cellulose nanofiber (CNF) freestanding films. The number of CNT freestanding films (NCNT) and the number of CNF freestanding films (6-NCNT) among the six walls of the cube were varied. We investigated the effect of NCNT on the levitation behaviors under light irradiation. We found that the balloons were levitated when NCNT was greater than or equal to two. The levitation height was found to be increased by increasing NCNT

    Freestanding Translucent ZnO–Cellulose Nanocomposite Films for Ultraviolet Sensor Applications

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    The rapidly advancing technology of wearable and miniaturized electronics has increased the demand for low-cost high-performance flexible sensors. Herein, the preparation of translucent freestanding films consisting of cellulose nanofibers (CNFs) and ZnO nanoparticles (NPs) via a simple spray coating method is presented. The obtained nanocomposite films were thin (~10 µm) and flexible. The scanning electron microscopy and atomic force microscopy analysis revealed that the nanocomposite film was composed of regions of ZnO NP-modified CNFs and regions of aggregation of ZnO NPs with each other. The electrical conductance of the films was rapidly increased beyond 40 wt.% ZnO and reached up to >50 nA at 60 wt.% ZnO. This was attributed to the increased number of conductive paths formed by the ZnO NPs in the nanocomposite film when a certain threshold was crossed. The ZnO–CNF nanocomposite film exhibited a stable response over on/off cycles of UV light exposure. The responsivity and sensitivity of the nanocomposite film with 60 wt.% ZnO were 36.5 mA/W and 247, respectively. Even when the device was curved (radius of curvature: 3 mm), the response and sensitivity remained high. The developed nanocomposite films are expected to be applied as environmentally friendly flexible UV sensors

    A hybrid of iterative Gauss–Newton and one-dimensional convolutional neural network for high-resolution electrical impedance tomography

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    We developed a processing method using benefits of both iterative Gauss–Newton (IGN) and a one-dimensional convolutional neural network (1D-CNN) for high-resolution electrical impedance tomography. The proposed method logically combines conductivity images reconstructed by different methods. The accuracies of the mathematical IGN method, 1D-CNN method, and the proposed method were compared. Utilizing the ideal potential data obtained through simulations, along with the experimental potential data derived from cement samples, we reconstruct the conductivity distribution. When utilizing the simulation data, the IGN method produces larger errors in the reconstructed images as the size of the foreign object decreases. The proposed method reconstructs the position and size more accurately than the IGN and 1D-CNN methods. When utilizing the experimental data, 1D-CNN and proposed methods were more accurate in terms of the position and size than the IGN method
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