Beijing University of Technology

About: Beijing University of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Microstructure & Computer science. The organization has 31929 authors who have published 31987 publications receiving 352112 citations. The organization is also known as: Běijīng Gōngyè Dàxué & Beijing Polytechnic University.

Three-dimensional nanoporous Fe2O3/Fe3C-graphene heterogeneous thin films for lithium-ion batteries

Abstract: Three-dimensional self-organized nanoporous thin films integrated into a heterogeneous Fe2O3/Fe3C-graphene structure were fabricated using chemical vapor deposition. Few-layer graphene coated on the nanoporous thin film was used as a conductive passivation layer, and Fe3C was introduced to improve capacity retention and stability of the nanoporous layer. A possible interfacial lithium storage effect was anticipated to provide additional charge storage in the electrode. These nanoporous layers, when used as an anode in lithium-ion batteries, deliver greatly enhanced cyclability and rate capacity compared with pristine Fe2O3: a specific capacity of 356 μAh cm(-2) μm(-1) (3560 mAh cm(-3) or ∼1118 mAh g(-1)) obtained at a discharge current density of 50 μA cm(-2) (∼0.17 C) with 88% retention after 100 cycles and 165 μAh cm(-2) μm(-1) (1650 mAh cm(-3) or ∼518 mAh g(-1)) obtained at a discharge current density of 1000 μA cm(-2) (∼6.6 C) for 1000 cycles were achieved. Meanwhile an energy density of 294 μWh cm(-2) μm(-1) (2.94 Wh cm(-3) or ∼924 Wh kg(-1)) and power density of 584 μW cm(-2) μm(-1) (5.84 W cm(-3) or ∼1834 W kg(-1)) were also obtained, which may make these thin film anodes promising as a power supply for micro- or even nanosized portable electronic devices.

Ligand-Induced Drastic Enhancement of Catalytic Activity of Nano-BiFeO3 for Oxidative Degradation of Bisphenol A

Abstract: Effects of chelating agents on the catalytic degradation of bisphenol A (BPA) was studied in the presence of BiFeO3 nanoparticles as a heterogeneous catalyst and H2O2 as a green oxidant. The oxidizing ability of H2O2 in the presence of nano-BiFeO3 alone was not so strong to degrade BPA at neutral pH values, due to the limited catalytic ability of nano-BiFeO3. Once the surface of nano-BiFeO3 was in situ modified by adding proper organic ligands, the BPA degradation was much accelerated in the pH range of 5–9. The enhancing effect of the ligand was observed to have an order of blank < tartaric acid < formic acid < glycine < nitrilotriacetic acid < ethylenediaminetetraacetic acid (EDTA). The addition of 0.25 mmol L–1 EDTA in the H2O2–BiFeO3 system at pH 5.0 and 30 °C increased the BPA removal from 20.4% to 91.2% with reaction time of 120 min. The enhancing effect of the ligand was found to be indifferent of the possible dissolution of iron from nano-BiFeO3, but correlated well with the accelerated •OH format...

Fe2O3/3DOM BiVO4: High-performance photocatalysts for the visible light-driven degradation of 4-nitrophenol

Abstract: The three-dimensionally ordered macroporous (3DOM) BiVO 4 and its supported iron oxide ( x Fe 2 O 3 /3DOM BiVO 4 , x = 0.18, 0.97, and 3.40 wt%) photocatalysts were prepared using the ascorbic acid-assisted polymethyl methacrylate-templating and incipient wetness impregnation methods, respectively. Physicochemical properties of the materials were characterized by means of numerous analytical techniques, and their photocatalytic activities were evaluated for the degradation of 4-nitrophenol under visible light illumination. It is found that the BiVO 4 possessed a high-quality 3DOM architecture with a monoclinic crystal phase, and the Fe 2 O 3 was highly dispersed on the surface of 3DOM BiVO 4 . The x Fe 2 O 3 /3DOM BiVO 4 samples much outperformed the 3DOM BiVO 4 sample, and 0.97Fe 2 O 3 /3DOM BiVO 4 showed the best photocatalytic performance (98% 4-nitrophenol was degraded in the presence of 0.6 mL H 2 O 2 within 30 min of visible light illumination) and excellent photocatalytic stability. The introduction of H 2 O 2 to the reaction system could promote the photodegradation of 4-nitrophenol by providing the active OH species generated via the reaction of photoinduced electrons and H 2 O 2 . The pseudo-first-order reaction rate constants (0.0876–0.1295 min −1 ) obtained over x Fe 2 O 3 /3DOM BiVO 4 were much higher than those (0.0033–0.0395 min −1 ) obtained over 3DOM or Bulk BiVO 4 and Fe 2 O 3 /Bulk BiVO 4 , with the 0.97Fe 2 O 3 /3DOM BiVO 4 sample exhibiting the highest rate constant. The enhanced photocatalytic performance of 0.97Fe 2 O 3 /3DOM BiVO 4 was associated with its unique porous architecture, high surface area, Fe 2 O 3 − BiVO 4 heterojunction, good light-harvesting ability, high adsorbed oxygen species concentration, and excellent separation efficiency of photogenerated electrons and holes as well as the photo-Fenton degradation process.

Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction

Abstract: Traffic prediction is a core problem in the intelligent transportation system and has broad applications in the transportation management and planning, and the main challenge of this field is how to efficiently explore the spatial and temporal information of traffic data. Recently, various deep learning methods, such as convolution neural network (CNN), have shown promising performance in traffic prediction. However, it samples traffic data in regular grids as the input of CNN, thus it destroys the spatial structure of the road network. In this paper, we introduce a graph network and propose an optimized graph convolution recurrent neural network for traffic prediction, in which the spatial information of the road network is represented as a graph. Additionally, distinguishing with most current methods using a simple and empirical spatial graph, the proposed method learns an optimized graph through a data-driven way in the training phase, which reveals the latent relationship among the road segments from the traffic data. Lastly, the proposed method is evaluated on three real-world case studies, and the experimental results show that the proposed method outperforms state-of-the-art traffic prediction methods.