Novel ZnSe/NiO heterostructure nanocomposites were successfully prepared by one-step hydrothermal method.The ZnSe/NiO-based sensor exhibits a response of~96.47% to 8×10^(-6) NO_(2) at 140℃,which is significantly...Novel ZnSe/NiO heterostructure nanocomposites were successfully prepared by one-step hydrothermal method.The ZnSe/NiO-based sensor exhibits a response of~96.47% to 8×10^(-6) NO_(2) at 140℃,which is significantly higher than those of intrinsic ZnSe-based(no response)and NiO-based(~19.65%)sensors.The theoretical detection limit(LOD)of the sensor is calculated to be 8.91×10^(-9),indicating that the sensor can be applied to detect the ultralow concentrations of NO_(2).The effect of NiO content on the gas-sensing performance of the nanocomposites was investigated in detail.The optimal NiO content in the nanocomposite is determined to be15.16%to achieve the highest response.The as-fabricated sensor also presents an excellent selectivity to several possible interferents such as methanol,ethanol,acetone,benzene,ammonia and formaldehyde.The enhanced sensing performance can be attributed to the formation of p-p heterostructures between ZnSe and NiO,which induces the charge transfer across the interfaces and yields more active sites.展开更多
Nonnegative tensor decomposition has become increasingly important for multiway data analysis in recent years. The alternating proximal gradient(APG) is a popular optimization method for nonnegative tensor decompositi...Nonnegative tensor decomposition has become increasingly important for multiway data analysis in recent years. The alternating proximal gradient(APG) is a popular optimization method for nonnegative tensor decomposition in the block coordinate descent framework. In this study, we propose an inexact version of the APG algorithm for nonnegative CANDECOMP/PARAFAC decomposition, wherein each factor matrix is updated by only finite inner iterations. We also propose a parameter warm-start method that can avoid the frequent parameter resetting of conventional APG methods and improve convergence performance.By experimental tests, we find that when the number of inner iterations is limited to around 10 to 20, the convergence speed is accelerated significantly without losing its low relative error. We evaluate our method on both synthetic and real-world tensors.The results demonstrate that the proposed inexact APG algorithm exhibits outstanding performance on both convergence speed and computational precision compared with existing popular algorithms.展开更多
基金financially supported by the National Natural Science Foundation of China(No.61971085)Dalian Science and Technology Innovation Fund Project(No.2019J12GX048)。
文摘Novel ZnSe/NiO heterostructure nanocomposites were successfully prepared by one-step hydrothermal method.The ZnSe/NiO-based sensor exhibits a response of~96.47% to 8×10^(-6) NO_(2) at 140℃,which is significantly higher than those of intrinsic ZnSe-based(no response)and NiO-based(~19.65%)sensors.The theoretical detection limit(LOD)of the sensor is calculated to be 8.91×10^(-9),indicating that the sensor can be applied to detect the ultralow concentrations of NO_(2).The effect of NiO content on the gas-sensing performance of the nanocomposites was investigated in detail.The optimal NiO content in the nanocomposite is determined to be15.16%to achieve the highest response.The as-fabricated sensor also presents an excellent selectivity to several possible interferents such as methanol,ethanol,acetone,benzene,ammonia and formaldehyde.The enhanced sensing performance can be attributed to the formation of p-p heterostructures between ZnSe and NiO,which induces the charge transfer across the interfaces and yields more active sites.
基金This work was supported by the National Natural Science Foundation of China(Grant No.91748105)the National Foundation in China(Grant Nos.JCKY2019110B009 and 2020-JCJQ-JJ-252)+1 种基金the Fundamental Research Funds for the Central Universities(Grant Nos.DUT20LAB303 and DUT20LAB308)in Dalian University of Technology in Chinathe scholarship from China Scholarship Council(Grant No.201600090043)。
文摘Nonnegative tensor decomposition has become increasingly important for multiway data analysis in recent years. The alternating proximal gradient(APG) is a popular optimization method for nonnegative tensor decomposition in the block coordinate descent framework. In this study, we propose an inexact version of the APG algorithm for nonnegative CANDECOMP/PARAFAC decomposition, wherein each factor matrix is updated by only finite inner iterations. We also propose a parameter warm-start method that can avoid the frequent parameter resetting of conventional APG methods and improve convergence performance.By experimental tests, we find that when the number of inner iterations is limited to around 10 to 20, the convergence speed is accelerated significantly without losing its low relative error. We evaluate our method on both synthetic and real-world tensors.The results demonstrate that the proposed inexact APG algorithm exhibits outstanding performance on both convergence speed and computational precision compared with existing popular algorithms.
