Flexible transparent graphene laminates via direct lamination of graphene onto polyethylene naphthalate substrates.

Graphene, with its excellent electrical, mechanical, and optical properties, has emerged as an exceptional material for flexible and transparent nanoelectronics. Such versatility makes it compelling to find new pathways to lay graphene sheets onto smooth, flexible substrates to create large-scale flexible transparent graphene conductors. Here, we report the realization of flexible transparent graphene laminates by direct adhesion of chemical vapor deposition (CVD) graphene on a polyethylene naphthalate (PEN) substrate, which is an emerging standard for flexible electronics. By systematically optimizing the conditions of a hot-press technique, we have identified that applying optimum temperature and pressure can make graphene directly adhere to flexible PEN substrates without any intermediate layer. The resultant flexible graphene films are transparent, have a standard sheet resistance of 1 kΩ with high bending resilience, and high optical transmittance of 85%. Our direct hot-press method is achieved below the glass transition temperature of the PEN substrate. Furthermore, we demonstrate press-assisted embossing for patterned transfer of graphene, and hence it can serve as a reliable new means for creating universal, transparent conducting patterned films for designing flexible nanoelectronic and optoelectronic components.

Fight Recognition in video using Hough Forests and 2D Convolutional Neural Network.

While action recognition has become an important line of research in computer vision, the recognition of particular events such as aggressive behaviors, or fights, has been relatively less studied. These tasks may be extremely useful in several video surveillance scenarios such as psychiatric wards, prisons or even in personal camera smartphones. Their potential usability has led to a surge of interest in developing fight or violence detectors. One of the key aspects in this case is efficiency, that is, these methods should be computationally fast. "Handcrafted" spatiotemporal features that account for both motion and appearance information can achieve high accuracy rates, albeit the computational cost of extracting some of those features is still prohibitive for practical applications. The deep learning paradigm has been recently applied for the first time to this task too, in the form of a 3D Convolutional Neural Network that processes the whole video sequence as input. However, results in human perception of other's actions suggest that, in this specific task, motion features are crucial. This means that using the whole video as input may add both redundancy and noise in the learning process. In this work, we propose a hybrid "handcrafted/learned" feature framework which provides better accuracy than the previous feature learning method, with similar computational efficiency. The proposed method is compared to three related benchmark datasets. The method outperforms the different state-of-the-art methods in two of the three considered benchmark datasets.

Thickness-modulated tungsten-carbon superconducting nanostructures grown by focused ion beam induced deposition for vortex pinning up to high magnetic fields.

We report efficient vortex pinning in thickness-modulated tungsten-carbon-based (W-C) nanostructures grown by focused ion beam induced deposition (FIBID). By using FIBID, W-C superconducting films have been created with thickness modulation properties exhibiting periodicity from 60 to 140 nm, leading to a strong pinning potential for the vortex lattice. This produces local minima in the resistivity up to high magnetic fields (2.2 T) in a broad temperature range due to commensurability effects between the pinning potential and the vortex lattice. The results show that the combination of single-step FIBID fabrication of superconducting nanostructures with built-in artificial pinning landscapes and the small intrinsic random pinning potential of this material produces strong periodic pinning potentials, maximizing the opportunities for the investigation of fundamental aspects in vortex science under changing external stimuli (e.g., temperature, magnetic field, electrical current).