TiO_(2)is the dominant and most widely researched photocatalyst for environmental remediation,however,the drawbacks,such as only responding to UV light(<5%of sunlight),low charge separation efficiency,and difficult...TiO_(2)is the dominant and most widely researched photocatalyst for environmental remediation,however,the drawbacks,such as only responding to UV light(<5%of sunlight),low charge separation efficiency,and difficulties in recycling,have severely hindered its practical application.Herein,we synthesized magnetically separable Fe_(3)O_(4)@MoS_(2)@mesoporous TiO_(2)(FMmT)photocatalysts via a simple,green,and template-free solvothermal method combined with ultrasonic hydrolysis.It is found that FMmT possesses a high specific surface area(55.09 m2·g−1),enhanced visible-light responsiveness(~521 nm),and remarkable photogenerated charge separation efficiency.In addition,the photocatalytic degradation efficiencies of FMmT for methylene blue(MB),rhodamine B(RhB),and tetracycline(TC)are 99.4%,98.5%,and 89.3%within 300 min,respectively.The corresponding degradation rates are 4.5,4.3,and 3.1 times higher than those of pure TiO_(2)separately.Owing to the high saturation magnetization(43.1 A·m^(2)·kg^(−1)),FMmT can achieve effective recycling with an applied magnetic field.The improved photocatalytic activity is closely related to the effective transport of photogenerated electrons by the active interlayer MoS_(2) and the electron–hole separation caused by the MoS_(2)@TiO_(2)heterojunction.Meanwhile,the excellent light-harvesting ability and abundant reactive sites of the mesoporous TiO_(2)shell further boost the photocatalytic efficiency of FMmT.This work provides a new approach and some experimental basis for the design and performance improvement of magnetic photocatalysts by innovatively incorporating MoS2 as the active interlayer and integrating it with a mesoporous shell.展开更多
Exclusive responsiveness to ultraviolet light (~3.2 eV) and high photogenerated charge recombination rate are the two primary drawbacks of pure TiO_(2). We combined N-doped graphene quantum dots (N-GQDs), morphology r...Exclusive responsiveness to ultraviolet light (~3.2 eV) and high photogenerated charge recombination rate are the two primary drawbacks of pure TiO_(2). We combined N-doped graphene quantum dots (N-GQDs), morphology regulation, and heterojunction construction strategies to synthesize N-GQD/N-doped TiO_(2)/P-doped porous hollow g-C_(3)N_(4) nanotube (PCN) composite photocatalysts (denoted as G-TPCN). The optimal sample (G-TPCN doped with 0.1wt% N-GQD, denoted as 0.1% G-TPCN) exhibits significantly enhanced photoabsorption, which is attributed to the change in bandgap caused by elemental doping (P and N), the improved light-harvesting resulting from the tube structure, and the upconversion effect of N-GQDs. In addition, the internal charge separation and transfer capability of0.1% G-TPCN are dramatically boosted, and its carrier concentration is 3.7, 2.3, and 1.9 times that of N-TiO_(2), PCN, and N-TiO_(2)/PCN(TPCN-1), respectively. This phenomenon is attributed to the formation of Z-scheme heterojunction between N-TiO_(2) and PCNs, the excellent electron conduction ability of N-GQDs, and the short transfer distance caused by the porous nanotube structure. Compared with those of N-TiO_(2), PCNs, and TPCN-1, the H2 production activity of 0.1%G-TPCN under visible light is enhanced by 12.4, 2.3, and 1.4times, respectively, and its ciprofloxacin (CIP) degradation rate is increased by 7.9, 5.7, and 2.9 times, respectively. The optimized performance benefits from excellent photoresponsiveness and improved carrier separation and migration efficiencies. Finally, the photocatalytic mechanism of 0.1% G-TPCN and five possible degradation pathways of CIP are proposed. This study clarifies the mechanism of multiple modification strategies to synergistically improve the photocatalytic performance of 0.1% G-TPCN and provides a potential strategy for rationally designing novel photocatalysts for environmental remediation and solar energy conversion.展开更多
A tremendous amount of data has been generated by global financial markets everyday,and such time-series data needs to be analyzed in real time to explore its potential value.In recent years,we have witnessed the succ...A tremendous amount of data has been generated by global financial markets everyday,and such time-series data needs to be analyzed in real time to explore its potential value.In recent years,we have witnessed the successful adoption of machine learning models on financial data,where the importance of accuracy and timeliness demands highly effective computing frameworks.However,traditional financial time-series data processing frameworks have shown performance degradation and adaptation issues,such as the outlier handling with stock suspension in Pandas and TA-Lib.In this paper,we propose HXPY,a high-performance data processing package with a C++/Python interface for financial time-series data.HXPY supports miscellaneous acceleration techniques such as the streaming algorithm,the vectorization instruction set,and memory optimization,together with various functions such as time window functions,group operations,down-sampling operations,cross-section operations,row-wise or column-wise operations,shape transformations,and alignment functions.The results of benchmark and incremental analysis demonstrate the superior performance of HXPY compared with its counterparts.From MiBs to GiBs data,HXPY significantly outperforms other in-memory dataframe computing rivals even up to hundreds of times.展开更多
基金financially supported by the National Key R & D Projects (Nos. 2021YFC1910504, 2019YFC1907101, 2019YFC1907103, and 2017YFB0702304)the Key R & D Project in Ningxia Hui Autonomous Region, China (No. 2020BCE01001)+6 种基金the Key and Normal Projects National Natural Science Foundation of China (Nos. U2002212 and 51672024)the Xijiang Innovation and Entrepreneurship Team (No. 2017A0109004)the Fundamental Research Funds for the Central Universities (Nos. FRF-BD-20-24A, FRF-TP-20-031A1, FRF-IC-19-017Z, FRF-GF-19-032B, and 06500141)the Integration of Green Key Process Systems MIIT, Natural Science Foundation of Beijing Municipality (No. 2214073)the Guangdong Basic and Applied Research Foundation, China (No. 2020A1515110408)the Foshan Science and Technology Innovation Special Foundation, China (No. BK21BE002)the Postdoctor Research Foundation of Shunde Graduate School of University of Science and Technology Beijing (No. 2020BH004)
文摘TiO_(2)is the dominant and most widely researched photocatalyst for environmental remediation,however,the drawbacks,such as only responding to UV light(<5%of sunlight),low charge separation efficiency,and difficulties in recycling,have severely hindered its practical application.Herein,we synthesized magnetically separable Fe_(3)O_(4)@MoS_(2)@mesoporous TiO_(2)(FMmT)photocatalysts via a simple,green,and template-free solvothermal method combined with ultrasonic hydrolysis.It is found that FMmT possesses a high specific surface area(55.09 m2·g−1),enhanced visible-light responsiveness(~521 nm),and remarkable photogenerated charge separation efficiency.In addition,the photocatalytic degradation efficiencies of FMmT for methylene blue(MB),rhodamine B(RhB),and tetracycline(TC)are 99.4%,98.5%,and 89.3%within 300 min,respectively.The corresponding degradation rates are 4.5,4.3,and 3.1 times higher than those of pure TiO_(2)separately.Owing to the high saturation magnetization(43.1 A·m^(2)·kg^(−1)),FMmT can achieve effective recycling with an applied magnetic field.The improved photocatalytic activity is closely related to the effective transport of photogenerated electrons by the active interlayer MoS_(2) and the electron–hole separation caused by the MoS_(2)@TiO_(2)heterojunction.Meanwhile,the excellent light-harvesting ability and abundant reactive sites of the mesoporous TiO_(2)shell further boost the photocatalytic efficiency of FMmT.This work provides a new approach and some experimental basis for the design and performance improvement of magnetic photocatalysts by innovatively incorporating MoS2 as the active interlayer and integrating it with a mesoporous shell.
基金financially supported by the National Natural Science Foundation of China (Nos.U2002212,52102058,52204414,52204413,and 52204412)the National Key R&D Program of China (Nos.2021YFC1910504,2019YFC1907101,2019YFC1907103,and 2017YFB0702304)+7 种基金the Key R&D Program of Ningxia Hui Autonomous Region,China (Nos.2021BEG01003 and2020BCE01001)the Xijiang Innovation and Entrepreneurship Team,China (No.2017A0109004)the Macao Young Scholars Program (No.AM2022024),Chinathe Beijing Natural Science Foundation (Nos.L212020 and 2214073),Chinathe Guangdong Basic and Applied Basic Research Foundation,China (Nos.2021A1515110998 and 2020A1515110408)the China Postdoctoral Science Foundation (No.2022M710349)the Fundamental Research Funds for the Central Universities,China (Nos.FRF-BD-20-24A,FRF-TP-20-031A1,FRF-IC-19-017Z,and 06500141)the Integration of Green Key Process Systems MIIT and Scientific and Technological Innovation Foundation of Foshan,China(Nos.BK22BE001 and BK21BE002)。
文摘Exclusive responsiveness to ultraviolet light (~3.2 eV) and high photogenerated charge recombination rate are the two primary drawbacks of pure TiO_(2). We combined N-doped graphene quantum dots (N-GQDs), morphology regulation, and heterojunction construction strategies to synthesize N-GQD/N-doped TiO_(2)/P-doped porous hollow g-C_(3)N_(4) nanotube (PCN) composite photocatalysts (denoted as G-TPCN). The optimal sample (G-TPCN doped with 0.1wt% N-GQD, denoted as 0.1% G-TPCN) exhibits significantly enhanced photoabsorption, which is attributed to the change in bandgap caused by elemental doping (P and N), the improved light-harvesting resulting from the tube structure, and the upconversion effect of N-GQDs. In addition, the internal charge separation and transfer capability of0.1% G-TPCN are dramatically boosted, and its carrier concentration is 3.7, 2.3, and 1.9 times that of N-TiO_(2), PCN, and N-TiO_(2)/PCN(TPCN-1), respectively. This phenomenon is attributed to the formation of Z-scheme heterojunction between N-TiO_(2) and PCNs, the excellent electron conduction ability of N-GQDs, and the short transfer distance caused by the porous nanotube structure. Compared with those of N-TiO_(2), PCNs, and TPCN-1, the H2 production activity of 0.1%G-TPCN under visible light is enhanced by 12.4, 2.3, and 1.4times, respectively, and its ciprofloxacin (CIP) degradation rate is increased by 7.9, 5.7, and 2.9 times, respectively. The optimized performance benefits from excellent photoresponsiveness and improved carrier separation and migration efficiencies. Finally, the photocatalytic mechanism of 0.1% G-TPCN and five possible degradation pathways of CIP are proposed. This study clarifies the mechanism of multiple modification strategies to synergistically improve the photocatalytic performance of 0.1% G-TPCN and provides a potential strategy for rationally designing novel photocatalysts for environmental remediation and solar energy conversion.
文摘A tremendous amount of data has been generated by global financial markets everyday,and such time-series data needs to be analyzed in real time to explore its potential value.In recent years,we have witnessed the successful adoption of machine learning models on financial data,where the importance of accuracy and timeliness demands highly effective computing frameworks.However,traditional financial time-series data processing frameworks have shown performance degradation and adaptation issues,such as the outlier handling with stock suspension in Pandas and TA-Lib.In this paper,we propose HXPY,a high-performance data processing package with a C++/Python interface for financial time-series data.HXPY supports miscellaneous acceleration techniques such as the streaming algorithm,the vectorization instruction set,and memory optimization,together with various functions such as time window functions,group operations,down-sampling operations,cross-section operations,row-wise or column-wise operations,shape transformations,and alignment functions.The results of benchmark and incremental analysis demonstrate the superior performance of HXPY compared with its counterparts.From MiBs to GiBs data,HXPY significantly outperforms other in-memory dataframe computing rivals even up to hundreds of times.
