Sunlight-driven photocatalytic water-splitting for hydrogen(H2)evolution is a desirable strategy to utilize solar energy.However,this strategy is restricted by insufficient light harvesting and high photogenerated ele...Sunlight-driven photocatalytic water-splitting for hydrogen(H2)evolution is a desirable strategy to utilize solar energy.However,this strategy is restricted by insufficient light harvesting and high photogenerated electron-hole recombination rates of TiO2-based photocatalysts.Here,a graphene-modified WO3/TiO2 step-scheme heterojunction(S-scheme heterojunction)composite photocatalyst was fabricated by a facile one-step hydrothermal method.In the ternary composite,TiO2 and WO3 nanoparticles adhered closely to reduced graphene oxide(rGO)and formed a novel S-scheme heterojunction.Moreover,rGO in the composite not only supplied abundant adsorption and catalytically active sites as an ideal support but also promoted electron separation and transfer from the conduction band of TiO2 by forming a Schottky junction between TiO2 and rGO.The positive cooperative effect of the S-scheme heterojunction formed between WO3 and TiO2 and the Schottky heterojunction formed between TiO2 and graphene sheets suppressed the recombination of relatively useful electrons and holes.This effect also enhanced the light harvesting and promoted the reduction reaction at the active sites.Thus,the novel ternary WO3/TiO2/rGO composite demonstrated a remarkably enhanced photocatalytic H2 evolution rate of 245.8μmol g^-1 h^-1,which was approximately 3.5-fold that of pure TiO2.This work not only presents a low-cost graphene-based S-scheme heterojunction photocatalyst that was obtained via a feasible one-step hydrothermal approach to realize highly efficient H2 generation without using noble metals,but also provides new insights into the design of novel heterojunction photocatalysts.展开更多
Extended light absorption and dynamic charge separation are vital factors that determine the effectivenessof photocatalysts.In this study,a nonmetallic plasmonic S‐scheme photocatalyst was fabricatedby loading 1D pla...Extended light absorption and dynamic charge separation are vital factors that determine the effectivenessof photocatalysts.In this study,a nonmetallic plasmonic S‐scheme photocatalyst was fabricatedby loading 1D plasmonic W_(18)O_(49)nanowires onto 2D g‐C_(3)N_(4)nanosheets.W_(18)O_(49)nanowiresplay the dual role of a light absorption antenna—that extends light adsorption—and a hot electrondonor—that assists the water reduction reaction in a wider light spectrum range.Moreover,S‐scheme charge transfer resulting from the matching bandgaps of W_(18)O_(49)and g‐C_(3)N_(4)can lead tostrong redox capability and high migration speed of the photoinduced charges.Consequently,in thisstudy,W_(18)O_(49)/g‐C_(3)N_(4)hybrids exhibited higher photocatalytic H2 generation than that of pristineg‐C_(3)N_(4)under light irradiation of 420–550 nm.Furthermore,the H2 production rate of thebest‐performing W_(18)O_(49)/g‐C_(3)N_(4)hybrid was 41.5μmol·g^(−1)·h^(−1)upon exposure to monochromaticlight at 550 nm,whereas pure g‐C_(3)N_(4)showed negligible activity.This study promotes novel andenvironmentally friendly hot‐electron‐assisted S‐scheme photocatalysts for the broad‐spectrumutilization of solar light.展开更多
We study theoretically intense terahertz radiation from multi-color laser pulse with uncommon frequency ratios. Com- paring the two-color laser scheme, of which the uncommon frequency ratio should be set to be a speci...We study theoretically intense terahertz radiation from multi-color laser pulse with uncommon frequency ratios. Com- paring the two-color laser scheme, of which the uncommon frequency ratio should be set to be a specific value, we show that by using multi-color harmonic laser pulses as the first pump component, the lasers as the second pump component can be adjusted in a continuous frequency range. Moreover, these multi-color laser pulses can effectively modulate and enhance the terahertz radiation, and the terahertz yield increases with the increase of the wavelength of the uncommon pump com- ponent and is stable to the laser relative phase. Finally, we utilize the electron densities and velocities of ionization events to illustrate the physical mechanism of the intense terahertz generation.展开更多
Fractal time-dependent issues in fluid dynamics provide a distinct difficulty in numerical analysis due to their complex characteristics,necessitating specialized computing techniques for precise and economical soluti...Fractal time-dependent issues in fluid dynamics provide a distinct difficulty in numerical analysis due to their complex characteristics,necessitating specialized computing techniques for precise and economical solutions.This study presents an innovative computational approach to tackle these difficulties.The main focus is applying the Fractal Runge-Kutta Method to model the time-dependent magnetohydrodynamic(MHD)Newtonian fluid with rescaled viscosity flow on Riga plates.An efficient computational scheme is proposed for handling fractal time-dependent problems in flow phenomena.The scheme is comprised of three stages and constructed using three different time levels.The stability of the scheme is shown by employing the Fourier series analysis to solve scalar problems.The scheme’s convergence is guaranteed for a time fractal partial differential equations system.The scheme is applied to the dimensionless fractal heat and mass transfer model of incompressible,unsteady,laminar,Newtonian fluid with rescaled viscosity flow over the flat and oscillatory Riga plates under the effects of space-and temperature-dependent heat sources.The first-order back differences discretize the continuity equation.The results show that skin friction local Nusselt number declines by raising the coefficient of the temperature-dependent term of heat source and Eckert number.The numerical simulations provide valuable insights into fluid dynamics,explicitly highlighting the influence of the temperature-dependent coefficient of the heat source and the Eckert number on skin friction and local Nusselt number.展开更多
Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks(DNNs)in high efficiency and accuracy.This exploration implies heavy workloads for domain expert...Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks(DNNs)in high efficiency and accuracy.This exploration implies heavy workloads for domain experts,and an automatic compression method is needed.However,the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios.In this paper,we propose an end-to-end framework named AutoQNN,for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor.AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques:quantizing scheme search(QSS),quantizing precision learning(QPL),and quantized architecture generation(QAG).QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search,and then uses the Differentiable Neural Architecture Search(DNAS)algorithm to seek the layer-or model-desired scheme from the set.QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes,to the best of our knowledge.QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint.QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention,to facilitate end-to-end neural network quantization.We have implemented AutoQNN and integrated it into Keras.Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization.For 2-bit weight and activation of AlexNet and ResNet18,AutoQNN can achieve the accuracy results of 59.75%and 68.86%,respectively,and obtain accuracy improvements by up to 1.65%and 1.74%,respectively,compared with state-of-the-art methods.Especially,compared with the full-precision AlexNet and ResN展开更多
基金supported by the National Natural Science Foundation of China(U1705251,21871217,21573170,21433007)the National Key Research and Development Program of China(2018YFB1502001)~~
文摘Sunlight-driven photocatalytic water-splitting for hydrogen(H2)evolution is a desirable strategy to utilize solar energy.However,this strategy is restricted by insufficient light harvesting and high photogenerated electron-hole recombination rates of TiO2-based photocatalysts.Here,a graphene-modified WO3/TiO2 step-scheme heterojunction(S-scheme heterojunction)composite photocatalyst was fabricated by a facile one-step hydrothermal method.In the ternary composite,TiO2 and WO3 nanoparticles adhered closely to reduced graphene oxide(rGO)and formed a novel S-scheme heterojunction.Moreover,rGO in the composite not only supplied abundant adsorption and catalytically active sites as an ideal support but also promoted electron separation and transfer from the conduction band of TiO2 by forming a Schottky junction between TiO2 and rGO.The positive cooperative effect of the S-scheme heterojunction formed between WO3 and TiO2 and the Schottky heterojunction formed between TiO2 and graphene sheets suppressed the recombination of relatively useful electrons and holes.This effect also enhanced the light harvesting and promoted the reduction reaction at the active sites.Thus,the novel ternary WO3/TiO2/rGO composite demonstrated a remarkably enhanced photocatalytic H2 evolution rate of 245.8μmol g^-1 h^-1,which was approximately 3.5-fold that of pure TiO2.This work not only presents a low-cost graphene-based S-scheme heterojunction photocatalyst that was obtained via a feasible one-step hydrothermal approach to realize highly efficient H2 generation without using noble metals,but also provides new insights into the design of novel heterojunction photocatalysts.
文摘Extended light absorption and dynamic charge separation are vital factors that determine the effectivenessof photocatalysts.In this study,a nonmetallic plasmonic S‐scheme photocatalyst was fabricatedby loading 1D plasmonic W_(18)O_(49)nanowires onto 2D g‐C_(3)N_(4)nanosheets.W_(18)O_(49)nanowiresplay the dual role of a light absorption antenna—that extends light adsorption—and a hot electrondonor—that assists the water reduction reaction in a wider light spectrum range.Moreover,S‐scheme charge transfer resulting from the matching bandgaps of W_(18)O_(49)and g‐C_(3)N_(4)can lead tostrong redox capability and high migration speed of the photoinduced charges.Consequently,in thisstudy,W_(18)O_(49)/g‐C_(3)N_(4)hybrids exhibited higher photocatalytic H2 generation than that of pristineg‐C_(3)N_(4)under light irradiation of 420–550 nm.Furthermore,the H2 production rate of thebest‐performing W_(18)O_(49)/g‐C_(3)N_(4)hybrid was 41.5μmol·g^(−1)·h^(−1)upon exposure to monochromaticlight at 550 nm,whereas pure g‐C_(3)N_(4)showed negligible activity.This study promotes novel andenvironmentally friendly hot‐electron‐assisted S‐scheme photocatalysts for the broad‐spectrumutilization of solar light.
基金Project supported by the National Natural Science Foundation of China(Grant No.11604205)the Talent Program of Shanghai University of Engineering Science,China
文摘We study theoretically intense terahertz radiation from multi-color laser pulse with uncommon frequency ratios. Com- paring the two-color laser scheme, of which the uncommon frequency ratio should be set to be a specific value, we show that by using multi-color harmonic laser pulses as the first pump component, the lasers as the second pump component can be adjusted in a continuous frequency range. Moreover, these multi-color laser pulses can effectively modulate and enhance the terahertz radiation, and the terahertz yield increases with the increase of the wavelength of the uncommon pump com- ponent and is stable to the laser relative phase. Finally, we utilize the electron densities and velocities of ionization events to illustrate the physical mechanism of the intense terahertz generation.
基金support of Prince Sultan University in paying the article processing charges(APC)for this publication.
文摘Fractal time-dependent issues in fluid dynamics provide a distinct difficulty in numerical analysis due to their complex characteristics,necessitating specialized computing techniques for precise and economical solutions.This study presents an innovative computational approach to tackle these difficulties.The main focus is applying the Fractal Runge-Kutta Method to model the time-dependent magnetohydrodynamic(MHD)Newtonian fluid with rescaled viscosity flow on Riga plates.An efficient computational scheme is proposed for handling fractal time-dependent problems in flow phenomena.The scheme is comprised of three stages and constructed using three different time levels.The stability of the scheme is shown by employing the Fourier series analysis to solve scalar problems.The scheme’s convergence is guaranteed for a time fractal partial differential equations system.The scheme is applied to the dimensionless fractal heat and mass transfer model of incompressible,unsteady,laminar,Newtonian fluid with rescaled viscosity flow over the flat and oscillatory Riga plates under the effects of space-and temperature-dependent heat sources.The first-order back differences discretize the continuity equation.The results show that skin friction local Nusselt number declines by raising the coefficient of the temperature-dependent term of heat source and Eckert number.The numerical simulations provide valuable insights into fluid dynamics,explicitly highlighting the influence of the temperature-dependent coefficient of the heat source and the Eckert number on skin friction and local Nusselt number.
基金supported by the China Postdoctoral Science Foundation under Grant No.2022M721707the National Natural Science Foundation of China under Grant Nos.62002175 and 62272248+1 种基金the Special Funding for Excellent Enterprise Technology Correspondent of Tianjin under Grant No.21YDTPJC00380the Open Project Foundation of Information Security Evaluation Center of Civil Aviation,Civil Aviation University of China,under Grant No.ISECCA-202102.
文摘Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks(DNNs)in high efficiency and accuracy.This exploration implies heavy workloads for domain experts,and an automatic compression method is needed.However,the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios.In this paper,we propose an end-to-end framework named AutoQNN,for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor.AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques:quantizing scheme search(QSS),quantizing precision learning(QPL),and quantized architecture generation(QAG).QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search,and then uses the Differentiable Neural Architecture Search(DNAS)algorithm to seek the layer-or model-desired scheme from the set.QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes,to the best of our knowledge.QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint.QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention,to facilitate end-to-end neural network quantization.We have implemented AutoQNN and integrated it into Keras.Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization.For 2-bit weight and activation of AlexNet and ResNet18,AutoQNN can achieve the accuracy results of 59.75%and 68.86%,respectively,and obtain accuracy improvements by up to 1.65%and 1.74%,respectively,compared with state-of-the-art methods.Especially,compared with the full-precision AlexNet and ResN
