We herein provide an effective method to fabricate a transparent superamphiphobic coating with superhydrophobicity and near-superoleophobicity, the finished coating also shows improved stability under various measurements. To do this, a transparent superhydrophobic coating was first prepared with polydimethylsiloxane (PDMS) and hydrophobic silicon dioxide (SiO 2 ) nanoparticles. Then the coating was sintered to degrade the PDMS into SiO 2 before it was further oxidized into silanol (Si-OH). Finally, the coating was treated with 1H, 1H, 2H, 2H-Perfluorooctyl-trichlorosilane (PFTS). The PFTS treated coating shows transparency, superhydrophobicity with a water contact angle of 152.7 AE 2.1 and near-superoleophobicity with a diiodomethane contact angle of 140.7 AE 3.2 . The droplets of water and diiodomethane can simultaneously slide off the surface with a sliding angle of less than 6 . Moreover, the PFTS treated coating shows a higher stability than the PDMS/SiO 2 coating fabricated by spin coating under various environmental conditions. The PFTS treated coating also shows quite good stability under high temperature environment. The superamphiphobic properties, transparency and improved stability of the PFTS treated coating are systemically discussed and the results show that the finished coating may be appropriate for many outdoor applications.
Abstract. This paper is concerned with the inhomogeneous nonlinear Shrö-dinger equation (INLS-equation)In the critical and supercritical cases p ≥ 4/N, with N ≥ 2, it is shown here that standing-wave solutions of (INLS-equation) on H 1 (R N ) perturbation are nonlinearly unstable or unstable by blow-up under certain conditions on the potential term V with a small > 0.
Recent advance on signal processing has witnessed increasing interest in machine learning. In this paper, we revisit the problem of direction-of-arrival (DOA) estimation for colocated multiple-input multiple-output (MIMO) radar from the perspective of machine learning. The reduced-complexity transformation is first applied on the array data from matched filters, thus eliminating the redundancy of the array data for the relief of calculational burden. Furthermore, the pre-whitening is followed to obtain a simplified noise model. Finally, the DOA estimation is linked to off-grid sparse Bayesian learning (OGSBL), which does not require to update the noise hyper-parameter, and a block hyper-parameter is utilized to accelerate the convergence of the OGSBL algorithm. The proposed estimator provides better DOA estimation accuracy than the existing peak searching algorithm. The effectiveness of the proposed algorithm is verified via numerical simulation. INDEX TERMS Array signal processing, MIMO radar, DOA estimation, off-grid, sparse Bayesian learning.
In this article, polypropylene (PP)/multiwall carbon nanotubes (MWNTs) composites were prepared through dynamic packing injection molding, in which the oscillatory shear was exerted on the molten composite during packing and solidification stage of injection-molding. A simultaneous increase of tensile strength and impact strength has been achieved for PP/MWNTs composites containing only 0.6 wt % MWNTs. Particularly, the impact strength was found increased by almost 50% at such low MWNTs content. These improvements in properties were attributed to uniform dispersion and possible orientation of nanotube induced by shear stress. It was suggested that the dynamic packing injection molding could provide much strong shear force for better dispersion of MWNTs in PP matrix, on one hand, but breakdown the aspect ratio of MWNTs, on the other.
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