Flexible electrochromic energy storage devices(FECESDs)for powering flexible electronics have attracted considerable attention.Silver nanowires(AgNWs)are one kind of the most promising flexible transparent electrodes(...Flexible electrochromic energy storage devices(FECESDs)for powering flexible electronics have attracted considerable attention.Silver nanowires(AgNWs)are one kind of the most promising flexible transparent electrodes(FTEs)materials for the emerging flexible devices.Currently,fabricating FECESD based on AgNWs FTEs is still hindered by their intrinsic poor electrochemical stability.To address this issue,a hybrid AgNWs/Co(OH)_(2)/PEDOT:PSS electrode is proposed.The PEDOT:PSS could not only improve the resistance against electrochemical corrosion of AgNWs,but also work as functional layer to realize the color-changing and energy storage properties.Moreover,the Co(OH)_(2)interlayer further improved the color-changing and energy storage performance.Based on the improvement,we assembled the symmetrical FECESDs.Under the same condition,the areal capacitance(0.8 mF cm^(−2))and coloration efficiency(269.80 cm^(2)C−1)of AgNWs/Co(OH)_(2)/PEDOT:PSS FECESDs were obviously higher than AgNWs/PEDOT:PSS FECESDs.Furthermore,the obtained FECESDs exhibited excellent stability against the mechanical deformation.The areal capacitance remained stable during 1000 times cyclic bending with a 25 mm curvature radius.These results demonstrated the broad application potential of the AgNWs/Co(OH)_(2)/PEDOT:PSS FECESD for the emerging portable and multifunctional electronics.展开更多
The second-order random walk has recently been shown to effectively improve the accuracy in graph analysis tasks.Existing work mainly focuses on centralized second-order random walk(SOW)algorithms.SOW algorithms rely ...The second-order random walk has recently been shown to effectively improve the accuracy in graph analysis tasks.Existing work mainly focuses on centralized second-order random walk(SOW)algorithms.SOW algorithms rely on edge-to-edge transition probabilities to generate next random steps.However,it is prohibitively costly to store all the probabilities for large-scale graphs,and restricting the number of probabilities to consider can negatively impact the accuracy of graph analysis tasks.In this paper,we propose and study an alternative approach,SOOP(second-order random walks with on-demand probability computation),that avoids the space overhead by computing the edge-to-edge transition probabilities on demand during the random walk.However,the same probabilities may be computed multiple times when the same edge appears multiple times in SOW,incurring extra cost for redundant computation and communication.We propose two optimization techniques that reduce the complexity of computing edge-to-edge transition probabilities to generate next random steps,and reduce the cost of communicating out-neighbors for the probability computation,respectively.Our experiments on real-world and synthetic graphs show that SOOP achieves orders of magnitude better performance than baseline precompute solutions,and it can efficiently computes SOW algorithms on billion-scale graphs.展开更多
基金supports from the National Natural Science Foundation of China (Grant No. 52175300)Fundamental Research Funds for the Central Universities (2022FRFK060008)+2 种基金Heilongjiang Touyan Innovation Team Program (HITTY-20190013)Shenzhen Fundamental Research Programs (JCYJ20200925160843002)Start-up fund of SUSTech (Y01256114)
文摘Flexible electrochromic energy storage devices(FECESDs)for powering flexible electronics have attracted considerable attention.Silver nanowires(AgNWs)are one kind of the most promising flexible transparent electrodes(FTEs)materials for the emerging flexible devices.Currently,fabricating FECESD based on AgNWs FTEs is still hindered by their intrinsic poor electrochemical stability.To address this issue,a hybrid AgNWs/Co(OH)_(2)/PEDOT:PSS electrode is proposed.The PEDOT:PSS could not only improve the resistance against electrochemical corrosion of AgNWs,but also work as functional layer to realize the color-changing and energy storage properties.Moreover,the Co(OH)_(2)interlayer further improved the color-changing and energy storage performance.Based on the improvement,we assembled the symmetrical FECESDs.Under the same condition,the areal capacitance(0.8 mF cm^(−2))and coloration efficiency(269.80 cm^(2)C−1)of AgNWs/Co(OH)_(2)/PEDOT:PSS FECESDs were obviously higher than AgNWs/PEDOT:PSS FECESDs.Furthermore,the obtained FECESDs exhibited excellent stability against the mechanical deformation.The areal capacitance remained stable during 1000 times cyclic bending with a 25 mm curvature radius.These results demonstrated the broad application potential of the AgNWs/Co(OH)_(2)/PEDOT:PSS FECESD for the emerging portable and multifunctional electronics.
文摘The second-order random walk has recently been shown to effectively improve the accuracy in graph analysis tasks.Existing work mainly focuses on centralized second-order random walk(SOW)algorithms.SOW algorithms rely on edge-to-edge transition probabilities to generate next random steps.However,it is prohibitively costly to store all the probabilities for large-scale graphs,and restricting the number of probabilities to consider can negatively impact the accuracy of graph analysis tasks.In this paper,we propose and study an alternative approach,SOOP(second-order random walks with on-demand probability computation),that avoids the space overhead by computing the edge-to-edge transition probabilities on demand during the random walk.However,the same probabilities may be computed multiple times when the same edge appears multiple times in SOW,incurring extra cost for redundant computation and communication.We propose two optimization techniques that reduce the complexity of computing edge-to-edge transition probabilities to generate next random steps,and reduce the cost of communicating out-neighbors for the probability computation,respectively.Our experiments on real-world and synthetic graphs show that SOOP achieves orders of magnitude better performance than baseline precompute solutions,and it can efficiently computes SOW algorithms on billion-scale graphs.
