Layer-structured, single phase Bi5Fe0.5Co0.5Ti3O15 ceramics was synthesized following a multicalcination procedure. Magnetic moment increases more than three times by substituting half Fe sites by Co ions. The material exhibits an Aurivillius phase with a four-layer unit cell structure, and presents a remarkable coexistence of ferroelectricity and ferromagnetism above room temperature. The measured 2Pr and 2Mr are 13 μC/cm2 and 7.8 memu/g, respectively. The material’s magnetic behavior below 275 °C is relaxationlike and its magnetic Curie temperature is ∼345 °C.

Image restoration, including image denoising, super resolution, inpainting, and so on, is a well-studied problem in computer vision and image processing, as well as a test bed for low-level image modeling algorithms. In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers. In other words, the network is composed of multiple layers of convolution and de-convolution operators, learning end-to-end mappings from corrupted images to the original ones. The convolutional layers capture the abstraction of image contents while eliminating corruptions. Deconvolutional layers have the capability to upsample the feature maps and recover the image details. To deal with the problem that deeper networks tend to be more difficult to train, we propose to symmetrically link convolutional and deconvolutional layers with skip-layer connections, with which the training converges much faster and attains better results. The skip connections from convolutional layers to their mirrored corresponding deconvolutional layers exhibit two main advantages. First, they allow the signal to be back-propagated to bottom layers directly, and thus tackles the problem of gradient vanishing, making training deep networks easier and achieving restoration performance gains consequently. Second, these skip connections pass image details from convolutional layers to deconvolutional layers, which is beneficial in recovering the clean image. Significantly, with the large capacity, we show it is possible to cope with different levels of corruptions using a single model. Using the same framework, we train models on tasks of image denoising, super resolution removing JPEG compression artifacts, non-blind image deblurring and image inpainting. Our experiment results on benchmark datasets show that our network can achieve best reported performance on all of the four tasks, and set new state-of-the-art.

In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution. The network is composed of multiple layers of convolution and de-convolution operators, learning end-to-end mappings from corrupted images to the original ones. The convolutional layers act as the feature extractor, which capture the abstraction of image contents while eliminating noises/corruptions. De-convolutional layers are then used to recover the image details. We propose to symmetrically link convolutional and de-convolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum. First, The skip connections allow the signal to be back-propagated to bottom layers directly, and thus tackles the problem of gradient vanishing, making training deep networks easier and achieving restoration performance gains consequently. Second, these skip connections pass image details from convolutional layers to de-convolutional layers, which is beneficial in recovering the original image. Significantly, with the large capacity, we can handle different levels of noises using a single model. Experimental results show that our network achieves better performance than all previously reported state-of-the-art methods.

Crosslinked ethylene-vinyl acetate (EVA) copolymers with VA content of 28% by weight were prepared by a two-step method by evenly dispersing the crosslinking agent (dicumyl peroxide) into the EVA matrix and then crosslinking at elevated temperatures. The crosslinking features of the samples were analyzed by Soxhlet extraction with xylene and dynamic mechanical measurements. All the samples were crystalline at room temperature, and the chemical crosslinks seemed to have little effect on the melting behavior of polyethylene segment crystals in the EVA copolymers. The shape recovery results indicated that only those specimens that had a sufficiently high crosslinking degree (gel content higher than about 30%) were able to show the typical shape memory effect, a large recoverable strain, and a high final recovery rate. The degree of crosslinking can be influenced by the amount of the peroxide and the time and temperature of the reaction. The response temperature of the recovery effect (about 61°C) was related to the melting point of the samples. The EVA shape memory polymer was characterized by its low recovery speed that resulted from the wide melting range of the polyethylene segment crystals.

Multiferroic properties of four-layered Bi4.25La0.75Fe0.5Co0.5Ti3O15 ceramics were carefully investigated. X-ray diffraction and high resolution transmission electron microscopy analyses indicate that the as-prepared sample is almost free from secondary phases, and magnetization measurements confirm a ferromagnetic transition ∼483 K. At room temperature (RT), the sample shows a typical ferromagnetism with a remnant magnetization (2Mr) of ∼51.2 m emu/g, and a good ferroelectric hysteresis with a remnant polarization (2Pr) of ∼15.4 μC/cm2. More importantly, an obvious magneto-dielectric (MD) effect has been found under a low magnetic field of 1 T at RT with a maximum of magneto-dielectric constant of ∼10.5%.

No abstract

The structure and properties of a polypropylene microporous film prepared by biaxial stretching of non‐porous polypropylene film of high β‐crystal content were investigated. The porosity of these films can be as high as 30–40%, and the average pore size was around 0.05 μm. The films were found to have the structure of a two‐phase interpenetrating network; both the polypropylene and the pore regions were three‐dimensionally continuous. The advantages of the biaxially stretched microporous films are the combination of high permeability to fluids with good mechanical properties and almost circular pore shape with narrow pore size distribution. The application of this microporous film for battery separators, filtration membranes and substrates of functional polymer composites is discussed.

No abstract