Nanowire(NW) structures is an alternative candidate for constructing the next generation photoelectrochemical water splitting system, due to the outstanding optical and electrical properties. NW photoelectrodes compar...Nanowire(NW) structures is an alternative candidate for constructing the next generation photoelectrochemical water splitting system, due to the outstanding optical and electrical properties. NW photoelectrodes comparing to traditional semiconductor photoelectrodes shows the comparatively shorter transfer distance of photo-induced carriers and the increase amount of the surface reaction sites, which is beneficial for lowering the recombination probability of charge carriers and improving their photoelectrochemical(PEC) performances. Here, we demonstrate for the first time that super-long Cu_2O NWs, more than 4.5 μm,with highly efficient water splitting performance, were synthesized using a cost-effective anodic alumina oxide(AAO) template method. In comparison with the photocathode with planar Cu_2O films, the photocathode with Cu_2O NWs demonstrates a significant enhancement in photocurrent, from –1.00 to –2.75 mA/cm^2 at –0.8 V versus Ag/AgCl. After optimization of the photoelectrochemical electrode through depositing Pt NPs with atomic layer deposition(ALD) technology on the Cu_2O NWs, the plateau of photocurrent has been enlarged to –7 mA/cm^2 with the external quantum yield up to 34% at 410 nm. This study suggests that the photoelectrode based on Cu_2O NWs is a hopeful system for establishing high-efficiency water splitting system under visible light.展开更多
针对多雷达辐射源脉冲交错背景下,线性调频(Linear Frequency Modulation,LFM)信号低信噪比导致的脉冲分裂带来原始信号参数难以估计的问题,本文提出了基于深度神经网络和直方图统计的LFM信号两阶段提取与参数估计方法。首先利用双向长...针对多雷达辐射源脉冲交错背景下,线性调频(Linear Frequency Modulation,LFM)信号低信噪比导致的脉冲分裂带来原始信号参数难以估计的问题,本文提出了基于深度神经网络和直方图统计的LFM信号两阶段提取与参数估计方法。首先利用双向长短时记忆网络挖掘原始脉冲流中LFM信号与非LFM信号的调制模式差异并进行分类;其次通过序列调频斜率直方图寻找LFM信号分裂脉冲序列间隐含的原始信号调频斜率信息,提取不同调频斜率的LFM信号脉冲子序列;最后在每个子序列中分别估计原始信号的参数。仿真实验结果表明,相较于传统的序列差值直方图算法和循环神经网络分选方法,本文所提方法能够更准确地提取出LFM脉冲信号,并得到较为精确的参数估计结果。展开更多
基金supported by European Research Council(HiNaPc:737616)European Research Council(ThreeDsurface:240144)+8 种基金BMBF(ZIK-3DNanoDevice:03Z1MN11)DFG(LE2249_4-1)BMBF(Meta-ZIK-BioLithoMorphie:03Z1M511)National Natural Science Foundation of China(Nos.21577086,51702130,21503209)Natural Science Foundation of Jiangsu Province(BK 20170550)Jiangsu Specially-Appointed Professor ProgramHundred-Talent Program(Chinese Academy of Sciences)Beijing Natural Science Foundation(No.2162042)Key Research Program of Frontier Science,CAS(No.QYZDBSSW-SLH006)
文摘Nanowire(NW) structures is an alternative candidate for constructing the next generation photoelectrochemical water splitting system, due to the outstanding optical and electrical properties. NW photoelectrodes comparing to traditional semiconductor photoelectrodes shows the comparatively shorter transfer distance of photo-induced carriers and the increase amount of the surface reaction sites, which is beneficial for lowering the recombination probability of charge carriers and improving their photoelectrochemical(PEC) performances. Here, we demonstrate for the first time that super-long Cu_2O NWs, more than 4.5 μm,with highly efficient water splitting performance, were synthesized using a cost-effective anodic alumina oxide(AAO) template method. In comparison with the photocathode with planar Cu_2O films, the photocathode with Cu_2O NWs demonstrates a significant enhancement in photocurrent, from –1.00 to –2.75 mA/cm^2 at –0.8 V versus Ag/AgCl. After optimization of the photoelectrochemical electrode through depositing Pt NPs with atomic layer deposition(ALD) technology on the Cu_2O NWs, the plateau of photocurrent has been enlarged to –7 mA/cm^2 with the external quantum yield up to 34% at 410 nm. This study suggests that the photoelectrode based on Cu_2O NWs is a hopeful system for establishing high-efficiency water splitting system under visible light.
文摘针对多雷达辐射源脉冲交错背景下,线性调频(Linear Frequency Modulation,LFM)信号低信噪比导致的脉冲分裂带来原始信号参数难以估计的问题,本文提出了基于深度神经网络和直方图统计的LFM信号两阶段提取与参数估计方法。首先利用双向长短时记忆网络挖掘原始脉冲流中LFM信号与非LFM信号的调制模式差异并进行分类;其次通过序列调频斜率直方图寻找LFM信号分裂脉冲序列间隐含的原始信号调频斜率信息,提取不同调频斜率的LFM信号脉冲子序列;最后在每个子序列中分别估计原始信号的参数。仿真实验结果表明,相较于传统的序列差值直方图算法和循环神经网络分选方法,本文所提方法能够更准确地提取出LFM脉冲信号,并得到较为精确的参数估计结果。
