The preparation of carbon-based electromagnetic wave(EMW)absorbers possessing thin matching thickness,wide absorption bandwidth,strong absorption intensity,and low filling ratio remains a huge challenge.Metal-organic ...The preparation of carbon-based electromagnetic wave(EMW)absorbers possessing thin matching thickness,wide absorption bandwidth,strong absorption intensity,and low filling ratio remains a huge challenge.Metal-organic frameworks(MOFs)are ideal self-sacrificing templates for the construction of carbon-based EMW absorbers.In this work,bimetallic FeMn-MOF-derived MnFe_(2)O_(4)/C/graphene composites were fabricated via a two-step route of solvothermal reaction and the following pyrolysis treatment.The results re-veal the evolution of the microscopic morphology of carbon skeletons from loofah-like to octahedral and then to polyhedron and pomegran-ate after the adjustment of the Fe^(3+)to Mn^(2+)molar ratio.Furthermore,at the Fe^(3+)to Mn^(2+)molar ratio of 2:1,the obtained MnFe_(2)O_(4)/C/graphene composite exhibited the highest EMW absorption capacity.Specifically,a minimum reflection loss of-72.7 dB and a max-imum effective absorption bandwidth of 5.1 GHz were achieved at a low filling ratio of 10wt%.In addition,the possible EMW absorp-tion mechanism of MnFe_(2)O_(4)/C/graphene composites was proposed.Therefore,the results of this work will contribute to the construction of broadband and efficient carbon-based EMW absorbers derived from MOFs.展开更多
A thickness-controllable method for preparing metal-organic framework hollow nanofiowers on magnetic cores(Fe_(3)O_(4)@MOFs HFs)was demonstrated for the first time.The petal of magnetic core with hollow nanofiower str...A thickness-controllable method for preparing metal-organic framework hollow nanofiowers on magnetic cores(Fe_(3)O_(4)@MOFs HFs)was demonstrated for the first time.The petal of magnetic core with hollow nanofiower structure served as medium for assembling Ui O-66-NH_(2)shell with different thickness.To further improve its performance,Zr^(4+)was immobilized on the surface of Fe_(3)O_(4)@Ui O-66-NH_(2).Compared with conventional Fe_(3)O_(4)@Ui O-66-NH_(2)-Zr^(4+)nanospheres,the Fe_(3)O_(4)@Ui O-66-NH2-Zr4+HFs showed increased enrichment performance for phosphopeptides.The Fe_(3)O_(4)@Ui O-66-NH2-Zr4+HFs served as an attractive restricted-access adsorption material exhibited good selectivity(m_(β-casein):m_(BSA)=1:1000),high sensitivity(1.0 fmol)and excellent size-exclusion effect(m)((β-casein digests):m_(BSA)=1:200).Furthermore,the Fe_(3)O_(4)@Ui O-66-NH_(2)-Zr^(4+)HFs was successfully applied to the specific capture of ultratrace phosphopeptide from complex biological samples,revealing the great potential for the identification and analysis of trace phosphopeptides in clinical analysis.This work can be easily extended to the fabrication of diverse mag-MOF HFs with multifunctional and easy to post-modify properties,and open up a new avenue for the design and construction of new MOFs material.展开更多
Carbon-based sponge materials have attracted massive attention as electromagnetic wave(EMW)absorber candidates due to their lightweight and excellent electromagnetic(EM)attenuation capability.However,the high cost,com...Carbon-based sponge materials have attracted massive attention as electromagnetic wave(EMW)absorber candidates due to their lightweight and excellent electromagnetic(EM)attenuation capability.However,the high cost,complex fabrication process and limited EMW absorption bandwidth restrict their application.Herein,a hierarchical threedimensional(3D)cotton derived carbon fibers coated with core–shell structure Fe Ni nanoparticle@porous carbon(CCF/Fe Ni@PC)from MOF precursor were successfully constructed by coprecipitation and one-step pyrolysis methods.By regulating carbonization temperature,optimizing the electromagnetic parameters,outstanding electromagnetic absorption(EMA)performance was achieved.The CCF@Fe Ni/PC synthesized at 900℃demonstrates a-64.5 d B minimum reflection loss(RL_(min))at 15.39 GHz,and the effective absorption bandwidth(EAB)is 5.08 GHz when the thickness is only1.9 mm.Notably,the maximum EAB(EAB_(max))was extended8.18 GHz at 2.96 mm for Fe Ni-800.The excellent microwave absorption performance is attributed to the synergistic effect of enhanced dielectric loss and magnetic loss,good impedance matching as well as hierarchical multiple scattering and reflection of EMWs.The obtained CCF@Fe Ni/PC composites provide a novel and promising strategy for constructing lightweight low-cost efficient microwave absorption materials.展开更多
Bridge networks are essential components of civil infrastructure,supporting communities by delivering vital services and facilitating economic activities.However,bridges are vulnerable to natural disasters,particularl...Bridge networks are essential components of civil infrastructure,supporting communities by delivering vital services and facilitating economic activities.However,bridges are vulnerable to natural disasters,particularly earthquakes.To develop an effective disaster management strategy,it is critical to identify reliable,robust,and efficient indicators.In this regard,Life-Cycle Cost(LCC)and Resilience(R)serve as key indicators to assist decision-makers in selecting the most effective disaster risk reduction plans.This study proposes an innova-tive LCC-R optimization framework to identify the most optimal retrofit strategies for bridge networks facing hazardous events during their lifespan.The proposed framework employs both single-and multi-objective opti-mization techniques to identify retrofit strategies that maximize the R index while minimizing the LCC for the under-study bridge networks.The considered retrofit strategies include various options such as different mate-rials(steel,CFRP,and GFRP),thicknesses,arrangements,and timing of retrofitting actions.The first step in the proposed framework involves constructing fragility curves by performing a series of nonlinear time-history incre-mental dynamic analyses for each case.In the subsequent step,the seismic resilience surfaces are calculated using the obtained fragility curves and assuming a recovery function.Next,the LCC is evaluated according to the pro-posed formulation for multiple seismic occurrences,which incorporates the effects of complete and incomplete repair actions resulting from previous multiple seismic events.For optimization purposes,the Non-Dominated Sorting Genetic Algorithm II(NSGA-II)evolutionary algorithm efficiently identifies the Pareto front to represent the optimal set of solutions.The study presents the most effective retrofit strategies for an illustrative bridge network,providing a comprehensive discussion and insights into the resulting tactical approaches.The findings underscore that the methodologies employed lead to logical and actionable retrofit strategies,paving the way for enhanced resilience and cost-effectiveness in bridge network management against seismic hazards.展开更多
While single-ion conducting solid polymer electrolytes(SPE)offer inherent safety advantages for lithium metal batteries,their practical implementation is hindered by inadequate ionic transport and unstable electrode i...While single-ion conducting solid polymer electrolytes(SPE)offer inherent safety advantages for lithium metal batteries,their practical implementation is hindered by inadequate ionic transport and unstable electrode interfaces.We presented a breakthrough single-ion SPE system through molecular engineering of lithium-enriched porous aromatic frameworks(PAF-228-Li)integrated with poly(ethylene oxide),achieving exceptional ion conduction and interfacial stability.The precisely designed PAF-228-Li architecture established continuous 3D Li+transport pathways through its intrinsic porous channels and surface conduction sites,synergistically enhanced by PVDF-HFP nanofiber networks.This unique design enabled record-high ionic conductivities of 5.48×10^(-4)S cm^(-1)at 30℃and 3.4×10^(-4)S cm^(-1)at-20℃—among the best reported for PEO-based SPEs.The electrolyte spontaneously formed a selflimiting SEI rich in LiF/Li3N,effectively suppressing dendritic growth while enabling unprecedented cycling stability:Li||LiFePO4cells maintain 83.7%capacity over 700 cycles(30℃)and deliver 104.4 mA h g^(-1)after 1100 cycles at-20℃.Practical viability is further demonstrated in pouch cell configurations with stable operation at 30℃.This work pioneers a materials paradigm combining porous framework engineering with polymer electrolyte design,establishing new researching direction for low-temperature compatible,dendrite-resistant solid-state batteries.展开更多
Metal–organic frameworks(MOFs)hold great potential for gas separation and storage,and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering n...Metal–organic frameworks(MOFs)hold great potential for gas separation and storage,and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering new materials.Unlike traditional molecular graphs,crystal graphs require consideration of periodic invariance and modes.In addition,MOF structures such as covalent bonds,functional groups,and global structures impact adsorption performance in different ways.However,redundant atomic interactions can disrupt training accuracy,potentially leading to overfitting.In this paper,we propose a multi-scale crystal graph for describing periodic crystal structures,modeling interatomic interactions at different scales while preserving periodicity invariance.We also propose a multi-head attention crystal graph network in multi-scale graphs(MHACGN-MS),which learns structural characteristics by focusing on interatomic interactions at different scales,thereby reducing interference from redundant interactions.Using MOF adsorption for gases as an example,we demonstrate that MHACGN-MS outperforms traditional graph neural networks in predicting multi-component gas adsorption.We also visualize attention scores to validate effective learning and demonstrate the model’s interpretability.展开更多
Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation.However,existing detection methods often struggle with challenges such as com...Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation.However,existing detection methods often struggle with challenges such as complex defect morphology,texture similarity,and fuzzy edges,leading to poor accuracy and missed detections.In order to resolve these problems,we propose MSCM-Net(Multi-Scale Cross-Modal Network),a multiscale cross-modal framework focused on detecting rail surface defects.MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps,effectively capturing and enhancing features at different scales for each modality.To further enrich feature representation and improve edge detection in blurred areas,we propose a multi-scale void fusion module that integrates multi-scale feature information.To improve cross-modal feature fusion,we develop a cross-enhanced fusion module that transfers fused features between layers to incorporate interlayer information.We also introduce a multimodal feature integration module,which merges modality-specific features from separate decoders into a shared decoder,enhancing detection by leveraging richer complementary information.Finally,we validate MSCM-Net on the NEU RSDDS-AUG RGB-depth dataset,comparing it against 12 leading methods,and the results show that MSCM-Net achieves superior performance on all metrics.展开更多
The arbitrary discharge of tetracycline(TC)residuals has seriously influenced the ecosystem and human health.Laccase(Lac)-based biodegradation technology is considered a more effective way to remove TC due to its high...The arbitrary discharge of tetracycline(TC)residuals has seriously influenced the ecosystem and human health.Laccase(Lac)-based biodegradation technology is considered a more effective way to remove TC due to its high catalytic efficiency and less by-product.Nevertheless,free Lac suffers from poor stability,easy inactivation and difficult recovery,restricting its application.Immobilization of Lac is considered an efficient strategy for addressing these obstacles.In this study,a magnetic metal-organic framework of Fe_(3)O_(4)@SiO_(2)@UiO-66-NH_(2)(MMOF)was prepared and used as a carrier to immobilize Lac(Lac@MMOF)for TC degradation.Benefiting from the multiple binding sites,adsorption,and protection effect of MMOF,Lac@MMOF displayed a wider pH application range(2–7)and better thermal(15–85℃),repeatability,and storage stability than free Lac.Furthermore,owing to the synergism of MOF adsorption and Lac biocatalysis,the removal rate of Lac@MMOF for TC could be up to 98%at pH=7 within 1 hr,which was 1.29 and 1.24 times that of free Lac and MMOF,respectively.More importantly,Lac@MMOF could easily be separated from aqueous solution under a magnetic field and maintained good removal performance(80%)after five cycles.The degradation products were identified by applying LC-MS/MS,and possible degradation mechanisms and pathways were proposed.Finally,the antibacterial activity of intermediate products was evaluated using Escherichia coli,which revealed that the toxicity of TC was reduced effectively by the degradation of Lac@MMOF.Overall,Lac@MMOF is a green alternative for residual antibiotic removal in water.展开更多
Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or items.Collaborative filtering(CF)is a widely used personalization technique that lever...Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or items.Collaborative filtering(CF)is a widely used personalization technique that leverages user-item interactions to generate recommendations.However,it struggles with challenges like the cold-start problem,scalability issues,and data sparsity.To address these limitations,we develop a Graph Convolutional Networks(GCNs)model that captures the complex network of interactions between users and items,identifying subtle patterns that traditional methods may overlook.We integrate this GCNs model into a federated learning(FL)framework,enabling themodel to learn fromdecentralized datasets.This not only significantly enhances user privacy—a significant improvement over conventionalmodels but also reassures users about the safety of their data.Additionally,by securely incorporating demographic information,our approach further personalizes recommendations and mitigates the coldstart issue without compromising user data.We validate our RSs model using the openMovieLens dataset and evaluate its performance across six key metrics:Precision,Recall,Area Under the Receiver Operating Characteristic Curve(ROC-AUC),F1 Score,Normalized Discounted Cumulative Gain(NDCG),and Mean Reciprocal Rank(MRR).The experimental results demonstrate significant enhancements in recommendation quality,underscoring that combining GCNs with CF in a federated setting provides a transformative solution for advanced recommendation systems.展开更多
Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectu...Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,etc.This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest path.Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives.To address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective framework.The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives.The search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing methods.The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness.The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions.These adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network conditions.The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation.The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques.The proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended network lifetime.Furthermore,analysis using p-values obtained from multiple performance measures(p-values<0.05)showed that the proposed approach outperforms with a high level of confidence.The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs.It allows stable performance in networks with 100 to 300 nodes,under varying node densities,and across different base station placements.Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use.展开更多
The metal triazole(MTA)-based MOFs were found to preferentially adsorb O-rich species,which had enhanced electrocatalytic oxygen reduction reactions(ORR)and stabilized the O-containing species during the discharge and...The metal triazole(MTA)-based MOFs were found to preferentially adsorb O-rich species,which had enhanced electrocatalytic oxygen reduction reactions(ORR)and stabilized the O-containing species during the discharge and charge processes in Li-O_(2)battery.However,the MOFs exhibited low electron conductivity and poor electron transfer interface in the electrocatalysis,limiting the electrocatalytic activity.To address this issue,a nanocomposite with the Co-MTA-coated carbon nano tubes(Co-MTA-C)was constructed,which formed the three-dimensional conductivity network connected with the intersecting carbon nano tube(CNT).In this composite,the electron-rich Co-MTA interacted with the highly conductive CNT,resulting in a charge redistribution.Optimized the electronic structure of the Co center through compositional modifications presented a high valence compared to the pure MOFs.In situ X-ray absorption spectroscopy revealed a direct reaction of Co sites with intermediates such as LiO_(x),leading to the formation of nanosheet array discharge products.The battery based on optimized CoMTA-C demonstrated fast kinetics and superior stability,with a low overpotential of 1.13 V,high specific capacity of 9057 mAh g^(-1),and long-term durability of 600 cycles.It provides a facile and effective strategy for enhancing the electrocatalytic performance through rational tuning of high-conductivity substances.展开更多
Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programm...Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.展开更多
Mixed-lanthanide(Ln) luminescent materials have important application values in ratiometric temperature sensing.Hydrogen-bonded organic frameworks(HOFs) offer a self-supporting network to conveniently tune multiple Ln...Mixed-lanthanide(Ln) luminescent materials have important application values in ratiometric temperature sensing.Hydrogen-bonded organic frameworks(HOFs) offer a self-supporting network to conveniently tune multiple Ln^(3+)ions,but challenges still exist in material design.In this work,we selected two simple organic molecules as hydrogen-bonded building blocks,which are melamine(MA) and 2,5-pyridinedicarboxylic acid(PDC) owning the suitable energy to sensitize Tb^(3+)and Eu^(3+)ions.MA assists PDC molecules to support the network in a new HOF(MA-PDC),where the abundant carboxylic groups enable Ln^(3+)ions to combine with the HOF.Based on the effective energy transfer process,the emission of Tb^(3+)and Eu^(3+)from TbxEu1-x@MA-PDC(x=0.75,0.85,0.90) shows the obvious temperature dependence,which benefits ratiometric temperature detection.Taking Tb0.85Eu0.15@MA-PDC as an example,when temperature varies from 303 to 443 K,the intensity ratio exhibits distinct S-type response based on Mott-Seitz model.It also behaves good relative sensitivity(1.03%/K at 383 K),low temperature resolution(0.059 K) and large reproducibility(>96%).This work demonstrates that functionalization of a simple-component HOF with mixed Ln^(3+)ions is a fantastic strategy to develop novel ratiometric thermometers of both low cost and good performance.展开更多
The advancement of Internet of Things(IoT)technology is driving industries toward intelligent digital transformation,highlighting the crucial role of software engineering.Despite this,the integration of software engin...The advancement of Internet of Things(IoT)technology is driving industries toward intelligent digital transformation,highlighting the crucial role of software engineering.Despite this,the integration of software engineering into IoT engineering education remains underexplored.To address this gap,the School of Software at North University of China,in collaboration with QST Innovation Technology Group Co.,Ltd.(QST),has developed an innovative educational mechanism.This initiative focuses on the software engineering IoT track and optimizes the teaching process through the outcome-based education(OBE)concept.It incorporates military-industrial characteristics,introduces advanced information and technology curricula,and enhances laboratory infrastructure.The goal is to cultivate innovative talents with unique capabilities,thereby fostering the comprehensive development and application of IoT technology.展开更多
Organophosphorus pesticides(OPPs)in foods pose a serious threat to human health,motivating the development of novel analytical methods for their rapid detection and quantification.A magnetic covalent organic framework...Organophosphorus pesticides(OPPs)in foods pose a serious threat to human health,motivating the development of novel analytical methods for their rapid detection and quantification.A magnetic covalent organic framework(M-COF)adsorbent for the magnetic solid-phase extraction(MSPE)of OPPs from foods was reported.M-COF was synthesized by the Schiff base condensation reaction of 1,3,5-tris(4-aminophenyl)benzene and 4,4-biphenyldicarboxaldehyde on the surface of amino-functionalized magnetic nanoparticles.Density functional theory(DFT)calculations showed that adsorption of OPPs onto the surface of M-COF involved hydrophobic effects,van der Waals interactions,π-πinteractions,halogen-N bonding,and hydrogen bonding.Combined with gas chromatography-mass spectrometry(GC-MS)technology,the MSPE method features low limits of detection for OPPs(0.002-0.015μg/L),good reproducibility(1.45%-6.14%),wide linear detection range(0.01-1μg/L,R≥0.9935),and satisfactory recoveries(87.3%-110.4%).The method was successfully applied for the trace analysis of OPPs in spiked fruit juices.展开更多
基金supported by the Natural Science Research Project of the Anhui Educational Committee,China(No.2022AH050827)the Open Research Fund Program of Anhui Province Key Laboratory of Specialty Polymers,Anhui University of Science and Technology,China(No.AHKLSP23-12)the Joint National-Local Engineering Research Center for Safe and Precise Coal Mining Fund,China(No.EC2022020)。
文摘The preparation of carbon-based electromagnetic wave(EMW)absorbers possessing thin matching thickness,wide absorption bandwidth,strong absorption intensity,and low filling ratio remains a huge challenge.Metal-organic frameworks(MOFs)are ideal self-sacrificing templates for the construction of carbon-based EMW absorbers.In this work,bimetallic FeMn-MOF-derived MnFe_(2)O_(4)/C/graphene composites were fabricated via a two-step route of solvothermal reaction and the following pyrolysis treatment.The results re-veal the evolution of the microscopic morphology of carbon skeletons from loofah-like to octahedral and then to polyhedron and pomegran-ate after the adjustment of the Fe^(3+)to Mn^(2+)molar ratio.Furthermore,at the Fe^(3+)to Mn^(2+)molar ratio of 2:1,the obtained MnFe_(2)O_(4)/C/graphene composite exhibited the highest EMW absorption capacity.Specifically,a minimum reflection loss of-72.7 dB and a max-imum effective absorption bandwidth of 5.1 GHz were achieved at a low filling ratio of 10wt%.In addition,the possible EMW absorp-tion mechanism of MnFe_(2)O_(4)/C/graphene composites was proposed.Therefore,the results of this work will contribute to the construction of broadband and efficient carbon-based EMW absorbers derived from MOFs.
基金sponsored by the National Natural Science Foundation of China (Nos. 22106038, 22204171 and 22076038)the Henan Provincial Science and Technology Research Project (No. 232102310112)+2 种基金the China Postdoctoral Science Foundation (No. 2022M713299)Natural Science Foundation of Henan Province, China (No. 202300410044)Henan key scientific research programs to Universities and Colleges (No. 22ZX003)。
文摘A thickness-controllable method for preparing metal-organic framework hollow nanofiowers on magnetic cores(Fe_(3)O_(4)@MOFs HFs)was demonstrated for the first time.The petal of magnetic core with hollow nanofiower structure served as medium for assembling Ui O-66-NH_(2)shell with different thickness.To further improve its performance,Zr^(4+)was immobilized on the surface of Fe_(3)O_(4)@Ui O-66-NH_(2).Compared with conventional Fe_(3)O_(4)@Ui O-66-NH_(2)-Zr^(4+)nanospheres,the Fe_(3)O_(4)@Ui O-66-NH2-Zr4+HFs showed increased enrichment performance for phosphopeptides.The Fe_(3)O_(4)@Ui O-66-NH2-Zr4+HFs served as an attractive restricted-access adsorption material exhibited good selectivity(m_(β-casein):m_(BSA)=1:1000),high sensitivity(1.0 fmol)and excellent size-exclusion effect(m)((β-casein digests):m_(BSA)=1:200).Furthermore,the Fe_(3)O_(4)@Ui O-66-NH_(2)-Zr^(4+)HFs was successfully applied to the specific capture of ultratrace phosphopeptide from complex biological samples,revealing the great potential for the identification and analysis of trace phosphopeptides in clinical analysis.This work can be easily extended to the fabrication of diverse mag-MOF HFs with multifunctional and easy to post-modify properties,and open up a new avenue for the design and construction of new MOFs material.
基金financially supported by Joint Funds of the National Natural Science Foundation of China(Nos.U23A20673,U22A20244,and 52478261)Qingchuang Technology Project(No.2021KJ045)+1 种基金the China Postdoctoral Science Foundation(No.2024M750580)the Key Laboratory of Functional Inorganic Material Chemistry(Heilongjiang University),Ministry of Education
文摘Carbon-based sponge materials have attracted massive attention as electromagnetic wave(EMW)absorber candidates due to their lightweight and excellent electromagnetic(EM)attenuation capability.However,the high cost,complex fabrication process and limited EMW absorption bandwidth restrict their application.Herein,a hierarchical threedimensional(3D)cotton derived carbon fibers coated with core–shell structure Fe Ni nanoparticle@porous carbon(CCF/Fe Ni@PC)from MOF precursor were successfully constructed by coprecipitation and one-step pyrolysis methods.By regulating carbonization temperature,optimizing the electromagnetic parameters,outstanding electromagnetic absorption(EMA)performance was achieved.The CCF@Fe Ni/PC synthesized at 900℃demonstrates a-64.5 d B minimum reflection loss(RL_(min))at 15.39 GHz,and the effective absorption bandwidth(EAB)is 5.08 GHz when the thickness is only1.9 mm.Notably,the maximum EAB(EAB_(max))was extended8.18 GHz at 2.96 mm for Fe Ni-800.The excellent microwave absorption performance is attributed to the synergistic effect of enhanced dielectric loss and magnetic loss,good impedance matching as well as hierarchical multiple scattering and reflection of EMWs.The obtained CCF@Fe Ni/PC composites provide a novel and promising strategy for constructing lightweight low-cost efficient microwave absorption materials.
文摘Bridge networks are essential components of civil infrastructure,supporting communities by delivering vital services and facilitating economic activities.However,bridges are vulnerable to natural disasters,particularly earthquakes.To develop an effective disaster management strategy,it is critical to identify reliable,robust,and efficient indicators.In this regard,Life-Cycle Cost(LCC)and Resilience(R)serve as key indicators to assist decision-makers in selecting the most effective disaster risk reduction plans.This study proposes an innova-tive LCC-R optimization framework to identify the most optimal retrofit strategies for bridge networks facing hazardous events during their lifespan.The proposed framework employs both single-and multi-objective opti-mization techniques to identify retrofit strategies that maximize the R index while minimizing the LCC for the under-study bridge networks.The considered retrofit strategies include various options such as different mate-rials(steel,CFRP,and GFRP),thicknesses,arrangements,and timing of retrofitting actions.The first step in the proposed framework involves constructing fragility curves by performing a series of nonlinear time-history incre-mental dynamic analyses for each case.In the subsequent step,the seismic resilience surfaces are calculated using the obtained fragility curves and assuming a recovery function.Next,the LCC is evaluated according to the pro-posed formulation for multiple seismic occurrences,which incorporates the effects of complete and incomplete repair actions resulting from previous multiple seismic events.For optimization purposes,the Non-Dominated Sorting Genetic Algorithm II(NSGA-II)evolutionary algorithm efficiently identifies the Pareto front to represent the optimal set of solutions.The study presents the most effective retrofit strategies for an illustrative bridge network,providing a comprehensive discussion and insights into the resulting tactical approaches.The findings underscore that the methodologies employed lead to logical and actionable retrofit strategies,paving the way for enhanced resilience and cost-effectiveness in bridge network management against seismic hazards.
基金supported by the National Natural Science Foundation of China(52473206,U21A20330,52073118)the“111”Project(B18012l)+1 种基金the Shenzhen Science and Technology Plan Funding(JCYJ20230807112800001)the Open Research Funding of State Key Laboratory of Polymer Physics and Chemistry,Changchun Institute of Applied Chemistry,Chinese Academy of Sciences(PPCL2024-016)。
文摘While single-ion conducting solid polymer electrolytes(SPE)offer inherent safety advantages for lithium metal batteries,their practical implementation is hindered by inadequate ionic transport and unstable electrode interfaces.We presented a breakthrough single-ion SPE system through molecular engineering of lithium-enriched porous aromatic frameworks(PAF-228-Li)integrated with poly(ethylene oxide),achieving exceptional ion conduction and interfacial stability.The precisely designed PAF-228-Li architecture established continuous 3D Li+transport pathways through its intrinsic porous channels and surface conduction sites,synergistically enhanced by PVDF-HFP nanofiber networks.This unique design enabled record-high ionic conductivities of 5.48×10^(-4)S cm^(-1)at 30℃and 3.4×10^(-4)S cm^(-1)at-20℃—among the best reported for PEO-based SPEs.The electrolyte spontaneously formed a selflimiting SEI rich in LiF/Li3N,effectively suppressing dendritic growth while enabling unprecedented cycling stability:Li||LiFePO4cells maintain 83.7%capacity over 700 cycles(30℃)and deliver 104.4 mA h g^(-1)after 1100 cycles at-20℃.Practical viability is further demonstrated in pouch cell configurations with stable operation at 30℃.This work pioneers a materials paradigm combining porous framework engineering with polymer electrolyte design,establishing new researching direction for low-temperature compatible,dendrite-resistant solid-state batteries.
基金supported by the National Natural Science Foundation of China(NSFC)(61821005).
文摘Metal–organic frameworks(MOFs)hold great potential for gas separation and storage,and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering new materials.Unlike traditional molecular graphs,crystal graphs require consideration of periodic invariance and modes.In addition,MOF structures such as covalent bonds,functional groups,and global structures impact adsorption performance in different ways.However,redundant atomic interactions can disrupt training accuracy,potentially leading to overfitting.In this paper,we propose a multi-scale crystal graph for describing periodic crystal structures,modeling interatomic interactions at different scales while preserving periodicity invariance.We also propose a multi-head attention crystal graph network in multi-scale graphs(MHACGN-MS),which learns structural characteristics by focusing on interatomic interactions at different scales,thereby reducing interference from redundant interactions.Using MOF adsorption for gases as an example,we demonstrate that MHACGN-MS outperforms traditional graph neural networks in predicting multi-component gas adsorption.We also visualize attention scores to validate effective learning and demonstrate the model’s interpretability.
基金funded by the National Natural Science Foundation of China(grant number 62306186)the Technology Plan Joint Foundation of Liaoning Province(grant number 2023-MSLH-246)the Technology Plan Joint Foundation of Liaoning Province(grant number 2023-BSBA-238).
文摘Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation.However,existing detection methods often struggle with challenges such as complex defect morphology,texture similarity,and fuzzy edges,leading to poor accuracy and missed detections.In order to resolve these problems,we propose MSCM-Net(Multi-Scale Cross-Modal Network),a multiscale cross-modal framework focused on detecting rail surface defects.MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps,effectively capturing and enhancing features at different scales for each modality.To further enrich feature representation and improve edge detection in blurred areas,we propose a multi-scale void fusion module that integrates multi-scale feature information.To improve cross-modal feature fusion,we develop a cross-enhanced fusion module that transfers fused features between layers to incorporate interlayer information.We also introduce a multimodal feature integration module,which merges modality-specific features from separate decoders into a shared decoder,enhancing detection by leveraging richer complementary information.Finally,we validate MSCM-Net on the NEU RSDDS-AUG RGB-depth dataset,comparing it against 12 leading methods,and the results show that MSCM-Net achieves superior performance on all metrics.
基金supported by the National Natural Science Foundation of China(No.U20A20133)the National Key Research and Development Program of China(No.2022YFF0606703).
文摘The arbitrary discharge of tetracycline(TC)residuals has seriously influenced the ecosystem and human health.Laccase(Lac)-based biodegradation technology is considered a more effective way to remove TC due to its high catalytic efficiency and less by-product.Nevertheless,free Lac suffers from poor stability,easy inactivation and difficult recovery,restricting its application.Immobilization of Lac is considered an efficient strategy for addressing these obstacles.In this study,a magnetic metal-organic framework of Fe_(3)O_(4)@SiO_(2)@UiO-66-NH_(2)(MMOF)was prepared and used as a carrier to immobilize Lac(Lac@MMOF)for TC degradation.Benefiting from the multiple binding sites,adsorption,and protection effect of MMOF,Lac@MMOF displayed a wider pH application range(2–7)and better thermal(15–85℃),repeatability,and storage stability than free Lac.Furthermore,owing to the synergism of MOF adsorption and Lac biocatalysis,the removal rate of Lac@MMOF for TC could be up to 98%at pH=7 within 1 hr,which was 1.29 and 1.24 times that of free Lac and MMOF,respectively.More importantly,Lac@MMOF could easily be separated from aqueous solution under a magnetic field and maintained good removal performance(80%)after five cycles.The degradation products were identified by applying LC-MS/MS,and possible degradation mechanisms and pathways were proposed.Finally,the antibacterial activity of intermediate products was evaluated using Escherichia coli,which revealed that the toxicity of TC was reduced effectively by the degradation of Lac@MMOF.Overall,Lac@MMOF is a green alternative for residual antibiotic removal in water.
基金funded by Soonchunhyang University,Grant Numbers 20241422BK21 FOUR(Fostering Outstanding Universities for Research,Grant Number 5199990914048).
文摘Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or items.Collaborative filtering(CF)is a widely used personalization technique that leverages user-item interactions to generate recommendations.However,it struggles with challenges like the cold-start problem,scalability issues,and data sparsity.To address these limitations,we develop a Graph Convolutional Networks(GCNs)model that captures the complex network of interactions between users and items,identifying subtle patterns that traditional methods may overlook.We integrate this GCNs model into a federated learning(FL)framework,enabling themodel to learn fromdecentralized datasets.This not only significantly enhances user privacy—a significant improvement over conventionalmodels but also reassures users about the safety of their data.Additionally,by securely incorporating demographic information,our approach further personalizes recommendations and mitigates the coldstart issue without compromising user data.We validate our RSs model using the openMovieLens dataset and evaluate its performance across six key metrics:Precision,Recall,Area Under the Receiver Operating Characteristic Curve(ROC-AUC),F1 Score,Normalized Discounted Cumulative Gain(NDCG),and Mean Reciprocal Rank(MRR).The experimental results demonstrate significant enhancements in recommendation quality,underscoring that combining GCNs with CF in a federated setting provides a transformative solution for advanced recommendation systems.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2023-2-02038).
文摘Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,etc.This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest path.Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives.To address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective framework.The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives.The search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing methods.The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness.The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions.These adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network conditions.The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation.The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques.The proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended network lifetime.Furthermore,analysis using p-values obtained from multiple performance measures(p-values<0.05)showed that the proposed approach outperforms with a high level of confidence.The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs.It allows stable performance in networks with 100 to 300 nodes,under varying node densities,and across different base station placements.Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use.
基金financially supported by the National Natural Science Foundations of China(No.12304037)the Interdisciplinary Intelligence Super Computer Center of BNUZH
文摘The metal triazole(MTA)-based MOFs were found to preferentially adsorb O-rich species,which had enhanced electrocatalytic oxygen reduction reactions(ORR)and stabilized the O-containing species during the discharge and charge processes in Li-O_(2)battery.However,the MOFs exhibited low electron conductivity and poor electron transfer interface in the electrocatalysis,limiting the electrocatalytic activity.To address this issue,a nanocomposite with the Co-MTA-coated carbon nano tubes(Co-MTA-C)was constructed,which formed the three-dimensional conductivity network connected with the intersecting carbon nano tube(CNT).In this composite,the electron-rich Co-MTA interacted with the highly conductive CNT,resulting in a charge redistribution.Optimized the electronic structure of the Co center through compositional modifications presented a high valence compared to the pure MOFs.In situ X-ray absorption spectroscopy revealed a direct reaction of Co sites with intermediates such as LiO_(x),leading to the formation of nanosheet array discharge products.The battery based on optimized CoMTA-C demonstrated fast kinetics and superior stability,with a low overpotential of 1.13 V,high specific capacity of 9057 mAh g^(-1),and long-term durability of 600 cycles.It provides a facile and effective strategy for enhancing the electrocatalytic performance through rational tuning of high-conductivity substances.
文摘Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.
基金Project supported by the Zhejiang Provincial Natural Science Foundation of China (LY24E020005)Industrial Key Projects of Jinhua City(2023-1-090)。
文摘Mixed-lanthanide(Ln) luminescent materials have important application values in ratiometric temperature sensing.Hydrogen-bonded organic frameworks(HOFs) offer a self-supporting network to conveniently tune multiple Ln^(3+)ions,but challenges still exist in material design.In this work,we selected two simple organic molecules as hydrogen-bonded building blocks,which are melamine(MA) and 2,5-pyridinedicarboxylic acid(PDC) owning the suitable energy to sensitize Tb^(3+)and Eu^(3+)ions.MA assists PDC molecules to support the network in a new HOF(MA-PDC),where the abundant carboxylic groups enable Ln^(3+)ions to combine with the HOF.Based on the effective energy transfer process,the emission of Tb^(3+)and Eu^(3+)from TbxEu1-x@MA-PDC(x=0.75,0.85,0.90) shows the obvious temperature dependence,which benefits ratiometric temperature detection.Taking Tb0.85Eu0.15@MA-PDC as an example,when temperature varies from 303 to 443 K,the intensity ratio exhibits distinct S-type response based on Mott-Seitz model.It also behaves good relative sensitivity(1.03%/K at 383 K),low temperature resolution(0.059 K) and large reproducibility(>96%).This work demonstrates that functionalization of a simple-component HOF with mixed Ln^(3+)ions is a fantastic strategy to develop novel ratiometric thermometers of both low cost and good performance.
基金supported in part by the Universityindustry Collaborative Education Program of the Ministry of Education under Grant No.202102383004。
文摘The advancement of Internet of Things(IoT)technology is driving industries toward intelligent digital transformation,highlighting the crucial role of software engineering.Despite this,the integration of software engineering into IoT engineering education remains underexplored.To address this gap,the School of Software at North University of China,in collaboration with QST Innovation Technology Group Co.,Ltd.(QST),has developed an innovative educational mechanism.This initiative focuses on the software engineering IoT track and optimizes the teaching process through the outcome-based education(OBE)concept.It incorporates military-industrial characteristics,introduces advanced information and technology curricula,and enhances laboratory infrastructure.The goal is to cultivate innovative talents with unique capabilities,thereby fostering the comprehensive development and application of IoT technology.
基金supported by Key Research and Development Project of Shandong Province(2021ZDSYS12)National Natural Science Foundation of China(22076086,21777089)+3 种基金Taishan Scholar Program of Shandong Province(ts20190948)Shandong Province Science and Technology Small and Medium Enterprises Innovation Ability Enhancement Project(2023TSGC0689,2023TSGC0055)Natural Science Foundation of Shandong Province(ZR2021MB086,ZR2023QB035)Jinan City University and Institute Innovation Team Project(2021GXRC061,20228045,202333027)。
文摘Organophosphorus pesticides(OPPs)in foods pose a serious threat to human health,motivating the development of novel analytical methods for their rapid detection and quantification.A magnetic covalent organic framework(M-COF)adsorbent for the magnetic solid-phase extraction(MSPE)of OPPs from foods was reported.M-COF was synthesized by the Schiff base condensation reaction of 1,3,5-tris(4-aminophenyl)benzene and 4,4-biphenyldicarboxaldehyde on the surface of amino-functionalized magnetic nanoparticles.Density functional theory(DFT)calculations showed that adsorption of OPPs onto the surface of M-COF involved hydrophobic effects,van der Waals interactions,π-πinteractions,halogen-N bonding,and hydrogen bonding.Combined with gas chromatography-mass spectrometry(GC-MS)technology,the MSPE method features low limits of detection for OPPs(0.002-0.015μg/L),good reproducibility(1.45%-6.14%),wide linear detection range(0.01-1μg/L,R≥0.9935),and satisfactory recoveries(87.3%-110.4%).The method was successfully applied for the trace analysis of OPPs in spiked fruit juices.
