(E)-N′-(2,5-Dimethoxy­benzyl­idene)-2-(8-quinol­yloxy)acetohydrazide methanol solvate

The two mol­ecules in the asymmetric unit of the title compound, C20H19N3O4·CH4O, are paired via O—H⋯(O,N), N—H⋯O, and C—H⋯O hydrogen bonds. The mol­ecular skeleton of the acetohydrazide mol­ecule is close to planar; the benzene and quinoline mean planes form a dihedral angle of 3.9 (3)°. The crystal packing exhibits weak inter­molecular C—H⋯O hydrogen bonds and π–π inter­actions, indicated by short distances of 3.668 (3) Å, between the centroids of N-containing six-membered rings from neighbouring acetohydrazide mol­ecules

9-(4-Bromo­but­yl)-9H-carbazole

In the title compound, C16H16BrN, the bromo­butyl group lies on one side of the carbazole ring plane and has a zigzag shape. The dihedral angle between the two benzene rings is 0.55°. In the crystal, mol­ecules are connected by van der Waals inter­actions

Flexible, Free-Standing and Holey Graphene Paper for High-Power Supercapacitors

Flexible supercapacitors based on bendable electrodes have aroused much interest for integration in clothing materials and portable electronic devices. However, simultaneous achievement of high areal energy and high power densities still presents a great challenge. Herein we report the fabrication of free-standing, flexible graphene papers suitable for high-performance flexible supercapacitors. The binder-free graphene paper is made up of two types of holey graphene units (i.e., wrinkled graphene sheets and graphene nanoscrolls) that produce closely interconnected, porous 3D graphene architectures. The graphene papers reported here can be fabricated with a variety of thicknesses and areal densities, in the 10–70 μm and 1–5 mg cm−2 ranges, respectively. They exhibit a remarkable electrochemical performance in aqueous electrolytes: a) a high cell areal capacitance (230–190 mF cm−2 in H2SO4 and 180–170 mF cm−2 in Li2SO4), b) an outstanding capacitance retention of 60 % at ultra-large current densities of 1200 mA cm−2, c) an excellent long-term cycling stability and d) high areal power (≈280 mW cm−2) and energy densities (≈32 and ≈60 μWh cm−2 in H2SO4 and Li2SO4, respectively). These highly flexible graphene papers show a great improvement, in terms of areal energy-power densities, in relation to the state-of-the-art graphene-based film electrodes.This research work was supported by the FICYT Regional Project (GRUPIN14- 102), Spanish MINECO (CTQ2015-63552-R) and Fondo Europeo de Desarrollo Regional (FEDER). G. A. F. thanks the MINECO for his predoctoral contract.Peer reviewe

SR-POD : sample rotation based on principal-axis orientation distribution for data augmentation in deep object detection

Convolutional neural networks (CNNs) have outperformed most state-of-the-art methods in object detection. However, CNNs suffer the difficulty of detecting objects with rotation, because the dataset used to train the CCNs often does not contain sufficient samples with various angles of orientation. In this paper, we propose a novel data-augmentation approach to handle samples with rotation, which utilizes the distribution of the object's orientation without the time-consuming process of rotating the sample images. Firstly, we present an orientation descriptor, named as "principal-axis orientation" to describe the orientation of the object's principal axis in an image and estimate the distribution of objects’ principal-axis orientations (PODs) of the whole dataset. Secondly, we define a similarity metric to calculate the POD similarity between the training set and an additional dataset, which is built by randomly selecting images from the benchmark ImageNet ILSVRC2012 dataset. Finally, we optimize a cost function to obtain an optimal rotation angle, which indicates the highest POD similarity between the two aforementioned data sets. In order to evaluate our data augmentation method for object detection, experiments, conducted on the benchmark PASCAL VOC2007 dataset, show that with the training set augmented using our method, the average precision (AP) of the Faster RCNN in the TV-monitor is improved by 7.5%. In addition, our experimental results also demonstrate that new samples generated by random rotation are more likely to result in poor performance of object detection