The development of efficient green energy technology is imperative in the face of energy crises and environmental concerns.Photocatalysis,which utilizes solar energy for processes such as carbon dioxide(CO_(2)) reduct...The development of efficient green energy technology is imperative in the face of energy crises and environmental concerns.Photocatalysis,which utilizes solar energy for processes such as carbon dioxide(CO_(2)) reduction,organic pollutants degradation,and hydrogen(H_(2)) production through water splitting,is a promising approach.The key to high-efficiency photocatalysis lies in the design of superior photocatalysts.Graphene quantum dots(GQDs) have sparked significant interest in photocatalysis due to their exceptional up conversion photoluminescence(UCPL),strong light-capturing capability,and unique photoinduced charge transfer properties.However,their standalone use is limited by stability and activity.By integrating GQDs into composite photocatalysts,the separation of photogenerated electron-hole pairs is enhanced,boosting photocatalytic performance.This review provides the first overview and summary of the preparation methods of GQDs in photocatalysts,encompassing top-down and bottom-up strategy.Subsequently,a pioneering detailed summary was made on the applications of GQDs-semiconductor composites(metal organic frameworks,CdS,and bismuth-based oxides,etc.) in photocatalytic reactions such as CO_(2) reduction,organic pollutant degradation,and H_(2) generation.Furthermore,the corresponding representative examples and mechanisms are also elaborated and discussed respectively.Finally,the challenges and prospects for GQDs-based photocatalysts in the field of photocatalysis are proposed.This review provides inspiration and guidance for the development of efficient GQDs-based photocatalysts.展开更多
In the complex urban road traffic network,a sudden accident leads to rapid congestion in the nearby traffic region,which even makes the local traffic network capacity quickly reduce.Therefore,an efficient monitoring s...In the complex urban road traffic network,a sudden accident leads to rapid congestion in the nearby traffic region,which even makes the local traffic network capacity quickly reduce.Therefore,an efficient monitoring system for abnormal conditions of the urban road network plays a crucial role in the tolerance of the urban road network.The traditional traffic monitoring system not only costs a lot in construction and maintenance,but also may not cover the road network comprehensively,which could not meet the basic needs of traffic management.Only a more comprehensive and intelligent monitoring method is able to identify traffic anomalies more effectively and quickly,so that it can provide more effective support for traffic management decisions.The extensive use of positioning equipment made us able to obtain accurate trajectory data.This paper presents a traffic anomaly monitoring and prediction method based on vehicle trajectory data.This model uses deep learning to detect abnormal trajectory on the traffic road network.The method effectively analyses the abnormal source and potential anomaly to judge the abnormal region,which provides an important reference for the traffic department to take effective traffic control measures.Finally,the paper uses Internet vehicle trajectory data from Chengdu(China)to test and obtains an accurate result.展开更多
基金financial support provided by National Natural Science Foundation of China(No.22262024)research start-up funding from Changzhou University(No.ZMF23020031)+1 种基金the technical support from the Jiangsu Key Laboratory of Advanced Catalytic Materials and Technology,Jiangxi Province Academic and Technical Leader of Major Disciplines(No.20232BCJ22008)Key Project of Natural Science Foundation of Jiangxi Province(No.20232ACB204007)。
文摘The development of efficient green energy technology is imperative in the face of energy crises and environmental concerns.Photocatalysis,which utilizes solar energy for processes such as carbon dioxide(CO_(2)) reduction,organic pollutants degradation,and hydrogen(H_(2)) production through water splitting,is a promising approach.The key to high-efficiency photocatalysis lies in the design of superior photocatalysts.Graphene quantum dots(GQDs) have sparked significant interest in photocatalysis due to their exceptional up conversion photoluminescence(UCPL),strong light-capturing capability,and unique photoinduced charge transfer properties.However,their standalone use is limited by stability and activity.By integrating GQDs into composite photocatalysts,the separation of photogenerated electron-hole pairs is enhanced,boosting photocatalytic performance.This review provides the first overview and summary of the preparation methods of GQDs in photocatalysts,encompassing top-down and bottom-up strategy.Subsequently,a pioneering detailed summary was made on the applications of GQDs-semiconductor composites(metal organic frameworks,CdS,and bismuth-based oxides,etc.) in photocatalytic reactions such as CO_(2) reduction,organic pollutant degradation,and H_(2) generation.Furthermore,the corresponding representative examples and mechanisms are also elaborated and discussed respectively.Finally,the challenges and prospects for GQDs-based photocatalysts in the field of photocatalysis are proposed.This review provides inspiration and guidance for the development of efficient GQDs-based photocatalysts.
基金supported by the National Natural Science Foundation of China (Grant No.52172310).
文摘In the complex urban road traffic network,a sudden accident leads to rapid congestion in the nearby traffic region,which even makes the local traffic network capacity quickly reduce.Therefore,an efficient monitoring system for abnormal conditions of the urban road network plays a crucial role in the tolerance of the urban road network.The traditional traffic monitoring system not only costs a lot in construction and maintenance,but also may not cover the road network comprehensively,which could not meet the basic needs of traffic management.Only a more comprehensive and intelligent monitoring method is able to identify traffic anomalies more effectively and quickly,so that it can provide more effective support for traffic management decisions.The extensive use of positioning equipment made us able to obtain accurate trajectory data.This paper presents a traffic anomaly monitoring and prediction method based on vehicle trajectory data.This model uses deep learning to detect abnormal trajectory on the traffic road network.The method effectively analyses the abnormal source and potential anomaly to judge the abnormal region,which provides an important reference for the traffic department to take effective traffic control measures.Finally,the paper uses Internet vehicle trajectory data from Chengdu(China)to test and obtains an accurate result.
