264 research outputs found
Electro-catalytic degradation of sulfisoxazole by using graphene anode
No full textGraphite and graphene electrodes were prepared by using pure graphite as precursor. The electrode materials were characterized by a scanning electron microscope (SEM), X-ray diffraction (XRD) and cyclic voltammetry (CV) measurements. The electro-catalytic activity for degradation of sulfisoxazole (SIZ) was investigated by using prepared graphene or graphite anode. The results showed that the degradation of SIZ was much more rapid on the graphene than that on the graphite electrode. Moreover, the graphene electrode exhibited good stability and recyclability. The analysis on the intermediate products and the measurement of active species during the SIZ degradation demonstrated that indirect oxidation is the dominant mechanism, involving the electro-catalytic generation of UOH and O-2(-) as the main active oxygen species. This study implies that graphene is a promising potential electrode material for long-term application to electro-catalytic degradation of organic pollutants. (C) 2015 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V
Temperature-Induced Variations in Photocatalyst Properties and Photocatalytic Hydrogen Evolution: Differences in UV, Visible and Infrared Radiation
No full textIn this work, solar-heating-induced temperature-based photocatalytic hydrogen evolution reaction (PC-HER) of different photocatalysts (TiO₂ P25, g-C₃N₄, and their loaded Pt) was comprehensively studied and analyzed with the assistance of a series of temperature-based in situ characterizations. It was found that pristine TiO₂ P25 and g-C₃N₄ displayed enhanced PC-HER performances with increasing temperature (25–65 °C), while their loaded Pt nanoparticles (NPs) demonstrated a different behavior under ultraviolet (UV) or visible irradiation, presenting the highest hydrogen evolution rate at 35 °C. More interestingly, Pt NPs-g-C₃N₄ showed increasing PC-HER performances from 25 to 65 °C under visible light irradiation. Characterizations suggested that lowered electrical impedance, reduced band gap, increased light absorption, and elongated photoelectron lifetime with increased temperature are beneficial for improved PC-HER. However, agglomeration of Pt NPs significantly deteriorated the PC-HER performance at higher temperature and UV light can aggravate the thermal agglomeration of Pt NPs.Xiaojie Li, Jingkai Lin, Jiaquan Li, Huayang Zhang, Xiaoguang Duan, Hongqi Sun, Yingping Huang, Yanfen Fang, and Shaobin Wan
Carbon nitride-based Z-scheme heterojunctions for solar-driven advanced oxidation processes
No full textSolar-driven advanced oxidation processes (AOPs) via direct photodegradation or indirect photocatalytic activation of typical oxidants, such as hydrogen peroxide (H2O2), peroxymonosulfate (PMS), and peroxydisulfate (PDS), have been deemed to be an efficient technology for wastewater remediation. Artificial Z-scheme structured materials represent a promising class of photocatalysts due to their spatially separated charge carriers and strong redox abilities. Herein, we summarize the development of metal-free graphitic carbon nitride (g-C3N4, CN)-based direct and indirect Z-scheme photocatalysts for solar-driven AOPs in removing organic pollutants from water. In the work, the classification of AOPs, definition and validation of Z-schemes are summarized firstly. The innovative engineering strategies (e.g., morphology and dimensionality control, element doping, defect engineering, cocatalyst loading, and tandem Z-scheme construction) over CN-based direct Z-scheme structure are then examined. Rational design of indirect CN-based Z-scheme systems using different charge mediators, such as solid conductive materials and soluble ion pairs, is further discussed. Through examining the relationship be- tween the Z-scheme structure and activity (charge transfer and separation, light absorption, and reaction kinetics), we aim to provide more insights into the construction strategies and structure modification on CN-based Z-schemes towards improving their catalytic performances in AOPs. Lastly, limitations, challenges, and perspectives on future development in this emerging field are proposed.Jingkai Lin, Wenjie Tian, Huayang Zhang, Xiaoguang Duan, Hongqi Sun, Hao Wang, Yanfen Fang, Yingping Huang, Shaobin Wan
Binocular image sequence analysis : integration of stereo disparity and optic flow for improved obstacle detection and tracking
No full textBinocular vision systems have been widely used for detecting obstacles in advanced driver assistant systems (ADASs). These systems normally utilise disparity information extracted from left and right image pairs, but ignore the optic flows able to be extracted from the two image sequences. In fact, integration of these two methods may generate some distinct benefits. This paper proposes two algorithms for integrating stereovision and motion analysis for improving object detection and tracking. The basic idea is to fully make use of information extracted from stereo image sequence pairs captured from a stereovision rig. The first algorithm is to impose the optic flows as extra constraints for stereo matching. The second algorithm is to use a Kalman filter as a mixer to combine the distance measurement and the motion displacement measurement for object tracking. The experimental results demonstrate that the proposed methods are effective for improving the quality of stereo matching and three-dimensional object tracking
A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera
No full textVisual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera’s 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg–Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade–Lucas–Tomasi (KLT) algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method
Online Multiple Object Tracking Using Spatial Pyramid Pooling Hashing and Image Retrieval for Autonomous Driving
No full textMultiple object tracking (MOT) is a fundamental issue and has attracted considerable attention in the autonomous driving community. This paper presents a novel MOT framework for autonomous driving. The framework consists of two stages of object representation and data association. In the stage of object representation, we employ appearance, motion, and position features to characterize objects. We design a spatial pyramidal pooling hash network (SPPHNet) to generate the appearance features. Multiple-level representative features in the SPPHNet are mapped into a similarity-preserving binary space, called hash features. The hash features retain the visual discriminability of high-dimensional features and are beneficial for computational efficiency. For data association, a two-tier data association scheme is designed to address the occlusion issue, consisting of an affinity cost model and a hash-based image retrieval model. The affinity cost model accommodates the hash features, disparity, and optical flow as the first tier of data association. The hash-based image retrieval model exploits the hash features and adopts image retrieval technology to handle reappearing objects as the second tier of data association. Experiments on the KITTI public benchmark dataset and our campus scenario sequences show that our method has superior tracking performance to the state-of-the-art vision-based MOT methods
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