Sepsis remains a leading cause morbidity and mortality worldwide;effective targeted therapies remain elusive due to its inherent heterogeneity and dynamic temporal evolution.Existing frameworks often focus on either t...Sepsis remains a leading cause morbidity and mortality worldwide;effective targeted therapies remain elusive due to its inherent heterogeneity and dynamic temporal evolution.Existing frameworks often focus on either the diverse manifestations of sepsis or its progression over time,but fail to integrate these critical aspects.In this review,we propose a novel spatial-temporal framework that integrates both the heterogeneity and temporality of sepsis.The framework consists of two key dimensions:The cross-sectional(heterogeneity)dimension,which addresses pathogen variability,host factors,and pathogen-host interactions;The longitudinal(temporality)dimension,which explores the dynamic evolution of sepsis and the need for adaptive,real-time interventions.Given the complexity of multidimensional temporal data,big data techniques have the potential to integrate these data and decompose sepsis into distinct disease subtypes.Stratification facilitates the development of personalized therapeutic approaches tailored to specific subtypes.Moreover,methods,such as reinforcement learning,can track the dynamic transitions between these subtypes,enabling real-time adaptation of treatment strategies.展开更多
The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang...The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang for multiple years via a decision tree method based on a classification and regression tree(CART)algorithm using Landsat time series images.Spatiotemporal transform and fragmentation patterns of mangrove distribution were separately assessed with a transfer matrix of land cover types and a landscape pattern index.The classification method combined with multi-band images showed good accuracy,with overall accuracy higher than 90%.Mangrove areas in 1988,1999,2009,and 2019 were 2050,1875,1818,and 1750 ha,respectively,with decreases mainly due to conversion to aquaculture ponds and farmland.A mangrove growth index(MGI)was proposed,reflecting the water-mangrove relationship,showing positive mangrove growth from 1988–2009 and negative growth from 2009–2019.Study results indicated anthropogenic factors play a leading role in the extent and scale of mangrove effects over the past 30 years.According to the analysis results,corresponding management and protection measures are proposed to provide reference for the sustainable development of Dongzhaigang Mangrove Wetland ecosystem.展开更多
Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-netwo...Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously.Therefore,there is a need for complementary methods to address these deficiencies.To address these limitations,this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system.A dual information network is constructed to assess the degree of operational deviation considering the planning tasks.To validate the effectiveness,discussions are conducted through a modified cosine similarity calculation on theoretical analysis,delay level description,and the ability to identify abnormal dates.Compared to some state-of-the-art methods,the proposed method achieves an average Spearman delay correlation of 0.847 and a relative distance of 3.477.Furthermore,case analyses are invested in regions of China's Mainland,Europe,and the United States,investigating both the overall and sub-regional network fluctuations.To represent the impact of network fluctuations in sub-regions,a response loss value was developed.The times that are prone to fluctuations are also discussed through the classification of time series data.The research can offer a novel approach to system monitoring,providing a research direction that utilizes individual data combined to represent macroscopic states.Our code will be released at https://github.com/daozhong/STPN.git.展开更多
Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaboratio...Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.展开更多
As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.展开更多
In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed p...In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
In this study,a nickel-based MOF{(NH_(2)(CH_(3))_(2))_(2)[Ni_(3)(O)(L)3(NH(CH_(3))_(2))_(3)]}_(n)(Ni_(3)-MOF),with pore sizes of approximately 1.6 nm×1.6 nm,was synthesized by reacting 4,4′-biphenyldicarboxylic ...In this study,a nickel-based MOF{(NH_(2)(CH_(3))_(2))_(2)[Ni_(3)(O)(L)3(NH(CH_(3))_(2))_(3)]}_(n)(Ni_(3)-MOF),with pore sizes of approximately 1.6 nm×1.6 nm,was synthesized by reacting 4,4′-biphenyldicarboxylic acid(H_(2)L)with Ni(NO_(3))_(2)·6H_(2)O in an N,N-dimethylformamide(DMF)solution.The nanoscale adsorbent Ni_(3)-MOF-N with a particle diameter of approximately 200 nm was prepared using Ni_(3)-MOF.It exhibited a maximum equilibrium tetracycline(TC)adsorption capacity of 358.2 mg·g^(-1)at its isoelectric point(pH=6.50),outperforming most reported MOF-based adsorbents.This exceptional performance is likely attributed to the well-matched pore size of Ni_(3)-MOF-N(1.6 nm×1.6 nm)and the molecular dimensions of TC(0.8 nm×1.2 nm),combined with the presence of partial Ni(Ⅱ)sites on the surface of Ni_(3)-MOF-N.These features collectively facilitate effective TC adsorption through a combination of pore filling,electrostatic attraction,hydrogen bonding,surface complexation,andπ-πinteractions.Recycling experiments demonstrated that Ni_(3)-MOF-N possesses excellent structural stability and consistent adsorption performance.CCDC:2481791,Ni_(3)-MOF.展开更多
A metal-organic framework{[Zn(L)_(0.5)(1,2,4,5-tpb)_(0.5)]·DMF·3H_(2)O}_(n)(1)was synthesized by solvothermal reaction,where H4L=5,5'-(ethane-1,2-diyl)diisophthalic acid,and 1,2,4,5-tpb=1,2,4,5-tetra(pyr...A metal-organic framework{[Zn(L)_(0.5)(1,2,4,5-tpb)_(0.5)]·DMF·3H_(2)O}_(n)(1)was synthesized by solvothermal reaction,where H4L=5,5'-(ethane-1,2-diyl)diisophthalic acid,and 1,2,4,5-tpb=1,2,4,5-tetra(pyridin-4-yl)benzene.The analysis of the single crystal structure indicates that L^(4-)and 1,2,4,5-tpb are connected with Zn(Ⅱ)to form a 2D layered structure,and the layers are linked by 1,2,4,5-tpb to form a 3D structure.1 can be used as a highly selective fluorescent probe for the detection of 2,4-dinitrophenylhydrazine(DNP)and tetracycline(TET),and the detection limits were 0.013 and 0.31μmol·L^(-1),respectively.1 was applied successfully to the determination of TET content in the Yanhe River water sample.CCDC:2466221.展开更多
In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detec...In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.展开更多
Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully construct...Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.展开更多
Silicon possesses a high theoretical capacity,making it a potential contender for lithium-ion battery(LIB)anodes.Nonetheless,its practical usage is challenged by low electrical conductivity and significant volume expa...Silicon possesses a high theoretical capacity,making it a potential contender for lithium-ion battery(LIB)anodes.Nonetheless,its practical usage is challenged by low electrical conductivity and significant volume expansion during cycling.Here,we synthesized a novel silicon/carbon(Si/C)anode doped with ZnO via a template-derived method and high-temperature carbonization.The carbon structure,originated from metal-organic frameworks(MOFs)and ZnO doping,substantially enhanced the electrochemical properties of the composite material.It exhibited an initial capacity of 2100.3 mA h g^(-1)at a current density of 0.2 A g^(-1)and demonstrated excellent capacity retention over successive cycles.Moreover,the composite material displayed superior rate performance at higher current densities of 2 A g^(-1)and 3 A g^(-1).To address the low initial Coulombic efficiency(ICE)of siliconbased materials,we adopted a direct contact prelithiation approach and optimized the lithiation process by controlling the prelithiation time.After 30 min of prelithiation,the ICE reached 97.9%,thereby reducing the initial irreversible capacity loss(ICL)and realizing stable discharge-charge in subsequent cycles.This rational design provides valuable insights for achieving high-performance silicon anode.展开更多
High-sensitive quantitative determination of alpha-fetoprotein(AFP)is of crucial importance for early clinical diagnosis of cancers.Herein,an AuNPs-free electrochemical immunosensor(Ab1-Fc-COF)was prepared from a carb...High-sensitive quantitative determination of alpha-fetoprotein(AFP)is of crucial importance for early clinical diagnosis of cancers.Herein,an AuNPs-free electrochemical immunosensor(Ab1-Fc-COF)was prepared from a carboxylic group enriched COF by post-functionalization with detecting antibody(Ab1)and ferrocene(Fc),and used for electrochemical detection of AFP.Due to the small,homogeneous pore size of the COF,Ab1 with a big size was immobilized on the surface of the COF,while Fc with a small size was covalently modified both on the surface and in the pores of COF.The covalently immobilized Ab1 was quite stable and beneficial to specifically detect AFP biomarkers.Meanwhile,the enriched Fc molecules not only improved the conductivity of the COF,but also effectively transferred and amplified the electrochemical signal.This proposed immunosensor exhibited high sensitivity in detecting AFP with a detection limit of 0.39 pg/mL(S/N of 3:1)and a wide linear response range spanning from 1 pg/mL to 100 ng/mL when plotted against logarithmic concentrations.Furthermore,this immunosensor showed excellent selectivity,stability and reproducibility in the testing of real samples.This study presents an innovative prototype for construction of a precious metal-free,antibody-directly-immobilized,simple and stable electrochemical immunoprobe.展开更多
Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based...Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.展开更多
Accelerating the development of new quality productive forces(NQPF),with innovation at its core,has become essential for firm growth in the new era.Drawing on financial data from China's A-share listed companies s...Accelerating the development of new quality productive forces(NQPF),with innovation at its core,has become essential for firm growth in the new era.Drawing on financial data from China's A-share listed companies spanning the period 2010–2023,this study empirically investigates the impact of entrepreneurial spirit on firm-level NQPF.The results indicate that entrepreneurial spirit significantly promotes firm-level NQPF.Mechanism analysis indicates that entrepreneurial effort—underpinned by technological capital accumulation,effective incentive and constraint mechanisms,and a competitive market environment—plays a mediating role in this relationship.Further heterogeneity analysis reveals that,amid China's economic transition,the positive effects of entrepreneurial spirit are more pronounced in non-state-owned enterprises,high-tech firms,and newly established firms.Accordingly,systematic efforts should be pursued across the technological,organizational,and environmental(TOE)dimensions to optimize the cultivation of entrepreneurial spirit.In particular,greater emphasis should be placed on productive entrepreneurial spirit and the constructive role of entrepreneurial effort,so as to fully leverage their contribution to the advancement of firm-level NQPF.展开更多
Gas sensors are valuable tools for human applications,and extensive research has been conducted in this field.However,practical implementation has yet to be fully realized.In response,efforts have been made to explore...Gas sensors are valuable tools for human applications,and extensive research has been conducted in this field.However,practical implementation has yet to be fully realized.In response,efforts have been made to explore metal-organic frameworks(MOFs),a novel class of porous materials,as potential solutions.MOFs exhibit exceptional porosity and highly tunable chemical compositions and structures,giving rise to a wide range of unique physical and chemical properties.Significant progress has been achieved in developing MOF-based gas sensors,improving sensing performance for various gases.This review aims to provide a comprehensive understanding of MOF-based gas sensors,even for readers unfamiliar with MOFs and gas sensors.It covers the working principles of these sensors,fundamental concepts of MOFs,strategies for tuning MOF properties,fabrication techniques for MOF films,and recent studies on MOF and MOF-derivative gas sensors.Finally,current challenges,overlooked aspects,and future directions for fully exploiting the potential of MOFs in gas sensor development are discussed.展开更多
Lead-halide perovskite solar cells(PSCs)have rapidly achieved certified efficiencies>27%,rivaling silicon photovoltaics.However,their commercialization is hindered by intrinsic material challenges:poor operational ...Lead-halide perovskite solar cells(PSCs)have rapidly achieved certified efficiencies>27%,rivaling silicon photovoltaics.However,their commercialization is hindered by intrinsic material challenges:poor operational stability under moisture,heat,and light;toxic lead leakage from degraded films.Metal-organic frameworks(MOFs),with their unique framework structure,large specific surface area,high heavy metal capturing capacity,and tunable conductivity,offer promising solutions to these issues.Recent studies have integrated MOFs into PSCs architectures to enhance performance and durability.This comprehensive review begins with an in-depth discussion of the structure,optical properties,electrical characteristics,and stability of MOFs,as well as their theoretical compatibility with perovskites.Subsequently,it provides a detailed analysis of how MOFs enhance charge carrier transport,promote perovskite crystallinity,improve device stability,and suppress lead leakage in PSCs.In summary,this review examines the research progress and potential of integrating MOFs with perovskites to address the critical PSCs challenges of efficiency,instability,and toxicity.展开更多
The recovery of precious metals(PMs)from secondary resources is critical for addressing global supply-chain vulnerabilities and sustainable resource utilization.This review systematically examines the transformative p...The recovery of precious metals(PMs)from secondary resources is critical for addressing global supply-chain vulnerabilities and sustainable resource utilization.This review systematically examines the transformative potential of metal-organic frameworks(MOFs)as next-generation adsorbents for PM recovery,focusing on their synthesis,functionalization,and multiscale adsorption mechanisms.We critically analyze conventional pyrometallurgical and hydrometallurgical methods and highlight their limitations in terms of selectivity,energy consumption,and secondary pollution.In contrast,MOFs offer tunable porosity,abundant active sites,and tunable surface chemistry,enabling efficient PM capture via synergistic physical and chemical adsorption.Advanced modification techniques,including direct synthesis and post-synthetic modification,are reviewed to propose strategies for enhancing the adsorption kinetics and selectivity for Au,Ag,Pt,and Pd.Key structure-property relationships are established through multiscale characterization and thermodynamic models,revealing the critical roles of hierarchical porosity,soft donor atoms,and framework stability.Industrial challenges,such as aqueous stability and scalability,are addressed via Zr-O bond strengthening,hydrophobic functionalization,and support immobilization.This study consolidates the experimental and theoretical advances in MOF-based PM recovery and provides a roadmap for translating laboratory innovations into practical applications within the circular-economy framework.展开更多
Photocatalytic carbon dioxide reduction reaction(CO_(2)RR)is a carbon-neutral strategy to address global energy use and its impact on climate.Metal oxide and metal chalcogenide catalysts are the most investigated cata...Photocatalytic carbon dioxide reduction reaction(CO_(2)RR)is a carbon-neutral strategy to address global energy use and its impact on climate.Metal oxide and metal chalcogenide catalysts are the most investigated catalysts for photocatalytic CO_(2)RR.Unfortunately,low CO_(2)adsorption ability and limited active sites of metal oxide and metal chalcogenide catalysts for CO_(2)RR make them less competitive compared to their industrial counterparts.Inspired by applications of porphyrin-based metal-organic framework(MOF)catalysts for hydrogen evolution and photodynamic therapy,the investigations of these porphyrin-based MOFs,including pristine and composite porphyrin-based MOFs in photocatalytic CO_(2)RR,have attracted significant attention in the last five years due to their excellent CO_(2)adsorption capacities,high porosity,high stability,exceptional optoelectronic properties,and multi-functionality.However,due to the difference in photocatalytic CO_(2)RR,several critical issues need to be addressed to achieve the rational design of advanced porphyrin-based MOF photocatalysts to improve activity,selectivity,and stability for CO_(2)RR.Here,we review recent developments in the field of porphyrin-based MOF CO_(2)RR photocatalysts,along with critical issues,challenges,and perspectives concerning porphyrin-based MOF catalysts for photocatalytic CO_(2)RR.展开更多
Metal-organic frameworks(MOFs)with high porosity,specific surface area,and unique topologies are highly regarded for their applications in photocatalysis,medical treatment,and environmental pollutant degradation.Howev...Metal-organic frameworks(MOFs)with high porosity,specific surface area,and unique topologies are highly regarded for their applications in photocatalysis,medical treatment,and environmental pollutant degradation.However,due to the limitations of the tumor microenvironment(TME),traditional MOFs have limited efficacy in this environment.This paper designs multi-metal oxide-based heterostructure POMOFs nanoreactors with a nesting doll-like structure.This new structure not only exhibits therapeutic effects in TME but also utilizes ultrasound(US)to enhance the release of reactive oxygen species(ROS)for CDT&SDT co-therapy,becoming an effective sound sensitizer for destroying tumor cells.In summary,our study proposes an idea for constructing multi-metal oxide-based heterostructure MOFs nanoreactors material with a nesting doll-like structure to enhance ROS release and synergistically treat tumor diseases.展开更多
基金supported by grants from the Beijing Municipal Natural Science Foundation(No.L222019)CAMS Innovation Fund for Medical Sciences(CIFMS)(No.2024-I2M-C&T-C-002)+1 种基金National High Level Hospital Clinical Research Funding(Nos.2022-PUMCH-B-115 and 2022-PUMCH-D-005)National Key Research and Development Program of China(No.2024YFF1207104).
文摘Sepsis remains a leading cause morbidity and mortality worldwide;effective targeted therapies remain elusive due to its inherent heterogeneity and dynamic temporal evolution.Existing frameworks often focus on either the diverse manifestations of sepsis or its progression over time,but fail to integrate these critical aspects.In this review,we propose a novel spatial-temporal framework that integrates both the heterogeneity and temporality of sepsis.The framework consists of two key dimensions:The cross-sectional(heterogeneity)dimension,which addresses pathogen variability,host factors,and pathogen-host interactions;The longitudinal(temporality)dimension,which explores the dynamic evolution of sepsis and the need for adaptive,real-time interventions.Given the complexity of multidimensional temporal data,big data techniques have the potential to integrate these data and decompose sepsis into distinct disease subtypes.Stratification facilitates the development of personalized therapeutic approaches tailored to specific subtypes.Moreover,methods,such as reinforcement learning,can track the dynamic transitions between these subtypes,enabling real-time adaptation of treatment strategies.
基金financially supported by the National Natural Science Foundation of China(Nos.U2244225 and 42020104005)the Ministry of Education of China(111 Project)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)and China Geological Survey(No.DD20211391)。
文摘The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang for multiple years via a decision tree method based on a classification and regression tree(CART)algorithm using Landsat time series images.Spatiotemporal transform and fragmentation patterns of mangrove distribution were separately assessed with a transfer matrix of land cover types and a landscape pattern index.The classification method combined with multi-band images showed good accuracy,with overall accuracy higher than 90%.Mangrove areas in 1988,1999,2009,and 2019 were 2050,1875,1818,and 1750 ha,respectively,with decreases mainly due to conversion to aquaculture ponds and farmland.A mangrove growth index(MGI)was proposed,reflecting the water-mangrove relationship,showing positive mangrove growth from 1988–2009 and negative growth from 2009–2019.Study results indicated anthropogenic factors play a leading role in the extent and scale of mangrove effects over the past 30 years.According to the analysis results,corresponding management and protection measures are proposed to provide reference for the sustainable development of Dongzhaigang Mangrove Wetland ecosystem.
文摘Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously.Therefore,there is a need for complementary methods to address these deficiencies.To address these limitations,this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system.A dual information network is constructed to assess the degree of operational deviation considering the planning tasks.To validate the effectiveness,discussions are conducted through a modified cosine similarity calculation on theoretical analysis,delay level description,and the ability to identify abnormal dates.Compared to some state-of-the-art methods,the proposed method achieves an average Spearman delay correlation of 0.847 and a relative distance of 3.477.Furthermore,case analyses are invested in regions of China's Mainland,Europe,and the United States,investigating both the overall and sub-regional network fluctuations.To represent the impact of network fluctuations in sub-regions,a response loss value was developed.The times that are prone to fluctuations are also discussed through the classification of time series data.The research can offer a novel approach to system monitoring,providing a research direction that utilizes individual data combined to represent macroscopic states.Our code will be released at https://github.com/daozhong/STPN.git.
基金supported by the Beijing Natural Science Foundation(Certificate Number:L234025).
文摘Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
基金supported by the National Office for Philosophy and Social Sciences(grant reference 22&ZD067).
文摘In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
文摘In this study,a nickel-based MOF{(NH_(2)(CH_(3))_(2))_(2)[Ni_(3)(O)(L)3(NH(CH_(3))_(2))_(3)]}_(n)(Ni_(3)-MOF),with pore sizes of approximately 1.6 nm×1.6 nm,was synthesized by reacting 4,4′-biphenyldicarboxylic acid(H_(2)L)with Ni(NO_(3))_(2)·6H_(2)O in an N,N-dimethylformamide(DMF)solution.The nanoscale adsorbent Ni_(3)-MOF-N with a particle diameter of approximately 200 nm was prepared using Ni_(3)-MOF.It exhibited a maximum equilibrium tetracycline(TC)adsorption capacity of 358.2 mg·g^(-1)at its isoelectric point(pH=6.50),outperforming most reported MOF-based adsorbents.This exceptional performance is likely attributed to the well-matched pore size of Ni_(3)-MOF-N(1.6 nm×1.6 nm)and the molecular dimensions of TC(0.8 nm×1.2 nm),combined with the presence of partial Ni(Ⅱ)sites on the surface of Ni_(3)-MOF-N.These features collectively facilitate effective TC adsorption through a combination of pore filling,electrostatic attraction,hydrogen bonding,surface complexation,andπ-πinteractions.Recycling experiments demonstrated that Ni_(3)-MOF-N possesses excellent structural stability and consistent adsorption performance.CCDC:2481791,Ni_(3)-MOF.
文摘A metal-organic framework{[Zn(L)_(0.5)(1,2,4,5-tpb)_(0.5)]·DMF·3H_(2)O}_(n)(1)was synthesized by solvothermal reaction,where H4L=5,5'-(ethane-1,2-diyl)diisophthalic acid,and 1,2,4,5-tpb=1,2,4,5-tetra(pyridin-4-yl)benzene.The analysis of the single crystal structure indicates that L^(4-)and 1,2,4,5-tpb are connected with Zn(Ⅱ)to form a 2D layered structure,and the layers are linked by 1,2,4,5-tpb to form a 3D structure.1 can be used as a highly selective fluorescent probe for the detection of 2,4-dinitrophenylhydrazine(DNP)and tetracycline(TET),and the detection limits were 0.013 and 0.31μmol·L^(-1),respectively.1 was applied successfully to the determination of TET content in the Yanhe River water sample.CCDC:2466221.
文摘In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.
文摘Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.
基金supported by the National Key R&D Program of China(No.2022YFA1504100)the Anhui Provincial Major Science and Technology Project(No.202203a05020017)+4 种基金the National Natural Science Foundation of China(Nos.52222210,51925207,U1910210,52161145101,51972067,51902062,and 52002083)the“Transformational Technologies for Clean Energy and Demonstration”Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA21000000)the National Synchrotron Radiation Laboratory(No.KY2060000173)the Joint Fund of the Yulin University and the Dalian National Laboratory for Clean Energy(No.YLU-DNL Fund 2021002)the Fundamental Research Funds for the Central Universities(No.WK2060140026)。
文摘Silicon possesses a high theoretical capacity,making it a potential contender for lithium-ion battery(LIB)anodes.Nonetheless,its practical usage is challenged by low electrical conductivity and significant volume expansion during cycling.Here,we synthesized a novel silicon/carbon(Si/C)anode doped with ZnO via a template-derived method and high-temperature carbonization.The carbon structure,originated from metal-organic frameworks(MOFs)and ZnO doping,substantially enhanced the electrochemical properties of the composite material.It exhibited an initial capacity of 2100.3 mA h g^(-1)at a current density of 0.2 A g^(-1)and demonstrated excellent capacity retention over successive cycles.Moreover,the composite material displayed superior rate performance at higher current densities of 2 A g^(-1)and 3 A g^(-1).To address the low initial Coulombic efficiency(ICE)of siliconbased materials,we adopted a direct contact prelithiation approach and optimized the lithiation process by controlling the prelithiation time.After 30 min of prelithiation,the ICE reached 97.9%,thereby reducing the initial irreversible capacity loss(ICL)and realizing stable discharge-charge in subsequent cycles.This rational design provides valuable insights for achieving high-performance silicon anode.
基金the Natural Science Foundation of ZhejiangProvince(No.LZ24B020005)the National Natural Science Foundation of China(No.22071040)for financial support.
文摘High-sensitive quantitative determination of alpha-fetoprotein(AFP)is of crucial importance for early clinical diagnosis of cancers.Herein,an AuNPs-free electrochemical immunosensor(Ab1-Fc-COF)was prepared from a carboxylic group enriched COF by post-functionalization with detecting antibody(Ab1)and ferrocene(Fc),and used for electrochemical detection of AFP.Due to the small,homogeneous pore size of the COF,Ab1 with a big size was immobilized on the surface of the COF,while Fc with a small size was covalently modified both on the surface and in the pores of COF.The covalently immobilized Ab1 was quite stable and beneficial to specifically detect AFP biomarkers.Meanwhile,the enriched Fc molecules not only improved the conductivity of the COF,but also effectively transferred and amplified the electrochemical signal.This proposed immunosensor exhibited high sensitivity in detecting AFP with a detection limit of 0.39 pg/mL(S/N of 3:1)and a wide linear response range spanning from 1 pg/mL to 100 ng/mL when plotted against logarithmic concentrations.Furthermore,this immunosensor showed excellent selectivity,stability and reproducibility in the testing of real samples.This study presents an innovative prototype for construction of a precious metal-free,antibody-directly-immobilized,simple and stable electrochemical immunoprobe.
基金the Hebei Province Science and Technology Plan Project(19221909D)rincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R308),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.
基金Liaoning Provincial Social Science Fund Key Disciplines Development Project,Research on the New Supply Function of Entrepreneurs Based on Innovation Ecosystems Driven by Data(Grant No.L22ZD061)。
文摘Accelerating the development of new quality productive forces(NQPF),with innovation at its core,has become essential for firm growth in the new era.Drawing on financial data from China's A-share listed companies spanning the period 2010–2023,this study empirically investigates the impact of entrepreneurial spirit on firm-level NQPF.The results indicate that entrepreneurial spirit significantly promotes firm-level NQPF.Mechanism analysis indicates that entrepreneurial effort—underpinned by technological capital accumulation,effective incentive and constraint mechanisms,and a competitive market environment—plays a mediating role in this relationship.Further heterogeneity analysis reveals that,amid China's economic transition,the positive effects of entrepreneurial spirit are more pronounced in non-state-owned enterprises,high-tech firms,and newly established firms.Accordingly,systematic efforts should be pursued across the technological,organizational,and environmental(TOE)dimensions to optimize the cultivation of entrepreneurial spirit.In particular,greater emphasis should be placed on productive entrepreneurial spirit and the constructive role of entrepreneurial effort,so as to fully leverage their contribution to the advancement of firm-level NQPF.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2024-00333650)supported by basic science research program through the National Research Foundation of Korea funded by the Ministry of Education(NRF-2019R1A6A1A11055660)+1 种基金supported by the Technology Innovation Program(“20013621”,Center for Super Critical Material Industrial Technology)funded By the Ministry of Trade,Industry&Energy(MOTIE,Korea)supported by Strategic Networking&Development Program funded by the Ministry of Science and ICT through the National Research Foundation of Korea(RS-2023-00268523)。
文摘Gas sensors are valuable tools for human applications,and extensive research has been conducted in this field.However,practical implementation has yet to be fully realized.In response,efforts have been made to explore metal-organic frameworks(MOFs),a novel class of porous materials,as potential solutions.MOFs exhibit exceptional porosity and highly tunable chemical compositions and structures,giving rise to a wide range of unique physical and chemical properties.Significant progress has been achieved in developing MOF-based gas sensors,improving sensing performance for various gases.This review aims to provide a comprehensive understanding of MOF-based gas sensors,even for readers unfamiliar with MOFs and gas sensors.It covers the working principles of these sensors,fundamental concepts of MOFs,strategies for tuning MOF properties,fabrication techniques for MOF films,and recent studies on MOF and MOF-derivative gas sensors.Finally,current challenges,overlooked aspects,and future directions for fully exploiting the potential of MOFs in gas sensor development are discussed.
基金financially supported by the National Natural Science foundation of China(grants nos.52272176)。
文摘Lead-halide perovskite solar cells(PSCs)have rapidly achieved certified efficiencies>27%,rivaling silicon photovoltaics.However,their commercialization is hindered by intrinsic material challenges:poor operational stability under moisture,heat,and light;toxic lead leakage from degraded films.Metal-organic frameworks(MOFs),with their unique framework structure,large specific surface area,high heavy metal capturing capacity,and tunable conductivity,offer promising solutions to these issues.Recent studies have integrated MOFs into PSCs architectures to enhance performance and durability.This comprehensive review begins with an in-depth discussion of the structure,optical properties,electrical characteristics,and stability of MOFs,as well as their theoretical compatibility with perovskites.Subsequently,it provides a detailed analysis of how MOFs enhance charge carrier transport,promote perovskite crystallinity,improve device stability,and suppress lead leakage in PSCs.In summary,this review examines the research progress and potential of integrating MOFs with perovskites to address the critical PSCs challenges of efficiency,instability,and toxicity.
基金supported by the National Natural Science Foundation of China(No.52304329)the Yunnan Fundamental Research Projects(No.202201BE070001-003),Guo Lin would like to acknowledge Xing Dian talent support program of Yunnan Province.
文摘The recovery of precious metals(PMs)from secondary resources is critical for addressing global supply-chain vulnerabilities and sustainable resource utilization.This review systematically examines the transformative potential of metal-organic frameworks(MOFs)as next-generation adsorbents for PM recovery,focusing on their synthesis,functionalization,and multiscale adsorption mechanisms.We critically analyze conventional pyrometallurgical and hydrometallurgical methods and highlight their limitations in terms of selectivity,energy consumption,and secondary pollution.In contrast,MOFs offer tunable porosity,abundant active sites,and tunable surface chemistry,enabling efficient PM capture via synergistic physical and chemical adsorption.Advanced modification techniques,including direct synthesis and post-synthetic modification,are reviewed to propose strategies for enhancing the adsorption kinetics and selectivity for Au,Ag,Pt,and Pd.Key structure-property relationships are established through multiscale characterization and thermodynamic models,revealing the critical roles of hierarchical porosity,soft donor atoms,and framework stability.Industrial challenges,such as aqueous stability and scalability,are addressed via Zr-O bond strengthening,hydrophobic functionalization,and support immobilization.This study consolidates the experimental and theoretical advances in MOF-based PM recovery and provides a roadmap for translating laboratory innovations into practical applications within the circular-economy framework.
基金financially supported by the National Natural Science Foundation of China(No.22305009)the Science and Technology Development Fund,Macao SAR(File no.FDCT-0125/2022/A and FDCT-0006/2023/RIB1)Hong Kong Research Grant Council(RGC)General Research Fund(GRF)City U 11305419,11306920,CityU 11308721,CityU 11316522,and SIRG7020022。
文摘Photocatalytic carbon dioxide reduction reaction(CO_(2)RR)is a carbon-neutral strategy to address global energy use and its impact on climate.Metal oxide and metal chalcogenide catalysts are the most investigated catalysts for photocatalytic CO_(2)RR.Unfortunately,low CO_(2)adsorption ability and limited active sites of metal oxide and metal chalcogenide catalysts for CO_(2)RR make them less competitive compared to their industrial counterparts.Inspired by applications of porphyrin-based metal-organic framework(MOF)catalysts for hydrogen evolution and photodynamic therapy,the investigations of these porphyrin-based MOFs,including pristine and composite porphyrin-based MOFs in photocatalytic CO_(2)RR,have attracted significant attention in the last five years due to their excellent CO_(2)adsorption capacities,high porosity,high stability,exceptional optoelectronic properties,and multi-functionality.However,due to the difference in photocatalytic CO_(2)RR,several critical issues need to be addressed to achieve the rational design of advanced porphyrin-based MOF photocatalysts to improve activity,selectivity,and stability for CO_(2)RR.Here,we review recent developments in the field of porphyrin-based MOF CO_(2)RR photocatalysts,along with critical issues,challenges,and perspectives concerning porphyrin-based MOF catalysts for photocatalytic CO_(2)RR.
基金funded by the National Natural Science Foundation of China(Nos.52372264,32271609and 52473109)+2 种基金The Natural Science Foundation of Heilongjiang Province of China(No.LH2023B002)The Fundamental Research Funds for the Central Universities(No.2572023CT12)Undergraduate Training Programs for Innovations by NEFU(No.202310225565)。
文摘Metal-organic frameworks(MOFs)with high porosity,specific surface area,and unique topologies are highly regarded for their applications in photocatalysis,medical treatment,and environmental pollutant degradation.However,due to the limitations of the tumor microenvironment(TME),traditional MOFs have limited efficacy in this environment.This paper designs multi-metal oxide-based heterostructure POMOFs nanoreactors with a nesting doll-like structure.This new structure not only exhibits therapeutic effects in TME but also utilizes ultrasound(US)to enhance the release of reactive oxygen species(ROS)for CDT&SDT co-therapy,becoming an effective sound sensitizer for destroying tumor cells.In summary,our study proposes an idea for constructing multi-metal oxide-based heterostructure MOFs nanoreactors material with a nesting doll-like structure to enhance ROS release and synergistically treat tumor diseases.
