Synthesizing highly efficient,low-toxicity catalysts for the remediation of polycyclic aromatic hydrocarbons(PAHs)contaminated soils is crucial.Nanoscale zero-valent iron(n-ZVI)is widely used in the treatment of pollut...Synthesizing highly efficient,low-toxicity catalysts for the remediation of polycyclic aromatic hydrocarbons(PAHs)contaminated soils is crucial.Nanoscale zero-valent iron(n-ZVI)is widely used in the treatment of pollutants due to its high catalytic activity.However,n-ZVI is prone to aggregation and passivation.Therefore,to design an environmentally friendly,efficient,and practical catalyst material,this study designed a nanoscale zero-valent iron-loaded biochar(BC)polyacrylic acid(PAA)composite materials.Biochar and polyacrylic acid can prevent the ag-gregation of zero-valent iron and provide a large number of functional groups.The iron on the carrier is uniformly distributed,exposing active sites and activating persulfate to remove anthracene(ANT)pollutants from the soil.The BC/PAA/Fe0 system can achieve an anthracene degradation efficiency of 93.7%in soil,and the degradation efficiency of anthracene remains around 90%under both acidic and alkaline con-$$ditions.Free radical capture experiments indicate that the degradation of anthracene proceeds through the radical pathways SO4,$OH,O2 and the non-radical pathway 1O2.In addition,possible degradation pathways for anthracene have been proposed.Plant planting experiments have shown that the catalyst designed in this study has low toxicity and has excellent application prospects in thefield of soil remediation.展开更多
Single-atom catalysts are promising for H_(2)O_(2) photosynthesis from O_(2) and H_(2)O,but their efficiency is still limited by the ill-defined electronic structure.In this study,Co single-atoms with unique four plan...Single-atom catalysts are promising for H_(2)O_(2) photosynthesis from O_(2) and H_(2)O,but their efficiency is still limited by the ill-defined electronic structure.In this study,Co single-atoms with unique four planar N-coordination and one axial P-coordination(Co-N_(4)P_(1))are decorated on the lateral edges of nanorod-like crystalline g-C_(3)N_(4)(CCN)photocatalysts.Significantly,the electronic structures of central Co as active sites for O_(2) reduction reaction(ORR)and planar N-coordinator as active sites for H_(2)O oxidation reaction(WOR)in Co-N_(4)P_(1) can be well regulated by the synergetic effects of introducing axial P-coordinator,in contrast to the decorated Co single-atoms with only four planar N-coordination(Co-N_(4)).Specifically,directional photoelectron accumulation at central Co active sites,induced by an introduced midgap level in Co-N_(4)P_(1),mediates the ORR active sites from 4e–-ORR-selective terminal–NH_(2) sites to 2e–-ORR-selective Co sites,moreover,an elevated d-band center of Co 3d orbital strengthens ORR intermediate*OOH adsorption,thus jointly facilitating a highly selective and active 2e^(–)-ORR pathway to H_(2)O_(2) photosynthesis.Simultaneously,a downshifted p-band center of N_(2)p orbital in Co-N_(4)P_(1) weakens WOR intermediate*OH adsorption,thus enabling a preferable 2e^(–)-WOR pathway toward H_(2)O_(2) photosynthesis.Subsequently,Co-N_(4)P_(1) exhibits exceptional H_(2)O_(2) photosynthesis efficiency,reaching 295.6μmol g^(-1) h^(-1) with a remarkable solar-to-chemical conversion efficiency of 0.32%,which is 15 times that of Co-N_(4)(19.2μmol g^(-1) h^(-1))and 10 times higher than CCN(27.6μmol g^(-1) h^(-1)).This electronic structure modulation on single-atom catalysts offers a promising strategy for boosting the activity and selectivity of H_(2)O_(2) photosynthesis.展开更多
The solution of fractional partial differential equations(PDEs)is an important topic in scientific computing.However,the traditional physics-informed neural networks(PINNs)have problems of memory overflow and low comp...The solution of fractional partial differential equations(PDEs)is an important topic in scientific computing.However,the traditional physics-informed neural networks(PINNs)have problems of memory overflow and low computational efficiency when the derivative is discretized for a long time.Therefore in this paper we innovatively propose a framework of Laplace transform physics-informed neural networks(LT-PINNs),which is dedicated to solving the forward and inverse problems of Caputo-type fractional PDEs.The core of this method is to use the Laplace transform to construct the loss function,which skillfully avoids the dilemma that the fractional derivative operator in traditional PINNs is difficult to operate effectively.By studying the benchmark problem of parameter a in a series of different scenarios we verify that LT-PINNs can predict the solution of Caputo-type fractional PDEs more accurately than fractional PINNs.The excellent performance of LT-PINNs in identifying inverse problems involving fractional order,convection and diffusion coefficients is further explored.At the same time,the effects of network structure,the number of sampling points and noise on the LT-PINNs method are analyzed in detail.The results show that the method can predict the solution of the equation satisfactorily even under severe noise interference.The proposed LT-PINNs framework opens up a new path for efficiently solving fractional PDEs.It shows significant advantages in improving computational efficiency,reducing memory usage and dealing with complex noise environments.It is expected to promote the further development of fractional PDEs in many fields.展开更多
Recently,Li[16]introduced three kinds of single-hidden layer feed-forward neural networks with optimized piecewise linear activation functions and fixed weights,and obtained the upper and lower bound estimations on th...Recently,Li[16]introduced three kinds of single-hidden layer feed-forward neural networks with optimized piecewise linear activation functions and fixed weights,and obtained the upper and lower bound estimations on the approximation accuracy of the FNNs,for continuous function defined on bounded intervals.In the present paper,we point out that there are some errors both in the definitions of the FNNs and in the proof of the upper estimations in[16].By using new methods,we also give right approximation rate estimations of the approximation by Li’s neural networks.展开更多
基金support provided by the National Natural Science Foundation of China(22478267,22438009,U24A20535)Basic Research Program of Jiangsu province(BK20243002)+1 种基金Prospective Application Research Project of Suzhou(SYC2022042)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Synthesizing highly efficient,low-toxicity catalysts for the remediation of polycyclic aromatic hydrocarbons(PAHs)contaminated soils is crucial.Nanoscale zero-valent iron(n-ZVI)is widely used in the treatment of pollutants due to its high catalytic activity.However,n-ZVI is prone to aggregation and passivation.Therefore,to design an environmentally friendly,efficient,and practical catalyst material,this study designed a nanoscale zero-valent iron-loaded biochar(BC)polyacrylic acid(PAA)composite materials.Biochar and polyacrylic acid can prevent the ag-gregation of zero-valent iron and provide a large number of functional groups.The iron on the carrier is uniformly distributed,exposing active sites and activating persulfate to remove anthracene(ANT)pollutants from the soil.The BC/PAA/Fe0 system can achieve an anthracene degradation efficiency of 93.7%in soil,and the degradation efficiency of anthracene remains around 90%under both acidic and alkaline con-$$ditions.Free radical capture experiments indicate that the degradation of anthracene proceeds through the radical pathways SO4,$OH,O2 and the non-radical pathway 1O2.In addition,possible degradation pathways for anthracene have been proposed.Plant planting experiments have shown that the catalyst designed in this study has low toxicity and has excellent application prospects in thefield of soil remediation.
文摘Single-atom catalysts are promising for H_(2)O_(2) photosynthesis from O_(2) and H_(2)O,but their efficiency is still limited by the ill-defined electronic structure.In this study,Co single-atoms with unique four planar N-coordination and one axial P-coordination(Co-N_(4)P_(1))are decorated on the lateral edges of nanorod-like crystalline g-C_(3)N_(4)(CCN)photocatalysts.Significantly,the electronic structures of central Co as active sites for O_(2) reduction reaction(ORR)and planar N-coordinator as active sites for H_(2)O oxidation reaction(WOR)in Co-N_(4)P_(1) can be well regulated by the synergetic effects of introducing axial P-coordinator,in contrast to the decorated Co single-atoms with only four planar N-coordination(Co-N_(4)).Specifically,directional photoelectron accumulation at central Co active sites,induced by an introduced midgap level in Co-N_(4)P_(1),mediates the ORR active sites from 4e–-ORR-selective terminal–NH_(2) sites to 2e–-ORR-selective Co sites,moreover,an elevated d-band center of Co 3d orbital strengthens ORR intermediate*OOH adsorption,thus jointly facilitating a highly selective and active 2e^(–)-ORR pathway to H_(2)O_(2) photosynthesis.Simultaneously,a downshifted p-band center of N_(2)p orbital in Co-N_(4)P_(1) weakens WOR intermediate*OH adsorption,thus enabling a preferable 2e^(–)-WOR pathway toward H_(2)O_(2) photosynthesis.Subsequently,Co-N_(4)P_(1) exhibits exceptional H_(2)O_(2) photosynthesis efficiency,reaching 295.6μmol g^(-1) h^(-1) with a remarkable solar-to-chemical conversion efficiency of 0.32%,which is 15 times that of Co-N_(4)(19.2μmol g^(-1) h^(-1))and 10 times higher than CCN(27.6μmol g^(-1) h^(-1)).This electronic structure modulation on single-atom catalysts offers a promising strategy for boosting the activity and selectivity of H_(2)O_(2) photosynthesis.
基金funded by the National Natural Science Foundation of China(Grant No.12061055)the Key Projects of the Natural Science Foundation of Ningxia Hui Autonomous Region of China(Grant No.2022AAC02005)the Scientific and Technological Innovation Leading Talent Project of Ningxia Hui Autonomous Region of China(Grant No.2021GKLRLX06)。
文摘The solution of fractional partial differential equations(PDEs)is an important topic in scientific computing.However,the traditional physics-informed neural networks(PINNs)have problems of memory overflow and low computational efficiency when the derivative is discretized for a long time.Therefore in this paper we innovatively propose a framework of Laplace transform physics-informed neural networks(LT-PINNs),which is dedicated to solving the forward and inverse problems of Caputo-type fractional PDEs.The core of this method is to use the Laplace transform to construct the loss function,which skillfully avoids the dilemma that the fractional derivative operator in traditional PINNs is difficult to operate effectively.By studying the benchmark problem of parameter a in a series of different scenarios we verify that LT-PINNs can predict the solution of Caputo-type fractional PDEs more accurately than fractional PINNs.The excellent performance of LT-PINNs in identifying inverse problems involving fractional order,convection and diffusion coefficients is further explored.At the same time,the effects of network structure,the number of sampling points and noise on the LT-PINNs method are analyzed in detail.The results show that the method can predict the solution of the equation satisfactorily even under severe noise interference.The proposed LT-PINNs framework opens up a new path for efficiently solving fractional PDEs.It shows significant advantages in improving computational efficiency,reducing memory usage and dealing with complex noise environments.It is expected to promote the further development of fractional PDEs in many fields.
文摘Recently,Li[16]introduced three kinds of single-hidden layer feed-forward neural networks with optimized piecewise linear activation functions and fixed weights,and obtained the upper and lower bound estimations on the approximation accuracy of the FNNs,for continuous function defined on bounded intervals.In the present paper,we point out that there are some errors both in the definitions of the FNNs and in the proof of the upper estimations in[16].By using new methods,we also give right approximation rate estimations of the approximation by Li’s neural networks.
