Optically transparent microwave absorbing metasurfaces have shown great potential and are needed in multiple applications environments containing optical windows owing to their ability to reduce backscattering electro...Optically transparent microwave absorbing metasurfaces have shown great potential and are needed in multiple applications environments containing optical windows owing to their ability to reduce backscattering electromagnetic(EM)signals while keeping continuous optical observation.Meanwhile,they are also required to have adaptive EM manipulation capability to cope with complex and capricious EM environments.As a general approach,distributed circuit components,including positive-intrinsic-negative diodes and varactors and sensing components,are integrated with passive absorbing metasurfaces to realize adaptive control of microwave absorption.However,these circuit elements generally require bulky electrical wires and complex control circuits to regulate the operating state,resulting in the absorbing structures being optically opaque.Hence,it is a great challenge to realize self-operating absorbers while maintaining optical transparency.Here,we report an optically transparent cognitive metasurface made of patterned graphene sandwich structures and a radio frequency detector,which can achieve adaptive frequency manipulation to match incident EM waves.As a proof-of-principle application example,we realize a closed-loop automatic absorber system prototype of the proposed graphene metasurface with self-adaptive frequency variation,without any human intervention.The approach may facilitate other adaptive metadevices in microwave regime with high-level recognition and manipulation and,more generally,promote the development of intelligent stealth technologies.展开更多
In this paper, we prepared a dual functional system based on dextrin-coated silver nanoparticles which were further attached with iron oxide nanoparticles and cell penetrating peptide(Tat), producing Tat-modified Ag-F...In this paper, we prepared a dual functional system based on dextrin-coated silver nanoparticles which were further attached with iron oxide nanoparticles and cell penetrating peptide(Tat), producing Tat-modified Ag-Fe_3O_4 nanocomposites(Tat-FeAgNPs). To load drugs, an –SH containing linker, 3-mercaptopropanohydrazide, was designed and synthesized. It enabled the silver carriers to load and release doxorubicin(Dox) in a pH-sensitive pattern. The delivery efficiency of this system was assessed in vitro using MCF-7 cells, and in vivo using null BalB/c mice bearing MCF-7 xenograft tumors. Our results demonstrated that both Tat and externally applied magnetic field could promote cellular uptake and consequently the cytotoxicity of doxorubicin-loaded nanoparticles,with the IC_(50) of Tat-FeAgNP-Dox to be 0.63 mmol/L. The in vivo delivery efficiency of Tat-FeAgNP carrying Cy5 to the mouse tumor was analyzed using the in vivo optical imaging tests, in which TatFeAgNP-Cy5 yielded the most efficient accumulation in the tumor(6.772.4% ID of Tat-FeAgNPs).Anti-tumor assessment also demonstrated that Tat-FeAgNP-Dox displayed the most significant tumor-inhibiting effects and reduced the specific growth rate of tumor by 29.6%(P ? 0.009), which could be attributed to its superior performance in tumor drug delivery in comparison with the control nanovehicles.展开更多
A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management.At present,using well logging data to estimate reservoir physical parameters is an import...A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management.At present,using well logging data to estimate reservoir physical parameters is an important means for reservoir evaluation.Based on the characteristics of large quantity and complexity of estimating process,we have attempted to design a nonlinear back propagation neural network model optimized by genetic algorithm(BPNNGA)for reservoir porosity prediction.This model is with the advantages of self-learning and self-adaption of back propagation neural network(BPNN),structural parameters optimizing and global searching optimal solution of genetic algorithm(GA).The model is applied to the Chang 8 oil group tight sandstone of Yanchang Formation in southwestern Ordos Basin.According to the correlations between well logging data and measured core porosity data,5 well logging curves(gamma ray,deep induction,density,acoustic,and compensated neutron)are selected as the input neurons while the measured core porosity is selected as the output neurons.The number of hidden layer neurons is defined as 20 by the method of multiple calibrating optimizations.Modeling results demonstrate that the average relative error of the model output is 10.77%,indicating the excellent predicting effect of the model.The predicting results of the model are compared with the predicting results of conventional multivariate stepwise regression algorithm,and BPNN model.The average relative errors of the above models are 12.83%,12.9%,and 13.47%,respectively.Results show that the predicting results of the BPNNGA model are more accurate than that of the other two,and BPNNGA is a more applicable method to estimate the reservoir porosity parameters in the study area.展开更多
基金China Postdoctoral Science Foundation(2022M710670)Fundamental Research Funds for the Central Universities(2242022k30008,2242022R20018)+1 种基金National Natural Science Foundation of China(62101115)China National Funds for Distinguished Young Scientists(61925103)。
文摘Optically transparent microwave absorbing metasurfaces have shown great potential and are needed in multiple applications environments containing optical windows owing to their ability to reduce backscattering electromagnetic(EM)signals while keeping continuous optical observation.Meanwhile,they are also required to have adaptive EM manipulation capability to cope with complex and capricious EM environments.As a general approach,distributed circuit components,including positive-intrinsic-negative diodes and varactors and sensing components,are integrated with passive absorbing metasurfaces to realize adaptive control of microwave absorption.However,these circuit elements generally require bulky electrical wires and complex control circuits to regulate the operating state,resulting in the absorbing structures being optically opaque.Hence,it is a great challenge to realize self-operating absorbers while maintaining optical transparency.Here,we report an optically transparent cognitive metasurface made of patterned graphene sandwich structures and a radio frequency detector,which can achieve adaptive frequency manipulation to match incident EM waves.As a proof-of-principle application example,we realize a closed-loop automatic absorber system prototype of the proposed graphene metasurface with self-adaptive frequency variation,without any human intervention.The approach may facilitate other adaptive metadevices in microwave regime with high-level recognition and manipulation and,more generally,promote the development of intelligent stealth technologies.
基金financial supports from National Key Research and Development Plan of China (2016YFE0119200)the Young Elite Scientists Sponsorship Program by Tianjin (No. TJSQNTJ-2017-14)National Natural Science Foundation of China (NSFC 81361140344, 21376164, 81402885, and 81373357)
文摘In this paper, we prepared a dual functional system based on dextrin-coated silver nanoparticles which were further attached with iron oxide nanoparticles and cell penetrating peptide(Tat), producing Tat-modified Ag-Fe_3O_4 nanocomposites(Tat-FeAgNPs). To load drugs, an –SH containing linker, 3-mercaptopropanohydrazide, was designed and synthesized. It enabled the silver carriers to load and release doxorubicin(Dox) in a pH-sensitive pattern. The delivery efficiency of this system was assessed in vitro using MCF-7 cells, and in vivo using null BalB/c mice bearing MCF-7 xenograft tumors. Our results demonstrated that both Tat and externally applied magnetic field could promote cellular uptake and consequently the cytotoxicity of doxorubicin-loaded nanoparticles,with the IC_(50) of Tat-FeAgNP-Dox to be 0.63 mmol/L. The in vivo delivery efficiency of Tat-FeAgNP carrying Cy5 to the mouse tumor was analyzed using the in vivo optical imaging tests, in which TatFeAgNP-Cy5 yielded the most efficient accumulation in the tumor(6.772.4% ID of Tat-FeAgNPs).Anti-tumor assessment also demonstrated that Tat-FeAgNP-Dox displayed the most significant tumor-inhibiting effects and reduced the specific growth rate of tumor by 29.6%(P ? 0.009), which could be attributed to its superior performance in tumor drug delivery in comparison with the control nanovehicles.
基金supported by the National Natural Science Foundation of China(No.41002045)。
文摘A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management.At present,using well logging data to estimate reservoir physical parameters is an important means for reservoir evaluation.Based on the characteristics of large quantity and complexity of estimating process,we have attempted to design a nonlinear back propagation neural network model optimized by genetic algorithm(BPNNGA)for reservoir porosity prediction.This model is with the advantages of self-learning and self-adaption of back propagation neural network(BPNN),structural parameters optimizing and global searching optimal solution of genetic algorithm(GA).The model is applied to the Chang 8 oil group tight sandstone of Yanchang Formation in southwestern Ordos Basin.According to the correlations between well logging data and measured core porosity data,5 well logging curves(gamma ray,deep induction,density,acoustic,and compensated neutron)are selected as the input neurons while the measured core porosity is selected as the output neurons.The number of hidden layer neurons is defined as 20 by the method of multiple calibrating optimizations.Modeling results demonstrate that the average relative error of the model output is 10.77%,indicating the excellent predicting effect of the model.The predicting results of the model are compared with the predicting results of conventional multivariate stepwise regression algorithm,and BPNN model.The average relative errors of the above models are 12.83%,12.9%,and 13.47%,respectively.Results show that the predicting results of the BPNNGA model are more accurate than that of the other two,and BPNNGA is a more applicable method to estimate the reservoir porosity parameters in the study area.
