In Situ-Generated Reactive Oxygen Species in Precharged Titania and Tungsten Trioxide Composite Catalyst Membrane Filters: Application to As(III) Oxidation in the Absence of Irradiation

This study demonstrates that in situ-generated reactive oxygen species (ROSs) in prephotocharged TiO₂ and WO₃ (TW) composite particle-embedded inorganic membrane filters oxidize arsenite (As(III)) into arsenate (As(V)) without any auxiliary chemical oxidants under ambient conditions in the dark. TW membrane filters have been charged with UV or simulated sunlight and subsequently transferred to a once-through flow-type system. The charged TW filters can transfer the stored electrons to dissolved O₂, producing ROSs that mediate As(III) oxidation in the dark. Dramatic inhibition of As(V) production with O₂ removal or addition of ROS quenchers indicates an ROS-mediated As(III) oxidation mechanism. Electron paramagnetic spectroscopic analysis has confirmed the formation of the HO₂•/O₂•– pair in the dark. The WO₃ fraction in the TW filter significantly influences the performance of the As(III) oxidation, while As(V) production is enhanced with increasing charging time and solution pH. The As(III) oxidation is terminated when the singly charged TW filter is fully discharged; however, recharging of TW recovers the catalytic activity for As(III) oxidation. The proposed oxidation process using charged TW membrane filters is practical and environmentally benign for the continuous treatment of As(III)-contaminated water during periods of unavailability of sunlight

In Situ-Generated Reactive Oxygen Species in Precharged Titania and Tungsten Trioxide Composite Catalyst Membrane Filters: Application to As(III) Oxidation in the Absence of Irradiation

This study demonstrates that in situ-generated reactive oxygen species (ROSs) in prephotocharged TiO₂ and WO₃ (TW) composite particle-embedded inorganic membrane filters oxidize arsenite (As(III)) into arsenate (As(V)) without any auxiliary chemical oxidants under ambient conditions in the dark. TW membrane filters have been charged with UV or simulated sunlight and subsequently transferred to a once-through flow-type system. The charged TW filters can transfer the stored electrons to dissolved O₂, producing ROSs that mediate As(III) oxidation in the dark. Dramatic inhibition of As(V) production with O₂ removal or addition of ROS quenchers indicates an ROS-mediated As(III) oxidation mechanism. Electron paramagnetic spectroscopic analysis has confirmed the formation of the HO₂•/O₂•– pair in the dark. The WO₃ fraction in the TW filter significantly influences the performance of the As(III) oxidation, while As(V) production is enhanced with increasing charging time and solution pH. The As(III) oxidation is terminated when the singly charged TW filter is fully discharged; however, recharging of TW recovers the catalytic activity for As(III) oxidation. The proposed oxidation process using charged TW membrane filters is practical and environmentally benign for the continuous treatment of As(III)-contaminated water during periods of unavailability of sunlight

Preparation of Activated Biochar-Supported Magnetite Composite for Adsorption of Polychlorinated Phenols from Aqueous Solutions

For this study, we applied activated biochar (AB) and its composition with magnetite (AB-Fe3O4) as adsorbents for the removal of polychlorophenols in model wastewater. We comprehensively characterized these adsorbents and performed adsorption tests under several experimental parameters. Using FTIR, we confirmed successful synthesis of AB-Fe3O4 composite through cetrimonium bromide surfactant. We conducted adsorption tests using AB and AB-Fe3O4 to treat model wastewater containing polychlorophenols, such as 2,3,4,6-Tetrachlorophenol (TeCP), 2,4,6-Trichlorophenol (TCP), and 2,4-Dichlorophenol (DCP). Results of the isotherm and the kinetic experiments were well adapted to Freundlich’s isotherm model and the pseudo-second-order kinetic model, respectively. Main adsorption mechanisms in this study were attributed to non-covalent, π-electron acceptor–donor interactions and hydrophobic interactions judging from the number of chloride elements in each chlorophenol and its hydrophobic characteristics. We also considered the electrostatic repulsion effect between TeCP and AB, because adsorption performance of TeCP at basic condition was slightly worse than at weak acidic condition. Lastly, AB-Fe3O4 showed high adsorption selectivity of TeCP compared to other persistent organic pollutants (i.e., bisphenol A and sulfamethoxazole) due to hydrophobic interactions. We concluded that AB-Fe3O4 may be used as novel adsorbent for wastewater treatment including toxic and hydrophobic organic pollutants (e.g., TeCP)

Per-Client Network Performance Isolation in VDE-based Cloud Computing Servers

Authors' final versionIn a cloud server where multiple virtual machines owned by different clients are cohosted, excessive traffic generated by a small group of clients may well jeopardize the quality of service of other clients. It is thus very important to provide per-client network performance isolation in a cloud computing environment. Unfortunately, the existing techniques are not effective enough for a huge cloud computing system since it is difficult to adopt them in a large scale and they often require non-trivial modification to the established network protocols. To overcome such difficulties, we propose per-client network performance isolation using VDE (Virtual Distributed Ethernet) as a base framework. Our approach begins with per-client weight specification and support client-aware fair share scheduling and packet dispatching for both incoming and outgoing traffic. It also provides hierarchical fairness between a client and its virtual machines. Our approach supports full virtualization of a guest OS, wide scale adoption, limited modification to the existing system, low run-time overhead and work-conserving servicing. Our experimental results show the effectiveness of the proposed approach. Every client received at least 99.4% of its bandwidth share as specified by its weight.OAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000004193/4SEQ:4PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000004193ADJUST_YN:NEMP_ID:A005174DEPT_CD:4541CITE_RATE:.175FILENAME:11-12-23 JISE-VDE.pdfDEPT_NM:전기·컴퓨터공학부EMAIL:[email protected]_YN:YCONFIRM:

Sustainability and Industry 4.0 in the packaging and printing industry: a diagnostic survey in Poland

Industry 4.0 (I4.0) became an important paradigm to bridge the gap between technologies and humans. The paper aims to diagnose sustainability performance and I4.0 maturity in Poland’s printing and packaging sector and identify research areas where further actions for improvements are necessary. This article adopts a mixed-method study combining in-depth interviews of eleven heterogeneous enterprises, supported with a quantitative survey on a representative sample of 301 companies. The findings revealed an insignificant correlation from a statistical point of view (0.44) between the adopted I4.0 technologies currently used and sustainable best practices. Internet of Things technologies are more often adopted in the printing industry (27.2 %) than in the packaging industry (14 %). The study concludes that using I4.0 technologies boosts the execution of sustainable practices and/or realising sustainable development practices requires I4.0 technology adoption. The paper clarifies that more in-depth analyses are needed to help achieve sustainable objectives for printing and packaging companies through digital technologies. The methodology is replicable and might be applied in other economies across separate multinational enterprises to influence sustainable digitalised business strategy

Image-to-Image Retrieval by Learning Similarity between Scene Graphs

As a scene graph compactly summarizes the high-level content of an image in a structured and symbolic manner, the similarity between scene graphs of two images reflects the relevance of their contents. Based on this idea, we propose a novel approach for image-to-image retrieval using scene graph similarity measured by graph neural networks. In our approach, graph neural networks are trained to predict the proxy image relevance measure, computed from human-annotated captions using a pre-trained sentence similarity model. We collect and publish the dataset for image relevance measured by human annotators to evaluate retrieval algorithms. The collected dataset shows that our method agrees well with the human perception of image similarity than other competitive baselines.Comment: Accepted to AAAI 202