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Recent advances in electrochemical sensors for antibiotics and their applications 被引量:6
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作者 Qun Wang Qiang Xue +5 位作者 Tao Chen Jiawei Li Yuehua Liu xiaohan shan Fei Liu Jianbo Jia 《Chinese Chemical Letters》 SCIE CAS CSCD 2021年第2期609-619,共11页
The abuse of antibiotics will cause an increase of drug-resistant strains and environmental pollution,which in turn will affect human health.Therefore,it is important to develop effective detection techniques to deter... The abuse of antibiotics will cause an increase of drug-resistant strains and environmental pollution,which in turn will affect human health.Therefore,it is important to develop effective detection techniques to determine the level of antibiotics contamination in various fields.Compared with traditional detection methods,electrochemical sensors have received extensive attention due to their advantages such as high sensitivity,low detection limit,and good selectivity.In this mini review,we summarized the latest developments and new trends in electrochemical sensors for antibiotics.Here,modification methods and materials of electrode are discussed.We also pay more attention to the practical applications of antibiotics electrochemical sensors in different fields.In addition,the existing problems and the future challenges ahead have been proposed.We hope that this review can provide new ideas for the development of electrochemical sensors for antibiotics in the future. 展开更多
关键词 ANTIBIOTICS Electrochemical sensor ELECTRODES Modified materials APPLICATIONS
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Occurrences,spatial distributions,and ecological risks of organic ultraviolet absorbents in surface sediments from Taihu Lake,China 被引量:1
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作者 Wen Gu Shuai Sun +6 位作者 Yinying Cai Ziyan Du xiaohan shan Mengyuan Liang Min Guo Linjun Zhou Yong Zhang 《Emerging Contaminants》 2025年第2期53-62,共10页
The occurrences and spatial distributions of eight organic ultraviolet absorbents (UVAs) were investigated in surface sediments from Taihu Lake, China. The detection frequencies and concentrations of UVAs in 2016 were... The occurrences and spatial distributions of eight organic ultraviolet absorbents (UVAs) were investigated in surface sediments from Taihu Lake, China. The detection frequencies and concentrations of UVAs in 2016 were significantly higher than those in 2015.2-Ethylhexyl methoxycinnamate and homosalate (HMS) were the predominant UVAs in the sediment samples. The total UVA concentration range in sediment samples was 0.15–85.3 ng/g. The spatial distribution results showed that UVAs were concentrated in the northwest of Taihu Lake. The total organic carbon content may affect the fate of UVAs in sediments. Source analysis and correlation analysis showed that the following compounds had high proportions and significant correlations: 2-ethylhexyl methoxycinnamate, ethylhexyl salicylate, and HMS in 2015;and benzophenone-3, HMS, and benzyl salicylate in 2016. These results indicated the correlated compounds within each year had the same source. Ecological risk assessment results showed that UVAs in sediment from Taihu Lake may present a moderate risk to benthic organisms. The risk quotient of UVAs in 2016 was slightly higher than that in 2015. The seasonal pollution characteristics of UVAs and their comprehensive environmental risks should receive further attention. 展开更多
关键词 Organic ultraviolet absorbents 2-Ethylhexyl 4-methoxycinnamate Taihu lake Spatial and temporal variation Risk assessment
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Bridging Reinforcement Learning and Planning to Solve Combinatorial Optimization Problems with Nested Sub-Tasks
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作者 xiaohan shan Pengjiu Wang +3 位作者 Mingda Wan Dong Yan Jialian Li Jun Zhu 《CAAI Artificial Intelligence Research》 2023年第1期123-133,共11页
Combinatorial Optimization(CO)problems have been intensively studied for decades with a wide range of applications.For some classic CO problems,e.g.,the Traveling Salesman Problem(TSP),both traditional planning algori... Combinatorial Optimization(CO)problems have been intensively studied for decades with a wide range of applications.For some classic CO problems,e.g.,the Traveling Salesman Problem(TSP),both traditional planning algorithms and the emerging reinforcement learning have made solid progress in recent years.However,for CO problems with nested sub-tasks,neither end-to-end reinforcement learning algorithms nor traditional evolutionary methods can obtain satisfactory strategies within a limited time and computational resources.In this paper,we propose an algorithmic framework for solving CO problems with nested sub-tasks,in which learning and planning algorithms can be combined in a modular way.We validate our framework in the Job-Shop Scheduling Problem(JSSP),and the experimental results show that our algorithm has good performance in both solution qualities and model generalizations. 展开更多
关键词 reinforcement learning combinatorial optimization job-shop scheduling problem
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