7,730 research outputs found
Boron Nitride Nanosheets for Metal Protection
Although the high impermeability of graphene makes it an excellent barrier to
inhibit metal oxidation and corrosion, graphene can form a galvanic cell with
the underlying metal that promotes corrosion of the metal in the long term.
Boron nitride (BN) nanosheets which have a similar impermeability could be a
better choice as protective barrier, because they are more thermally and
chemically stable than graphene and, more importantly, do not cause galvanic
corrosion due to their electrical insulation. In this study, the performance of
commercially available BN nanosheets grown by chemical vapor deposition as a
protective coating on metal has been investigated. The heating of the copper
foil covered with the BN nanosheet at 250 {\deg}C in air over 100 h results in
dramatically less oxidation than the bare copper foil heated for 2 h under the
same conditions. The electrochemical analyses reveal that the BN nanosheet
coating can increase open circuit potential and possibly reduce oxidation of
the underlying copper foil in sodium chloride solution. These results indicate
that BN nanosheets are a good candidate for oxidation and corrosion protection,
although conductive atomic force microscopy analyses show that the
effectiveness of the protection relies on the quality of BN nanosheets.Comment: With Supporting Informatio
Atomically Thin Boron Nitride: Unique Properties and Applications
Atomically thin boron nitride (BN) is an important two-dimensional (2D)
nanomaterial, with many properties distinct from graphene. In this feature
article, these unique properties and associated applications often not possible
from graphene are outlined. The article starts with characterization and
identification of atomically thin BN. It is followed by demonstrating their
strong oxidation resistance at high temperatures and applications in protecting
metals from oxidation and corrosion. As flat insulators, BN nanosheets are
ideal dielectric substrates for surface enhanced Raman spectroscopy (SERS) and
electronic devices based on 2D heterostructures. The light emission of BN
nanosheets in the deep ultraviolet (DUV) and ultraviolet (UV) regions are also
included for its scientific and technological importance. The last part is
dedicated to synthesis, characterization, and optical properties of BN
nanoribbons, a special form of nanosheets
(2-Amino-5-chloroÂbenzeneÂsulfonato)bisÂ(triphenylÂphosphine)silver(I)
The asymmetric unit of the title mononuclear compound, [Ag(C6H5ClNO3S)(C18H15P)2], contains four independent molÂecules. In each of the molÂecules, the AgI cation is three-coordinated by two triphenylÂphosphine ligands, and one N atom from a 2-amino-5-chloroÂbenzeneÂsulfonate anion. The molÂecules are linked into a one-dimensional supraÂmolecular structure by N—Hâ‹ŻO hydrogen bonds
Protecting entanglement from correlated amplitude damping channel using weak measurement and quantum measurement reversal
Based on the quantum technique of weak measurement, we propose a scheme to
protect the entanglement from correlated amplitude damping decoherence. In
contrast to the results of memoryless amplitude damping channel, we show that
the memory effects play a significant role in the suppression of entanglement
sudden death and protection of entanglement under severe decoherence. Moreover,
we find that the initial entanglement could be drastically amplified by the
combination of weak measurement and quantum measurement reversal even under the
correlated amplitude damping channel. The underlying mechanism can be
attributed to the probabilistic nature of weak measurements.Comment: 11 pages, 5 figures, accepted by Quantum Information Processin
Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks
The identification of pulmonary lobes is of great importance in disease
diagnosis and treatment. A few lung diseases have regional disorders at lobar
level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this
work, we propose an automated segmentation of pulmonary lobes using
coordination-guided deep neural networks from chest CT images. We first employ
an automated lung segmentation to extract the lung area from CT image, then
exploit volumetric convolutional neural network (V-net) for segmenting the
pulmonary lobes. To reduce the misclassification of different lobes, we
therefore adopt coordination-guided convolutional layers (CoordConvs) that
generate additional feature maps of the positional information of pulmonary
lobes. The proposed model is trained and evaluated on a few publicly available
datasets and has achieved the state-of-the-art accuracy with a mean Dice
coefficient index of 0.947 0.044.Comment: ISBI 2019 (Oral
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