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| 1 | Synergy of first-and second-sphere interactions in a covalent organic framework boosts highly selective platinum uptake显示文摘Platinum recovery from waste electrical and electronic equipment(WEEE) in highly acidic solutions is significant to the electronics industry and environmental remediation. However, the lack of ingenious design and synergetic coordination gives rise to unsatisfied PtCl_(4)^(2-)extraction capacities and selectivities in most previously reported adsorbents(e.g., polymeric and inorganic materials). Herein, we proposed a synergistic strategy that realizes highly selective PtCl_(4)^(2-)uptake through first-and second-sphere coordinations. The proof-of-concept imine-linked covalent organic framework(SCU-COF-3) was found to chelate Pt Cl42-via the direct N…Pt coordination and the synergistically interlaminar N–H…Cl hydrogen bonds, which was disclosed by the comprehensive analysis of extended X-ray adsorption fine structure(EXAFS) characterizations and density functional theory(DFT) calculations. The unique adsorption mechanism imparts a superior adsorption capacity(168.4 mg g-1)and extraordinary Pt(II) selectivity to SCU-COF-3 under static conditions. In addition, SCU-COF-3 exhibits an upgraded distribution coefficient of 1.62 × 10^(5)mL· g^(-1), one order of magnitude higher than those of reported natural adsorbents. Specifically, SCU-COF-3 can extract PtCl_(4)^(2- )quantitatively from a simulated acidic waste solution coexisting with other 12 competitive ions, suggesting its promising application in practical scenarios. | Linwei He Baoyu Li Zhonglin Ma Lixi Chen Shicheng Gong Mingxing Zhang Yaoyao Bai Qi Guo Fuqi Wu Fuqiang Zhao Jie Li Duo Zhang Daopeng Sheng Xing Dai Long Chen Jie Shu Zhifang Chai Shuao Wang | 2023 | Science China Chemistry2023，66，3： | 0 |
| 2 | Identification of Key Links in Electric Power Operation Based-Spatiotemporal Mixing Convolution Neural Network显示文摘As the scale of the power system continues to expand,the environment for power operations becomes more and more complex.Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately.Therefore,more reliable and accurate security control methods are urgently needed.In order to improve the accuracy and reliability of the operation risk management and control method,this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal hybrid convolutional neural network.To provide early warning and control of targeted risks,first,the video stream is framed adaptively according to the pixel changes in the video stream.Then,the optimized MobileNet is used to extract the feature map of the video stream,which contains both time-series and static spatial scene information.The feature maps are combined and non-linearly mapped to realize the identification of dynamic operating scenes.Finally,training samples and test samples are produced by using the whole process image of a power company in Xinjiang as a case study,and the proposed algorithm is compared with the unimproved MobileNet.The experimental results demonstrated that the method proposed in this paper can accurately identify the type and start and end time of each operation link in the whole process of electric power operation,and has good real-time performance.The average accuracy of the algorithm can reach 87.8%,and the frame rate is 61 frames/s,which is of great significance for improving the reliability and accuracy of security control methods. | Lei Feng Bo Wang Fuqi Ma Hengrui Ma Mohamed AMohamed | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 3 | State Evaluation Based on Feature Identification of Measurement Data:for Resilient Power System显示文摘Resilient power systems urgently need real-time evaluation of their operational states.By mining the characteristics of the grid operational data and mapping them to the operational state,this paper proposes a method to evaluate the real-time state and the evolution direction of power systems.First,the state evaluation matrix is constructed using nodal voltages.Then,from the data-driven perspective,the grid state is embodied in the operational data change.Furthermore,four indicators are proposed to characterize the power grid statefrom inherent physical and operating characteristics perspectives.Finally,through the simulations of a real power grid in China,it is shown that the method proposed in this paper can adequately characterize the power grid state,and is robust against bad data. | Hongxia Wang Bo Wang Peng Luo Fuqi Ma Yinyu Zhou Mohamed A.Mohamed | 2022 | CSEE Journal of Power and Energy Systems2022，8，4： | 0 |
| 4 | Skeleton-based Violation Action Recognition Method for Safety Supervision in Operation Field of Distribution Network Based on Graph Convolutional Network显示文摘Safety accidents in the operation field of the distribution network often occur,which seriously endanger the safety and lives of operators.Existing identification methods for safety risk can identify static safety risks,such as no-helmet,no-safety gloves,etc.,but fail to identify risks in the dynamic actions of operators.Therefore,this paper proposes a skeletonbased violation action-recognition method for supervision of safety during operations in a distribution network,i.e.,based on spatial temporal graph convolutional network(STGCN)and key joint attention module(KJAM),which can implement dynamic violation behavior recognition of operators.In this method,the human posture estimation method,i.e.Multi-Person Pose Estimation,is utilized to extract the skeleton information of operators during operations,and to construct an undirected graph,which reflects the movement and posture of the human body.Then,the STGCN is utilized to identify actions of operators that can lead to dynamic violations.In addition,the KJAM captures important joint information of operators.The effectiveness and superiority of the proposed method are verified in comparison to other action recognition methods.The experimental results show that the proposed method has higher recognition accuracy for common violations collected at the actual operation site of the distribution network and shows a strong generalization ability,which can be applied to the video monitoring system of field operations to reduce the occurrence of safety accidents. | Bo Wang Fuqi Ma Rong Jia Peng Luo Xuzhu Dong | 2023 | CSEE Journal of Power and Energy Systems2023，9，6： | 0 |
| 5 | Galangin probably ameliorates hyperuricemia by inhibiting urate acid transport 1 (URAT1): Homology modeling and mechanism exploration显示文摘Urate acid transporter 1(URAT1)is the main transporter of uric acid reabsorption,which closely related to the pathogenesis of hyperuricemia.Screening URAT1 inhibitors and studying their possible metabolic processes is a hot spot in the development of uric acid-lowering drugs.Studies have shown that many food-borne plant polyphenols have uric acid lowering activity with non-toxic side eff ects,and can be used to improve and alleviate hyperuricemia.In this study,we take galangin(GAL)as an example to explore the mechanism of plant polyphenols aff ecting hyperuricemia by inhibiting URAT1.Homology modeling was used to construct a three-dimensional model of URAT1 protein,and the structure was optimized.Ramachandran diagram was used to verify the rationality of model protein structure.A known URAT1 inhibitor,benzbromarone(BBR),was used to dock with URAT1 to determine the docking site and show the key amino acids.GAL and model protein were docked by molecular docking method to analyze their interaction.Meanwhile,comparing the interaction of BBR and GAL with the key amino acids of model proteins,the binding of GAL was more stable,suggesting that GAL could aff ects hyperuricemia by inhibiting URAT1.This paper aims to provide theoretical guidance for the development of new functional food ingredients for lowering uric acid. | Aijinxiu Ma Fuqi Wang Xu Zhao | 2022 | Journal of Polyphenols2022，4，1： | 0 |
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