Geometrical configurations play a crucial role in dual-atom catalysts(DACs)for electrocatalytic applications.Significant progress has been made to design DACs electrocatalysts with various geometri-cal configurations,...Geometrical configurations play a crucial role in dual-atom catalysts(DACs)for electrocatalytic applications.Significant progress has been made to design DACs electrocatalysts with various geometri-cal configurations,but in-depth understanding the relationship between geometrical configurations and metal-metal interaction mechanisms for designing targeted DACs is still required.In this review,the recent progress in engineering of geometrical configurations of DACs is systematically summarized.Based on the polarity of geometrical configuration,DACs can be classified into two different types that are homonuclear and heteronuclear DACs.Furthermore,with regard to the geometrical configurations of the active sites,homonuclear DACs are identified into adjacent and bridged configurations,and heteronuclear DACs can be classified into adjacent,bridged,and separated configurations.Subsequently,metal-metal interactions in DACs with different geometrical configurations are introduced.Additionally,the applications of DACs in different electrocatalytic reactions are discussed,including the oxygen reduction reaction(ORR),oxygen evolution reaction(OER),hydrogen evolution reaction(HER),and other catalysis.Finally,the future challenges and perspectives for advancements in DACs are high-lighted.This review aims to provide inspiration for the design of highly effcient DACs towards energy relatedapplications.展开更多
The molybdenum carbide(Mo_(2)C)has been regarded as one of the most cost-efficient and stable electrocatalyst for the hydrogen evolution reaction(HER)by the virtue of its Pt-like electronic structures.However,the inhe...The molybdenum carbide(Mo_(2)C)has been regarded as one of the most cost-efficient and stable electrocatalyst for the hydrogen evolution reaction(HER)by the virtue of its Pt-like electronic structures.However,the inherent limitation of high density of empty valence band significantly reduces its catalytic reactivity by reason of strong hydrogen desorption resistance.Herein,we propose a multiscale confinement synthesis method to design the nitrogen-rich Mo_(2)C for modulating the band structure via decomposing the pre-coordination bonded polymer in a pressure-tight tube sealing system.Pre-bonded c/N-Mo in the coordination precursor constructs a micro-confinement space,enabling the homogeneous nitrogenization in-situ happened during the formation of Mo_(2)C.Simultaneously,the evolved gases from the precursor decomposition in tube sealing system establish a macro-confinement environment,preventing the lattice N escape and further endowing a continuous nitridation.Combining the multiscale confinement effects,the nitrogen-rich Mo2C displays as high as 25%N-Mo concentration in carbide lattice,leading to a satisfactory band structure.Accordingly,the constructed nitrogen-rich Mo_(2)C reveals an adorable catalytic activity for HER in both alkaline and acid solution.It is anticipated that the multiscale confinement synthesis strategy presents guideline for the rational design of electrocatalysts and beyond.展开更多
The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively...The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.展开更多
Selenopeptides may be a valuable bioactive compound to promote gut microbiota-targeted therapeutic methods for intestinal disease and hepatopathy.However,limited information is available on the utilization of selenope...Selenopeptides may be a valuable bioactive compound to promote gut microbiota-targeted therapeutic methods for intestinal disease and hepatopathy.However,limited information is available on the utilization of selenopeptides by gut microbiota,especially Selenium(Se)function.For this purpose,the present study aimed to investigate the protective effect of selenopeptide(RYNA(Se)MNDYT,Se-P2,purity of≥95%)and its original peptide(RYNAMNDYT,P2,purity of≥95%)in vivo by the microbiota-metabolite axis and further analyze the potential contribution of Se biofortification to Se-P2 bioactivity.The results showed that Se-P2 exhibits a higher protective effect on lipopolysaccharide(LPS)-induced inflammation than P2,including pathology of the colon and liver,which suggested that the bioactivity of P2 was promoted by the organic combination of Se.Notably,gut microbiota composition tended to be a healthy structure by Se-P2 pretreatment in LPS-injured mice,which had a positive effect on LPS-induced gut microbiota dysbacteriosis.Additionally,only Se-P2 promoted an increase in the relative abundance of Lactobacillus,Alistipes,and Roseburia and a decrease in the relative abundance of Akkermansia,Erysipelatoclostridium,and Bacteroides in LPS-injured mice.The changes in gut microbiota were obviously correlated with the changes in metabolites and affected the metabolic pathways of valine,leucine,isoleucine,phenylalanine,tyrosine,and tryptophan biosynthesis and phenylalanine metabolism.This may be one of the key reasons for Se-P2 to exert bioactivity through the microbiota-metabolite axis.Furthermore,Se-biofortification in Se-enriched Cordyceps militaris affected the parental proteins of Se-P2 to modulate mitogen-activated protein kinase,GPI anchored protein,and carbohydrate metabolism,translation,folding,sorting and degradation,which may contribute to the bioactivity of Se-P2.Our study provides information on the effect of Se on selenopeptides in vivo,which further promotes the prospective applications of selenopeptides as dietary supplements.展开更多
Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsi...Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsinformedneural network(PINN)is currently the most general framework,which is more popular due to theconvenience of constructing NNs and excellent generalization ability.The automatic differentiation(AD)-basedPINN model is suitable for the homogeneous scientific problem;however,it is unclear how AD can enforce fluxcontinuity across boundaries between cells of different properties where spatial heterogeneity is represented bygrid cells with different physical properties.In this work,we propose a criss-cross physics-informed convolutionalneural network(CC-PINN)learning architecture,aiming to learn the solution of parametric PDEs with spatialheterogeneity of physical properties.To achieve the seamless enforcement of flux continuity and integration ofphysicalmeaning into CNN,a predefined 2D convolutional layer is proposed to accurately express transmissibilitybetween adjacent cells.The efficacy of the proposedmethodwas evaluated through predictions of several petroleumreservoir problems with spatial heterogeneity and compared against state-of-the-art(PINN)through numericalanalysis as a benchmark,which demonstrated the superiority of the proposed method over the PINN.展开更多
基金supported by the Natural Science Foundation of China (22179062,52125202,and U2004209)the Natural Science Foundation of Jiangsu Province (BK20230035)+1 种基金the Fundamental Research Funds for the Central Universities (30922010303)the Intergovernmental Cooperation Projects in the National Key Research and Development Plan of the Ministry of Science and Technology of PRC (2022YFE0196800)
文摘Geometrical configurations play a crucial role in dual-atom catalysts(DACs)for electrocatalytic applications.Significant progress has been made to design DACs electrocatalysts with various geometri-cal configurations,but in-depth understanding the relationship between geometrical configurations and metal-metal interaction mechanisms for designing targeted DACs is still required.In this review,the recent progress in engineering of geometrical configurations of DACs is systematically summarized.Based on the polarity of geometrical configuration,DACs can be classified into two different types that are homonuclear and heteronuclear DACs.Furthermore,with regard to the geometrical configurations of the active sites,homonuclear DACs are identified into adjacent and bridged configurations,and heteronuclear DACs can be classified into adjacent,bridged,and separated configurations.Subsequently,metal-metal interactions in DACs with different geometrical configurations are introduced.Additionally,the applications of DACs in different electrocatalytic reactions are discussed,including the oxygen reduction reaction(ORR),oxygen evolution reaction(OER),hydrogen evolution reaction(HER),and other catalysis.Finally,the future challenges and perspectives for advancements in DACs are high-lighted.This review aims to provide inspiration for the design of highly effcient DACs towards energy relatedapplications.
基金supported by the National Natural Science Foundation of China(52372201,52125202,52202247)the Natural Science Foundation of Jiangsu Province(1192261031693)the Fundamental Research Funds for the Central Universities(30919011110,1191030558)。
文摘The molybdenum carbide(Mo_(2)C)has been regarded as one of the most cost-efficient and stable electrocatalyst for the hydrogen evolution reaction(HER)by the virtue of its Pt-like electronic structures.However,the inherent limitation of high density of empty valence band significantly reduces its catalytic reactivity by reason of strong hydrogen desorption resistance.Herein,we propose a multiscale confinement synthesis method to design the nitrogen-rich Mo_(2)C for modulating the band structure via decomposing the pre-coordination bonded polymer in a pressure-tight tube sealing system.Pre-bonded c/N-Mo in the coordination precursor constructs a micro-confinement space,enabling the homogeneous nitrogenization in-situ happened during the formation of Mo_(2)C.Simultaneously,the evolved gases from the precursor decomposition in tube sealing system establish a macro-confinement environment,preventing the lattice N escape and further endowing a continuous nitridation.Combining the multiscale confinement effects,the nitrogen-rich Mo2C displays as high as 25%N-Mo concentration in carbide lattice,leading to a satisfactory band structure.Accordingly,the constructed nitrogen-rich Mo_(2)C reveals an adorable catalytic activity for HER in both alkaline and acid solution.It is anticipated that the multiscale confinement synthesis strategy presents guideline for the rational design of electrocatalysts and beyond.
基金supported by the National Natural Science Foundation of China (Nos.52274048 and 52374017)Beijing Natural Science Foundation (No.3222037)the CNPC 14th five-year perspective fundamental research project (No.2021DJ2104)。
文摘The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.
基金Guangzhou Basic and Applied Basic Research Project(202201010197)State Key Laboratory of Applied Microbiology Southern China(SKLAM011-2021)+1 种基金National Natural Science Foundation of China(32202014),Guangdong Provincial Key Laboratory(2020B121201009)Guangdong Province Academy of Sciences Special Project for Capacity Building of Innovation Driven Development(2020GDASYL-20200401002).
文摘Selenopeptides may be a valuable bioactive compound to promote gut microbiota-targeted therapeutic methods for intestinal disease and hepatopathy.However,limited information is available on the utilization of selenopeptides by gut microbiota,especially Selenium(Se)function.For this purpose,the present study aimed to investigate the protective effect of selenopeptide(RYNA(Se)MNDYT,Se-P2,purity of≥95%)and its original peptide(RYNAMNDYT,P2,purity of≥95%)in vivo by the microbiota-metabolite axis and further analyze the potential contribution of Se biofortification to Se-P2 bioactivity.The results showed that Se-P2 exhibits a higher protective effect on lipopolysaccharide(LPS)-induced inflammation than P2,including pathology of the colon and liver,which suggested that the bioactivity of P2 was promoted by the organic combination of Se.Notably,gut microbiota composition tended to be a healthy structure by Se-P2 pretreatment in LPS-injured mice,which had a positive effect on LPS-induced gut microbiota dysbacteriosis.Additionally,only Se-P2 promoted an increase in the relative abundance of Lactobacillus,Alistipes,and Roseburia and a decrease in the relative abundance of Akkermansia,Erysipelatoclostridium,and Bacteroides in LPS-injured mice.The changes in gut microbiota were obviously correlated with the changes in metabolites and affected the metabolic pathways of valine,leucine,isoleucine,phenylalanine,tyrosine,and tryptophan biosynthesis and phenylalanine metabolism.This may be one of the key reasons for Se-P2 to exert bioactivity through the microbiota-metabolite axis.Furthermore,Se-biofortification in Se-enriched Cordyceps militaris affected the parental proteins of Se-P2 to modulate mitogen-activated protein kinase,GPI anchored protein,and carbohydrate metabolism,translation,folding,sorting and degradation,which may contribute to the bioactivity of Se-P2.Our study provides information on the effect of Se on selenopeptides in vivo,which further promotes the prospective applications of selenopeptides as dietary supplements.
基金the National Natural Science Foundation of China(No.52274048)Beijing Natural Science Foundation(No.3222037)+1 种基金the CNPC 14th Five-Year Perspective Fundamental Research Project(No.2021DJ2104)the Science Foundation of China University of Petroleum,Beijing(No.2462021YXZZ010).
文摘Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsinformedneural network(PINN)is currently the most general framework,which is more popular due to theconvenience of constructing NNs and excellent generalization ability.The automatic differentiation(AD)-basedPINN model is suitable for the homogeneous scientific problem;however,it is unclear how AD can enforce fluxcontinuity across boundaries between cells of different properties where spatial heterogeneity is represented bygrid cells with different physical properties.In this work,we propose a criss-cross physics-informed convolutionalneural network(CC-PINN)learning architecture,aiming to learn the solution of parametric PDEs with spatialheterogeneity of physical properties.To achieve the seamless enforcement of flux continuity and integration ofphysicalmeaning into CNN,a predefined 2D convolutional layer is proposed to accurately express transmissibilitybetween adjacent cells.The efficacy of the proposedmethodwas evaluated through predictions of several petroleumreservoir problems with spatial heterogeneity and compared against state-of-the-art(PINN)through numericalanalysis as a benchmark,which demonstrated the superiority of the proposed method over the PINN.
