Although the size effects of a filler are closely related to the complex multi-level structures of their polymer composites;unfortunately,such relationships remain poorly understood.In this study,we investigated the e...Although the size effects of a filler are closely related to the complex multi-level structures of their polymer composites;unfortunately,such relationships remain poorly understood.In this study,we investigated the effects of various sizes(40-600 nm)of silicon carbide(SiC)fillers on the wear behavior of ultrahigh molecular weight polyethylene(UHMWPE)in the presence of the silane coupling agent KH-560.All of these SiC fillers improved the wear resistance of UHMWPE significantly,with a medium size(150 nm)being optimal.To examine the reasons for this behavior,we analyzed the multi-level structures of the samples in terms of their matrix structures(crystalline;amorphous;interphase),matrix-filler interactions(physical adsorption;chemical crosslinking;hybrid network)and the external effects of SiC fillers(bearing loads;transferring frictional heat).The high rigidity and thermal conductivity of SiC fillers and,more importantly,the intrinsic characteristics of the matrix structures(larger crystal grains;higher interphase;stronger amorphous entangled networks)were the key parameters affecting the enhancement in the wear-resistance of the UHMWPE.Herein,we also provide interpretations of the corresponding physical effects.Our results should improve our understanding of the structure-property relationships and,thus,should guide the formula design of UHMWPE composites.展开更多
Accurate crime prediction is crucial for the proactive allocation of law enforcement resources and ensuring urban safety.A major challenge in achieving accurate predictions lies in identifying generalized patterns of ...Accurate crime prediction is crucial for the proactive allocation of law enforcement resources and ensuring urban safety.A major challenge in achieving accurate predictions lies in identifying generalized patterns of criminal behavior from spatiotemporal features in crime data.Additionally,the inherent randomness and volatility of crime data at the spatiotemporal level introduce noise,which can mislead prediction models.While many effective spatiotemporal crime prediction methods have been proposed,most overlook this issue,reducing their ability to generalize.In this paper,we introduce a novel deep learning-based model,adaptive-GCNLSTM(Ada-GCNLSTM).Specifcally,in the spatial feature extraction module,we enhance the model's ability to capture crime spatial distributions by leveraging graph convolutional networks to model spatial dependencies in conjunction with the maximum mean discrepancy to extract the universal features of crime data.We then incorporate a memory network based on long short-term memory network to capture the underlying relation-ships between temporal features.Through extensive experiments,our model demonstrates an average improvement of 11.7%in mean absolute error and 2.7%in root mean squared error across the three datasets,outperforming the best baseline model.These results underscore the effectiveness of our approach in enhancing crime prediction accuracy.展开更多
Enhancing the selectivity of noble metal catalysts through electronic modulation is important for academic research and chemical industrial processes.Herein,we report a facile sacrificial template strategy for the syn...Enhancing the selectivity of noble metal catalysts through electronic modulation is important for academic research and chemical industrial processes.Herein,we report a facile sacrificial template strategy for the synthesis of PdZn intermetallic compound(3-4 nm)highly distributed in ZnO/nitrogen-decorated carbon hollow spheres(PdZn-ZnO/NCHS)to optimize the selectivity of Pd catalysts,which involves carbonization of a core-shell structured polystyrene(PS)@ZIF-8 precursor in an inert atmosphere,impregnation Pd precursor,and subsequent H2 reduction treatment.Due to the unique structural and compositional features,the developed PdZn-ZnO/NCHS delivers an excellent catalytic performance for the semihydrogenation of 2-methyl-3-butyn-2-ol(MBY)to 2-methyl-3-buten-2-ol(MBE)with high activity(>99%),high selectivity(96%),and good recyclability,outperforming the analog Pd on ZnO(Pd/ZnO)as well as the supported Pd nanoparticles(Pd/C and Pd/NC).Density functional theory(DFT)calculations reveal that the presence of Znδ+species in PdZn-ZnO/NCHS alters the adsorption modes of reactant and product,leading to a decrease of the adsorption strength and an enhancement of the energy barrier for overhydrogenation,which results in a kinetic favor for the selective transformation of MBY to MBE.In addition,PdZn-ZnO/NCHS was also very effective for the partial hydrogenation of dehydrolinalool to hydrolinalool.展开更多
基金financially supported by the National Natural Science Foundation of China(Grants 21878089 and 21476085)National Key R&D Program of China(2016YFB0302201)the Fundamental Research Funds for the Central Universities(222201717025)。
文摘Although the size effects of a filler are closely related to the complex multi-level structures of their polymer composites;unfortunately,such relationships remain poorly understood.In this study,we investigated the effects of various sizes(40-600 nm)of silicon carbide(SiC)fillers on the wear behavior of ultrahigh molecular weight polyethylene(UHMWPE)in the presence of the silane coupling agent KH-560.All of these SiC fillers improved the wear resistance of UHMWPE significantly,with a medium size(150 nm)being optimal.To examine the reasons for this behavior,we analyzed the multi-level structures of the samples in terms of their matrix structures(crystalline;amorphous;interphase),matrix-filler interactions(physical adsorption;chemical crosslinking;hybrid network)and the external effects of SiC fillers(bearing loads;transferring frictional heat).The high rigidity and thermal conductivity of SiC fillers and,more importantly,the intrinsic characteristics of the matrix structures(larger crystal grains;higher interphase;stronger amorphous entangled networks)were the key parameters affecting the enhancement in the wear-resistance of the UHMWPE.Herein,we also provide interpretations of the corresponding physical effects.Our results should improve our understanding of the structure-property relationships and,thus,should guide the formula design of UHMWPE composites.
基金supported by the Top-Notch Innovative Talent Cultivation Project at the People's Public Security University of China(2022yjsky012)the Fundamental Research Business Funding Project of the People's Public Security University of China(2024JKF04).
文摘Accurate crime prediction is crucial for the proactive allocation of law enforcement resources and ensuring urban safety.A major challenge in achieving accurate predictions lies in identifying generalized patterns of criminal behavior from spatiotemporal features in crime data.Additionally,the inherent randomness and volatility of crime data at the spatiotemporal level introduce noise,which can mislead prediction models.While many effective spatiotemporal crime prediction methods have been proposed,most overlook this issue,reducing their ability to generalize.In this paper,we introduce a novel deep learning-based model,adaptive-GCNLSTM(Ada-GCNLSTM).Specifcally,in the spatial feature extraction module,we enhance the model's ability to capture crime spatial distributions by leveraging graph convolutional networks to model spatial dependencies in conjunction with the maximum mean discrepancy to extract the universal features of crime data.We then incorporate a memory network based on long short-term memory network to capture the underlying relation-ships between temporal features.Through extensive experiments,our model demonstrates an average improvement of 11.7%in mean absolute error and 2.7%in root mean squared error across the three datasets,outperforming the best baseline model.These results underscore the effectiveness of our approach in enhancing crime prediction accuracy.
基金We thank the financial supports from the National Natural Science Foundation of China(No.21576243)the Natural Science Foundation of Zhejiang Province(Nos.LY18B060006,LY17B060001,and LY21B030003).
文摘Enhancing the selectivity of noble metal catalysts through electronic modulation is important for academic research and chemical industrial processes.Herein,we report a facile sacrificial template strategy for the synthesis of PdZn intermetallic compound(3-4 nm)highly distributed in ZnO/nitrogen-decorated carbon hollow spheres(PdZn-ZnO/NCHS)to optimize the selectivity of Pd catalysts,which involves carbonization of a core-shell structured polystyrene(PS)@ZIF-8 precursor in an inert atmosphere,impregnation Pd precursor,and subsequent H2 reduction treatment.Due to the unique structural and compositional features,the developed PdZn-ZnO/NCHS delivers an excellent catalytic performance for the semihydrogenation of 2-methyl-3-butyn-2-ol(MBY)to 2-methyl-3-buten-2-ol(MBE)with high activity(>99%),high selectivity(96%),and good recyclability,outperforming the analog Pd on ZnO(Pd/ZnO)as well as the supported Pd nanoparticles(Pd/C and Pd/NC).Density functional theory(DFT)calculations reveal that the presence of Znδ+species in PdZn-ZnO/NCHS alters the adsorption modes of reactant and product,leading to a decrease of the adsorption strength and an enhancement of the energy barrier for overhydrogenation,which results in a kinetic favor for the selective transformation of MBY to MBE.In addition,PdZn-ZnO/NCHS was also very effective for the partial hydrogenation of dehydrolinalool to hydrolinalool.
