A Z-scheme heterostructure of Mo,W co-doped BiVO_(4)(Mo,W:BVO/BiOCl@C)was fabricated by a simple solid solution drying and calcination(SSDC)method.The heterostructure was characterized by X-ray diffraction(XRD),Fourie...A Z-scheme heterostructure of Mo,W co-doped BiVO_(4)(Mo,W:BVO/BiOCl@C)was fabricated by a simple solid solution drying and calcination(SSDC)method.The heterostructure was characterized by X-ray diffraction(XRD),Fourier transform infrared(FTIR),X-ray photoelectron spectroscopy(XPS),etc.Under visible light irradiation,Mo,W:BVO/BiOCl@C heterostructure exhibits excellent photoelectrochemical capability compared with other as-prepared samples.The photocurrent density and the incident photon-to-electron conversion efficiency(IPCE)are about 5.4 and 9.0 times higher than those of pure BiVO_(4),respectively.The enhancement of the photoelectrochemical performance can be attributed to the construct of Z-scheme system,which is deduced from the radical trapping experiments.The Mo,W:BVO/BiOCl@C Z-scheme heterojunction enhances the visible-light absorption and reduces the recombination rate of charge carriers.This work provides an effective strategy to construct Z-scheme photoelectrodes for the application of photoelectrochemical water splitting.展开更多
Spatial division multiplexing enabled elastic optical networks(SDM-EONs) are the potential implementation form of future optical transport networks, because it can curve the physical limitation of achievable transmiss...Spatial division multiplexing enabled elastic optical networks(SDM-EONs) are the potential implementation form of future optical transport networks, because it can curve the physical limitation of achievable transmission capacity in single-mode fiber and single-core fiber. However, spectrum fragmentation issue becomes more serious in SDM-EONs compared with simple elastic optical networks(EONs) with single mode fiber or single core fiber. In this paper, multicore virtual concatenation(MCVC) scheme is first proposed considering inter-core crosstalk to solve the spectrum fragmentation issue in SDM-EONs. Simulation results show that the proposed MCVC scheme can achieve better performance compared with the baseline scheme, i.e., single-core virtual concatenation(SCVC) scheme, in terms of blocking probability and spectrum utilization.展开更多
Stimulus-sensitive surfaces with tunable morphologies exhibit a wide range of applications in the fields of surface science and engineering.Herein,a cost-effective yet practical strategy is proposed to fabricate photo...Stimulus-sensitive surfaces with tunable morphologies exhibit a wide range of applications in the fields of surface science and engineering.Herein,a cost-effective yet practical strategy is proposed to fabricate photo-sensitive patterning surface on film/substrate wrinkle system based on an azo-containing polyblend.By manipulating the stress field of the bilayer system globally and/or locally upon the stress relaxation triggered by the reversible cis-trans isomerization of the azobenzene,heating/cooling triggered surface wrinkles on the polyblend films could be tailor-made with visible-light-irradiation.Notably,upon selective photo-irradiation,bespoke surface patterns may be cyclically generated or eliminated,allowing these reconfigurable patterned polyblend surfaces to be used as rewritable information storage media for non-ink printing.The as-prepared photo-printed information patterns with high-resolution are shown to be rewritable for multiple cycles and legible for over 90 d in dark ambient conditions.This study not only provides a versatile strategy for flourishing the stimulus-sensitive systems,but also sheds light on the stress relaxation-triggered morphological evolution of the wrinkling polyblend films.展开更多
In data-driven materials design where the target materials have limited data,the transfer machine learning from large known source materials,becomes a demanding strategy especially across different crystal structures....In data-driven materials design where the target materials have limited data,the transfer machine learning from large known source materials,becomes a demanding strategy especially across different crystal structures.In this work,we proposed a deep transfer learning approach to predict thermodynamically stable perovskite oxides based on a large computational dataset of spinel oxides.The deep neural network(DNN)source domain model with“Center-Environment”(CE)features was first developed using the formation energy of 5329 spinel oxide structures and then was fine-tuned by learning a small dataset of 855 perovskite oxide structures,leading to a transfer learning model with good transferability in the target domain of perovskite oxides.Based on the transferred model,we further predicted the formation energy of potential 5329 perovskite structures with combination of 73 elements.Combining the criteria of formation energy and structure factors including tolerance factor(0.7<t≤1.1)and octahedron factor(0.45<μ<0.7),we predicted 1314 thermodynamically stable perovskite oxides,among which 144 oxides were reported to be synthesized experimentally,10 oxides were predicted computationally by other literatures,301 oxides were recorded in the Materials Project database,and 859 oxides have been first reported.Combing with the structure-informed features the transfer machine learning approach in this work takes the advantage of existing data to predict new structures at a lower cost,providing an effective acceleration strategy for the expensive high-throughput computational screening in materials design.The predicted stable novel perovskite oxides serve as a rich platform for exploring potential renewable energy and electronic materials applications.展开更多
基金financially supported by the Natural Science Foundation of Shandong Province of China (No. ZR2019MB006)the China Postdoctoral Science Foundation (Nos. 2018M632610 and 2017M610409)
文摘A Z-scheme heterostructure of Mo,W co-doped BiVO_(4)(Mo,W:BVO/BiOCl@C)was fabricated by a simple solid solution drying and calcination(SSDC)method.The heterostructure was characterized by X-ray diffraction(XRD),Fourier transform infrared(FTIR),X-ray photoelectron spectroscopy(XPS),etc.Under visible light irradiation,Mo,W:BVO/BiOCl@C heterostructure exhibits excellent photoelectrochemical capability compared with other as-prepared samples.The photocurrent density and the incident photon-to-electron conversion efficiency(IPCE)are about 5.4 and 9.0 times higher than those of pure BiVO_(4),respectively.The enhancement of the photoelectrochemical performance can be attributed to the construct of Z-scheme system,which is deduced from the radical trapping experiments.The Mo,W:BVO/BiOCl@C Z-scheme heterojunction enhances the visible-light absorption and reduces the recombination rate of charge carriers.This work provides an effective strategy to construct Z-scheme photoelectrodes for the application of photoelectrochemical water splitting.
基金supported in part by NSFC project (61571058, 61601052)
文摘Spatial division multiplexing enabled elastic optical networks(SDM-EONs) are the potential implementation form of future optical transport networks, because it can curve the physical limitation of achievable transmission capacity in single-mode fiber and single-core fiber. However, spectrum fragmentation issue becomes more serious in SDM-EONs compared with simple elastic optical networks(EONs) with single mode fiber or single core fiber. In this paper, multicore virtual concatenation(MCVC) scheme is first proposed considering inter-core crosstalk to solve the spectrum fragmentation issue in SDM-EONs. Simulation results show that the proposed MCVC scheme can achieve better performance compared with the baseline scheme, i.e., single-core virtual concatenation(SCVC) scheme, in terms of blocking probability and spectrum utilization.
基金supports from the National Natural Science Foundation of China (Nos.21704033,52173168)the Natural Science Foundation of Shandong Province (Nos.ZR2020LFG009,SZR1946)the China Postdoctoral Science Foundation (No.2019M662441).
文摘Stimulus-sensitive surfaces with tunable morphologies exhibit a wide range of applications in the fields of surface science and engineering.Herein,a cost-effective yet practical strategy is proposed to fabricate photo-sensitive patterning surface on film/substrate wrinkle system based on an azo-containing polyblend.By manipulating the stress field of the bilayer system globally and/or locally upon the stress relaxation triggered by the reversible cis-trans isomerization of the azobenzene,heating/cooling triggered surface wrinkles on the polyblend films could be tailor-made with visible-light-irradiation.Notably,upon selective photo-irradiation,bespoke surface patterns may be cyclically generated or eliminated,allowing these reconfigurable patterned polyblend surfaces to be used as rewritable information storage media for non-ink printing.The as-prepared photo-printed information patterns with high-resolution are shown to be rewritable for multiple cycles and legible for over 90 d in dark ambient conditions.This study not only provides a versatile strategy for flourishing the stimulus-sensitive systems,but also sheds light on the stress relaxation-triggered morphological evolution of the wrinkling polyblend films.
基金This work was supported by the National Natural Science Foundation of China[Nos.22177067]Sino-German Mobility Program[No.M-0209]+3 种基金the Shanghai Rising-Star Program[No.20QA1403400]the Key Basic Research Program of Science and Technology Commission of Shanghai Municipality(20JC1415300)This work was also supported the Key Research Project of Zhejiang Laboratory(No.2021PE0AC02)Shanghai Technical Service Center for Advanced Ceramics Structure Design and Precision Manufacturing(No.20DZ2294000).The authors acknowledge the Beijing Super Cloud Computing Center,Hefei Advanced Computing Center,and Shanghai University for providing HPC resources.
文摘In data-driven materials design where the target materials have limited data,the transfer machine learning from large known source materials,becomes a demanding strategy especially across different crystal structures.In this work,we proposed a deep transfer learning approach to predict thermodynamically stable perovskite oxides based on a large computational dataset of spinel oxides.The deep neural network(DNN)source domain model with“Center-Environment”(CE)features was first developed using the formation energy of 5329 spinel oxide structures and then was fine-tuned by learning a small dataset of 855 perovskite oxide structures,leading to a transfer learning model with good transferability in the target domain of perovskite oxides.Based on the transferred model,we further predicted the formation energy of potential 5329 perovskite structures with combination of 73 elements.Combining the criteria of formation energy and structure factors including tolerance factor(0.7<t≤1.1)and octahedron factor(0.45<μ<0.7),we predicted 1314 thermodynamically stable perovskite oxides,among which 144 oxides were reported to be synthesized experimentally,10 oxides were predicted computationally by other literatures,301 oxides were recorded in the Materials Project database,and 859 oxides have been first reported.Combing with the structure-informed features the transfer machine learning approach in this work takes the advantage of existing data to predict new structures at a lower cost,providing an effective acceleration strategy for the expensive high-throughput computational screening in materials design.The predicted stable novel perovskite oxides serve as a rich platform for exploring potential renewable energy and electronic materials applications.
