Introduction:Human brucellosis persists as a critical public health challenge in China.Understanding disease clusters and trends is essential for implementing effective control strategies.This study evaluates the epid...Introduction:Human brucellosis persists as a critical public health challenge in China.Understanding disease clusters and trends is essential for implementing effective control strategies.This study evaluates the epidemiological characteristics and spatiotemporal distribution of brucellosis in China from 2011 to 2023.Methods:Data were obtained from the National Notifiable Disease Reporting System(NNDRS).We conducted descriptive epidemiological analyses and employed SaTScan10.1 and ArcGIS10.7 software to identify disease clusters and generate county(district)-level incidence maps.Results:The incidence of human brucellosis in Chinese mainland increased substantially between 2011 and 2023,rising from 38,151 cases(2.8/100,000)across 834 counties(25.4%)to 70,439 cases(5.2/100,000)across 2,290 counties(76.9%).A significant upward trend in reported incidence emerged during 2018–2023(average annual percentage change(AAPC)=14.9%,P=0.01).Most cases(89.3%)occurred in individuals aged 25–69 years,with an increasing proportion among those aged over 60 years.While 96.1%of cases were reported in northern provincial-level administrative divisions(PLADs),southern regions demonstrated escalating incidence rates and expanding geographical spread.Southern PLADs exhibited a notable annual increase of 31.5%in reported incidence(P<0.01).Counties(districts)with incidence rates exceeding 10 per 100,000 expanded geographically from northwestern pastoral regions to southern areas and from rural to urban settings.Primary spatiotemporal clusters were concentrated in Inner Mongolia and adjacent provincial-level administrative divisions(PLADs),with emerging clusters identified in Yunnan,Guangdong,and Xizang.Conclusions:The human brucellosis epidemic in China continues to intensify,characterized by rebounding incidence rates and broader geographical distribution across counties(districts).While spatiotemporal clusters remain predominantly centered in Inner Mongolia and neighboring regions,targeted interventions and increased resource allocation for high-risk areas and populations are imperative.展开更多
Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic st...Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.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
An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the...An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.展开更多
Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as...Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as the strongly enhanced synergistic interactions between Pt and Co atoms,the obtained Pt-on-Co/C400 catalysts exhib-ited excellent catalytic activity toward the hydrolysis of ammonia borane with an extremely high turnover frequency(TOF)value of 3022 min^(-1)at 303 K.Durability test indicated that the obtained Pt-on-Co/C400 catalysts possessed high catalytic stability,and there were no changes in the catalyst structures and catalytic activities after 10 cycles.展开更多
[Objectives]To explore the spatiotemporal evolution and development drivers for Taobao villages in Cao County.[Methods]This paper employs GIS spatial analysis methods such as standard deviation ellipse and kernel dens...[Objectives]To explore the spatiotemporal evolution and development drivers for Taobao villages in Cao County.[Methods]This paper employs GIS spatial analysis methods such as standard deviation ellipse and kernel density to characterize the spatial distribution,agglomeration,and correlation features of Taobao villages in Cao County at the township scale.Additionally,geographic detectors are used to explore influencing factors.[Results](i)The number of Taobao villages in Cao County exhibits phased characteristics,progressing through initial,rapid expansion,and stable development stages.(ii)Taobao villages are unevenly distributed,with significant clustering features,primarily concentrated in the southeastern part of the county.In recent years,they have expanded toward the central and northwestern regions,potentially forming a"multi-core"spatial pattern in the future.(iii)The number of garment industries and e-commerce industrial parks consistently remains the primary factor influencing the spatial distribution of Taobao villages.After 2020,the number of wood processing industries and specialty agricultural product processing industries also exerted considerable influence.The number of logistics parks began to have an impact after 2018,albeit a weak one.Other factors had minimal influence on the differentiation of Taobao villages.[Conclusions]The spatiotemporal evolution exhibited diffusion along the"northwest-southeast"axis,with strengthened regional clustering.Government policies and industries significantly influenced the differentiation of Taobao villages,with varying primary factors across different periods.展开更多
Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integr...Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.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
Modifying Ir by foreign metals with oxophilicity is a promising strategy to accelerate the hydrogen oxidation kinetics.However,uncontrolled enrichment and oxidative dissolution of metastable oxophilic dopants in conve...Modifying Ir by foreign metals with oxophilicity is a promising strategy to accelerate the hydrogen oxidation kinetics.However,uncontrolled enrichment and oxidative dissolution of metastable oxophilic dopants in conventional Ir-based alloys impair their activity and durability.Here,we address these challenges by atomically dispersing oxophilic Sn sites within Ir clusters to form dilute alloys.The Sn1-Ir pairs,confined within an atomic-scale lattice,prevent excessive*OH coverage caused by oxophilic site enrichment,while also reducing durability loss due to the dissolution of metastable dopants.Our analysis reveals that the Sn1-Ir pairs facilitate electron transfer between Sn1 and adjacent Ir sites,generating electron-rich Ir atoms and electron-poor Sn atoms.This modulation weakens*H and CO adsorption on Ir sites while enhancing OH adsorption on Sn sites.The resulting catalyst shows improved catalytic hydrogen oxidation performance in alkaline media,with mass activities 6.4 and 10.7 times higher than that of Ir/C and Pt/C,respectively.Under CO poisoning conditions,it retains 90.9%of its initial activity,outperforming both Ir/C and Pt/C.This work offers new perspectives on the design of dual-site catalysts for hydrogen oxidation catalysis.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
Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc...Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc-tion with the domain-based local pair natural orbital(DLPNO)method,has been applied to assess the average binding energies(ABEs)of large benzene clus-ters,specifically(C6H6)13,at the coupled cluster singles and doubles with perturbative triples correction[CCSD(T)]level and the complete basis set(CBS)limit.Utilizing GEBF-DLPNO-CCSD(T)/CBS ABEs as benchmarks,various DFT functionals were evaluated.It was found that several functionals with empirical dispersion correction,including M06-2X-D3,B3LYP-D3(BJ),and PBE-D3(BJ),provide accurate descriptions of the ABEs for(C6H6)13 clusters.Additionally,the M06-2X-D3 functional was used to calculate the ABEs and relative stabili-ties of(C6H6)n clusters for n=11,12,13,14,and 15 revealing that the(C6H6)13 cluster ex-hibits the highest relative stability.These findings align with experimental evidence suggest-ing that n=13 is one of the magic numbers for benzene clusters(C6H6)n,with n≤30.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
Objective This study aimed to investigate the prevalence of HIV pretreatment drug resistance(PDR)and the transmission clusters associated with PDR-related mutations in newly diagnosed,treatmentnaive patients between 2...Objective This study aimed to investigate the prevalence of HIV pretreatment drug resistance(PDR)and the transmission clusters associated with PDR-related mutations in newly diagnosed,treatmentnaive patients between 2020 and 2023 in Dehong prefecture,Yunnan province,China.Methods Demographic information and plasma samples were collected from study participants.PDR was assessed using the Stanford HIV Drug Resistance Database.The Tamura-Nei 93 model within HIVTRACE was employed to compute pairwise matches with a genetic distance of 0.015 substitutions per site.Results Among 948 treatment-naive individuals with eligible sequences,36 HIV subtypes were identified,with unique recombinant forms(URFs)being the most prevalent(18.8%,178/948).The overall prevalence of PDR was 12.4%(118/948),and resistance to non-nucleotide reverse transcriptase inhibitors(NNRTIs),nucleotide reverse transcriptase inhibitors(NRTIs),and protease inhibitors(PIs)was10.7%,1.3%,and 1.6%,respectively.A total of 91 clusters were identified,among which eight showed evidence of PDR strain transmission.The largest PDR-associated cluster consisted of six CRF01_AE drugresistant strains carrying K103N and V179T mutations;five of these individuals had initial CD4^(+)cell counts<200 cells/μL.Conclusion The distribution of HIV subtypes in Dehong is diverse and complex.PDR was moderately prevalent(12.4%)between 2020 and 2023.Evidence of transmission of CRF01_AE strains carrying K103N and V179T mutations was found.Routine surveillance of PDR and the strengthening of control measures are essential to limit the spread of drug-resistance HIV strains.展开更多
LiNO_(3) is known to significantly enhance the reversibility of lithium metal batteries;however,the modification of solvation structures in various solvents and its further impact on the interface have not been fully ...LiNO_(3) is known to significantly enhance the reversibility of lithium metal batteries;however,the modification of solvation structures in various solvents and its further impact on the interface have not been fully revealed.Herein,we systematically studied the evolution of solvation structures with increasing LiNO_(3) concentration in both carbonate and ether electrolytes.The results from molecular dynamics simulations unveil that the Li^(+)solvation structure is less affected in carbonate electrolytes,while in ether electrolytes,there is a significant decrease of solvent molecules in Li^(+)coordination,and a larger average size of Li^(+)solvation structure emerges as LiNO_(3) concentration increases.Notably,the formation of large ion aggregates with size of several nanometers(nano-clusters),is observed in ether-based electrolytes at conventional Li^(+)concentration(1 M)with higher NO_(3)^(-) ratio,which is further proved by infrared spectroscopy and small-angle X-ray scattering experiments.The nano-clusters with abundant anions are endowed with a narrow energy gap of molecular orbitals,contributing to the formation of an inorganic rich electrode/electrolyte interphase that enhances the reversibility of lithium stripping/plating with Coulombic efficiency up to 99.71%.The discovery of nano-clusters elucidates the underlying mechanism linking ions/solvent aggregation states of electrolytes to interfacial stability in advanced battery systems.展开更多
Objective:This study aims to investigate the patterns of symptom occurrence in patients experiencing acute exacerbations of chronic obstructive pulmonary disease(AECOPD).It will explore the composition of symptom clus...Objective:This study aims to investigate the patterns of symptom occurrence in patients experiencing acute exacerbations of chronic obstructive pulmonary disease(AECOPD).It will explore the composition of symptom clusters and analyze the correlation between these clusters and health-related quality of life(HRQoL).Methods:A total of 207 patients with AE-COPD were surveyed from a tertiary grade A hospital.Data collection was conducted using three validated instruments:the Basic Information Questionnaire(BIQ),Disease Symptom Survey Questionnaire(MSAS),and Quality of Life Questionnaire(CAT).Statistical software SPSS 22.0 was used to analyze the correlation between symptom clusters and quality of life.Results:Exploratory factor analysis showed that five major symptom clusters existed in the patients,including the psycho-emotional symptom cluster,the sleep-related symptom cluster,the other side effects symptom cluster,the energy deficiency symptom cluster and the cough-loss of appetite symptom cluster,and the severity of the symptom clusters showed a significant negative correlation with the quality of life of the patients(P<0.05).Conclusion:Strengthening the comprehensive management of symptom clusters in patients with AE-COPD can help to effectively reduce the symptom burden of patients,and then significantly improve their quality of life.展开更多
Two-dimensional(2D)materials loaded with single atoms and clusters are being set at the forefront of catalysis due to their distinctive geometric and electronic features.However,the usually-complicated synthesis proce...Two-dimensional(2D)materials loaded with single atoms and clusters are being set at the forefront of catalysis due to their distinctive geometric and electronic features.However,the usually-complicated synthesis procedures impede in-depth clarification of their catalytic mechanisms.To this end,herein we developed an efficient one-step dimension-reduction carbonization strategy,with which we successfully architected a highly-efficient catalyst for oxygen reduction reaction(ORR),featured with symbiotic cobalt single atoms and clusters decorated in two-dimensional(2D)ultra-thin(3.5 nm thickness)nitrogen-carbon nanosheets.The synergistic effects of the two components afford excellent oxygen reduction activity in alkaline media(E_(1/2)=0.823 V vs.RHE)and thereof a high power density(146.61 mW cm^(-2))in an assembled Zn-air battery.As revealed by theoretical calculations,the cobalt clusters can regulate electrons surrounding those individual atoms and affect the adsorption of intermediate species.As a consequence,the derived active sites of single cobalt atoms lead to a significant improvement of the ORR performance.Thus,our work may fuel interests to delicate architectu re of single atoms and clusters coexisting 2D support toward optimal electrocatalytic performance.展开更多
We investigated the ionization and dissociation processes of ammonia clusters ranging from dimer to pentamer induced by 800-nm femtosecond laser fields.Time-of-flight(TOF)mass spectra of the ammonia clusters were reco...We investigated the ionization and dissociation processes of ammonia clusters ranging from dimer to pentamer induced by 800-nm femtosecond laser fields.Time-of-flight(TOF)mass spectra of the ammonia clusters were recorded over a range of laser intensities from 2.1×10^(12)W/cm^(2) to 5.6×10^(12)W/cm^(2).The protonated ion signals dominate the spectra,which is consistent with the stability of the geometric structures.The ionization and dissociation channels of ammonia clusters are discussed.The competition and switching among observed dissociation channels are revealed by analyzing the variations in the relative ionic yields of specific protonated and unprotonated clusters under different laser intensities.These results indicate that the ionization of the neutral multiple-ammonia units,produced through the dissociation of cluster ions,may start to contribute,as well as the additional processes to consume protonated ions and/or produce unprotonated ions induced by the femtosecond laser fields when the laser intensity is above^4×10^(12)W/cm^(2).These findings provide deeper insights into the ionization and dissociation dynamics in multi-photon ionization experiments involving ammonia clusters.展开更多
Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition.They will significantly impact the electronic/magnetic properties.Moreov...Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition.They will significantly impact the electronic/magnetic properties.Moreover,surface supported atoms are also widely explored for high active and selecting catalysts.Severe deformation,even dipping into the surface,of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces.However,most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions.Here,we develop an accurate graph neural network machine learning potential(MLP)from ab initio data by active learning architecture through fine-tuning pre-trained models,and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters(1-8 atoms)on perfect and various defective MoS2 monolayers.Interestingly,Mo clusters can always sink and embed themselves into MoS2 layers.In contrast,S clusters float on perfect surfaces.On the defective surface,a few S atoms will fill the vacancy and rest S clusters float on the top.Such significant structural reconstructions should be carefully taken into account.展开更多
Taking advantage of the relatively automatic and easy operation procedure,continuous-flow catalysis has become a promising wastewater treatment technique for organic dye removal.However,developing suitable packing cat...Taking advantage of the relatively automatic and easy operation procedure,continuous-flow catalysis has become a promising wastewater treatment technique for organic dye removal.However,developing suitable packing catalysts with favorable activity and low flow resistance remains a challenging task for the construction of continuous-flow catalytic systems.In this paper,we report the preparation of a catalytic module,in which palladium clusters(PdC)are incorporated on defect-rich nitrogen-doped holey graphene(NHG)co-assembled withaluminum silicate fibers(ASFs)(PdC/NHG-ASFs).The resultant PdC/NHG-ASFs composite catalyst exhibits an assembly morphology and can be facilely integrated into a glass reactor to construct an efficient fixed-bed system for continuous-flow catalysis.The corresponding catalytic system demonstrates high processing capacity and excellent durability for the reduction of six N-containing organic dyes owing to the robust hierarchical structure and dualactive components(i.e.,NHG and PdC)of the PdC/NHGASFs composite.The processing rate of the fixed-bed system constructed with the PdC/NHG-ASFs catalyst for the reduction of a representative dye(i.e.,4-nitrophenol)was 1.45×10^(-3)mmol·mg^(-1)·min^(-1),surpassing those previously reported for systems based on metal catalysts.Theoretical calculations show that the activity enhancement in nitroarene reduction reaction originate from the synergistic effect of the two active components.The integration of heterogeneous catalysis and flow-chemistry techniques provides a rational design concept for environmental catalysis,offering a more efficient,scalable,and sustainable approach.展开更多
Dispersing metals from nanoparticles to clusters is often achieved using ligand protection methods,which exhibit unique properties such as suppressing structure-sensitive side reactions.However,this method is limited ...Dispersing metals from nanoparticles to clusters is often achieved using ligand protection methods,which exhibit unique properties such as suppressing structure-sensitive side reactions.However,this method is limited by the use of different metal precursor salts corresponding to different ligands.An alternative approach,the ion exchange(IE)method,can overcome this limitation to some extent.Nevertheless,there is still an urgent need to address the stabilization of metals(especially precious metals)by using IE method.Here,we reported a Pt cluster catalyst prepared mainly by anchoring Pt atoms via O located near the framework Zn in zincosilicate zeolites and riveted by zeolite surface rings after reduction(reduced Pt/Zn-3-IE).The catalyst can achieve an initial propane conversion of 26%in a pure propane atmosphere at 550℃and shows little deactivation even after 7.5 d of operation.Moreover,the alteration of catalyst by the introduction of framework Zn was also highlighted and interpreted.展开更多
基金Supported by the Public Health Emergency Response Mechanism Operation Program of Chinese Center for Disease Control and Prevention(102393220020010000017).
文摘Introduction:Human brucellosis persists as a critical public health challenge in China.Understanding disease clusters and trends is essential for implementing effective control strategies.This study evaluates the epidemiological characteristics and spatiotemporal distribution of brucellosis in China from 2011 to 2023.Methods:Data were obtained from the National Notifiable Disease Reporting System(NNDRS).We conducted descriptive epidemiological analyses and employed SaTScan10.1 and ArcGIS10.7 software to identify disease clusters and generate county(district)-level incidence maps.Results:The incidence of human brucellosis in Chinese mainland increased substantially between 2011 and 2023,rising from 38,151 cases(2.8/100,000)across 834 counties(25.4%)to 70,439 cases(5.2/100,000)across 2,290 counties(76.9%).A significant upward trend in reported incidence emerged during 2018–2023(average annual percentage change(AAPC)=14.9%,P=0.01).Most cases(89.3%)occurred in individuals aged 25–69 years,with an increasing proportion among those aged over 60 years.While 96.1%of cases were reported in northern provincial-level administrative divisions(PLADs),southern regions demonstrated escalating incidence rates and expanding geographical spread.Southern PLADs exhibited a notable annual increase of 31.5%in reported incidence(P<0.01).Counties(districts)with incidence rates exceeding 10 per 100,000 expanded geographically from northwestern pastoral regions to southern areas and from rural to urban settings.Primary spatiotemporal clusters were concentrated in Inner Mongolia and adjacent provincial-level administrative divisions(PLADs),with emerging clusters identified in Yunnan,Guangdong,and Xizang.Conclusions:The human brucellosis epidemic in China continues to intensify,characterized by rebounding incidence rates and broader geographical distribution across counties(districts).While spatiotemporal clusters remain predominantly centered in Inner Mongolia and neighboring regions,targeted interventions and increased resource allocation for high-risk areas and populations are imperative.
基金Under the auspices of National Natural Science Foundation of China(No.41771537)Fundamental Research Funds for the Central Universities
文摘Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.
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.
文摘An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.
文摘Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as the strongly enhanced synergistic interactions between Pt and Co atoms,the obtained Pt-on-Co/C400 catalysts exhib-ited excellent catalytic activity toward the hydrolysis of ammonia borane with an extremely high turnover frequency(TOF)value of 3022 min^(-1)at 303 K.Durability test indicated that the obtained Pt-on-Co/C400 catalysts possessed high catalytic stability,and there were no changes in the catalyst structures and catalytic activities after 10 cycles.
基金Supported by National Natural Science Foundation of China(42171206&42071159).
文摘[Objectives]To explore the spatiotemporal evolution and development drivers for Taobao villages in Cao County.[Methods]This paper employs GIS spatial analysis methods such as standard deviation ellipse and kernel density to characterize the spatial distribution,agglomeration,and correlation features of Taobao villages in Cao County at the township scale.Additionally,geographic detectors are used to explore influencing factors.[Results](i)The number of Taobao villages in Cao County exhibits phased characteristics,progressing through initial,rapid expansion,and stable development stages.(ii)Taobao villages are unevenly distributed,with significant clustering features,primarily concentrated in the southeastern part of the county.In recent years,they have expanded toward the central and northwestern regions,potentially forming a"multi-core"spatial pattern in the future.(iii)The number of garment industries and e-commerce industrial parks consistently remains the primary factor influencing the spatial distribution of Taobao villages.After 2020,the number of wood processing industries and specialty agricultural product processing industries also exerted considerable influence.The number of logistics parks began to have an impact after 2018,albeit a weak one.Other factors had minimal influence on the differentiation of Taobao villages.[Conclusions]The spatiotemporal evolution exhibited diffusion along the"northwest-southeast"axis,with strengthened regional clustering.Government policies and industries significantly influenced the differentiation of Taobao villages,with varying primary factors across different periods.
基金supported by the National Natural Science Foundation of China(Grant No.72571150)Beijing Natural Science Foundation(Grant No.9182015)。
文摘Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.
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financially supported by the National Natural Science Foundation of China(No.12274190)Shandong Provincial Natural Science Foundation(No.ZR2024QB039)the Special Fund for Taishan Scholars Project(No.tsqn202312224 and tsqn202211186)in Shandong Province.The authors acknowledge the support of the Carbon Neutrality Innovation Research Center at Ludong University.
文摘Modifying Ir by foreign metals with oxophilicity is a promising strategy to accelerate the hydrogen oxidation kinetics.However,uncontrolled enrichment and oxidative dissolution of metastable oxophilic dopants in conventional Ir-based alloys impair their activity and durability.Here,we address these challenges by atomically dispersing oxophilic Sn sites within Ir clusters to form dilute alloys.The Sn1-Ir pairs,confined within an atomic-scale lattice,prevent excessive*OH coverage caused by oxophilic site enrichment,while also reducing durability loss due to the dissolution of metastable dopants.Our analysis reveals that the Sn1-Ir pairs facilitate electron transfer between Sn1 and adjacent Ir sites,generating electron-rich Ir atoms and electron-poor Sn atoms.This modulation weakens*H and CO adsorption on Ir sites while enhancing OH adsorption on Sn sites.The resulting catalyst shows improved catalytic hydrogen oxidation performance in alkaline media,with mass activities 6.4 and 10.7 times higher than that of Ir/C and Pt/C,respectively.Under CO poisoning conditions,it retains 90.9%of its initial activity,outperforming both Ir/C and Pt/C.This work offers new perspectives on the design of dual-site catalysts for hydrogen oxidation catalysis.
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.
基金supported by the National Key R&D Program of China(No.2023YFB3712504)the National Natural Science Foundation of China(Nos.22273038,22073043,and 22033004)。
文摘Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc-tion with the domain-based local pair natural orbital(DLPNO)method,has been applied to assess the average binding energies(ABEs)of large benzene clus-ters,specifically(C6H6)13,at the coupled cluster singles and doubles with perturbative triples correction[CCSD(T)]level and the complete basis set(CBS)limit.Utilizing GEBF-DLPNO-CCSD(T)/CBS ABEs as benchmarks,various DFT functionals were evaluated.It was found that several functionals with empirical dispersion correction,including M06-2X-D3,B3LYP-D3(BJ),and PBE-D3(BJ),provide accurate descriptions of the ABEs for(C6H6)13 clusters.Additionally,the M06-2X-D3 functional was used to calculate the ABEs and relative stabili-ties of(C6H6)n clusters for n=11,12,13,14,and 15 revealing that the(C6H6)13 cluster ex-hibits the highest relative stability.These findings align with experimental evidence suggest-ing that n=13 is one of the magic numbers for benzene clusters(C6H6)n,with n≤30.
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 Key Research and Development Program of China(2022YFC2305201)National Natural Science Foundation of China(71874168)。
文摘Objective This study aimed to investigate the prevalence of HIV pretreatment drug resistance(PDR)and the transmission clusters associated with PDR-related mutations in newly diagnosed,treatmentnaive patients between 2020 and 2023 in Dehong prefecture,Yunnan province,China.Methods Demographic information and plasma samples were collected from study participants.PDR was assessed using the Stanford HIV Drug Resistance Database.The Tamura-Nei 93 model within HIVTRACE was employed to compute pairwise matches with a genetic distance of 0.015 substitutions per site.Results Among 948 treatment-naive individuals with eligible sequences,36 HIV subtypes were identified,with unique recombinant forms(URFs)being the most prevalent(18.8%,178/948).The overall prevalence of PDR was 12.4%(118/948),and resistance to non-nucleotide reverse transcriptase inhibitors(NNRTIs),nucleotide reverse transcriptase inhibitors(NRTIs),and protease inhibitors(PIs)was10.7%,1.3%,and 1.6%,respectively.A total of 91 clusters were identified,among which eight showed evidence of PDR strain transmission.The largest PDR-associated cluster consisted of six CRF01_AE drugresistant strains carrying K103N and V179T mutations;five of these individuals had initial CD4^(+)cell counts<200 cells/μL.Conclusion The distribution of HIV subtypes in Dehong is diverse and complex.PDR was moderately prevalent(12.4%)between 2020 and 2023.Evidence of transmission of CRF01_AE strains carrying K103N and V179T mutations was found.Routine surveillance of PDR and the strengthening of control measures are essential to limit the spread of drug-resistance HIV strains.
基金supported by the National Natural Science Foundation of China(No.22372083,52201259)the National Key R&D Program of China(2021YFB2500300)+2 种基金the Fundamental Research Funds for the Central Universities:Nankai University(63241607)the Natural Science Foundation of Tianjin(No.22JCZDJC00380)the Young Elite Scientist Sponsorship Program by CAST.
文摘LiNO_(3) is known to significantly enhance the reversibility of lithium metal batteries;however,the modification of solvation structures in various solvents and its further impact on the interface have not been fully revealed.Herein,we systematically studied the evolution of solvation structures with increasing LiNO_(3) concentration in both carbonate and ether electrolytes.The results from molecular dynamics simulations unveil that the Li^(+)solvation structure is less affected in carbonate electrolytes,while in ether electrolytes,there is a significant decrease of solvent molecules in Li^(+)coordination,and a larger average size of Li^(+)solvation structure emerges as LiNO_(3) concentration increases.Notably,the formation of large ion aggregates with size of several nanometers(nano-clusters),is observed in ether-based electrolytes at conventional Li^(+)concentration(1 M)with higher NO_(3)^(-) ratio,which is further proved by infrared spectroscopy and small-angle X-ray scattering experiments.The nano-clusters with abundant anions are endowed with a narrow energy gap of molecular orbitals,contributing to the formation of an inorganic rich electrode/electrolyte interphase that enhances the reversibility of lithium stripping/plating with Coulombic efficiency up to 99.71%.The discovery of nano-clusters elucidates the underlying mechanism linking ions/solvent aggregation states of electrolytes to interfacial stability in advanced battery systems.
文摘Objective:This study aims to investigate the patterns of symptom occurrence in patients experiencing acute exacerbations of chronic obstructive pulmonary disease(AECOPD).It will explore the composition of symptom clusters and analyze the correlation between these clusters and health-related quality of life(HRQoL).Methods:A total of 207 patients with AE-COPD were surveyed from a tertiary grade A hospital.Data collection was conducted using three validated instruments:the Basic Information Questionnaire(BIQ),Disease Symptom Survey Questionnaire(MSAS),and Quality of Life Questionnaire(CAT).Statistical software SPSS 22.0 was used to analyze the correlation between symptom clusters and quality of life.Results:Exploratory factor analysis showed that five major symptom clusters existed in the patients,including the psycho-emotional symptom cluster,the sleep-related symptom cluster,the other side effects symptom cluster,the energy deficiency symptom cluster and the cough-loss of appetite symptom cluster,and the severity of the symptom clusters showed a significant negative correlation with the quality of life of the patients(P<0.05).Conclusion:Strengthening the comprehensive management of symptom clusters in patients with AE-COPD can help to effectively reduce the symptom burden of patients,and then significantly improve their quality of life.
基金supported by the National Natural Science Foundation of China(51872115 and 12234018)Beijing Synchrotron Radiation Facility(BSRF)4B9A.
文摘Two-dimensional(2D)materials loaded with single atoms and clusters are being set at the forefront of catalysis due to their distinctive geometric and electronic features.However,the usually-complicated synthesis procedures impede in-depth clarification of their catalytic mechanisms.To this end,herein we developed an efficient one-step dimension-reduction carbonization strategy,with which we successfully architected a highly-efficient catalyst for oxygen reduction reaction(ORR),featured with symbiotic cobalt single atoms and clusters decorated in two-dimensional(2D)ultra-thin(3.5 nm thickness)nitrogen-carbon nanosheets.The synergistic effects of the two components afford excellent oxygen reduction activity in alkaline media(E_(1/2)=0.823 V vs.RHE)and thereof a high power density(146.61 mW cm^(-2))in an assembled Zn-air battery.As revealed by theoretical calculations,the cobalt clusters can regulate electrons surrounding those individual atoms and affect the adsorption of intermediate species.As a consequence,the derived active sites of single cobalt atoms lead to a significant improvement of the ORR performance.Thus,our work may fuel interests to delicate architectu re of single atoms and clusters coexisting 2D support toward optimal electrocatalytic performance.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.92261201,12134005,12334011)。
文摘We investigated the ionization and dissociation processes of ammonia clusters ranging from dimer to pentamer induced by 800-nm femtosecond laser fields.Time-of-flight(TOF)mass spectra of the ammonia clusters were recorded over a range of laser intensities from 2.1×10^(12)W/cm^(2) to 5.6×10^(12)W/cm^(2).The protonated ion signals dominate the spectra,which is consistent with the stability of the geometric structures.The ionization and dissociation channels of ammonia clusters are discussed.The competition and switching among observed dissociation channels are revealed by analyzing the variations in the relative ionic yields of specific protonated and unprotonated clusters under different laser intensities.These results indicate that the ionization of the neutral multiple-ammonia units,produced through the dissociation of cluster ions,may start to contribute,as well as the additional processes to consume protonated ions and/or produce unprotonated ions induced by the femtosecond laser fields when the laser intensity is above^4×10^(12)W/cm^(2).These findings provide deeper insights into the ionization and dissociation dynamics in multi-photon ionization experiments involving ammonia clusters.
基金supported by the National Natural Science Foundation of China(Grant No.12374253,12074053,12004064)J.G.thanks the Foreign talents project(G2022127004L),The authors also acknowledge computer support from the Shanghai Supercomputer Center,the DUT Supercomputing Center,and the Tianhe supercomputer of Tianjin Center.
文摘Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition.They will significantly impact the electronic/magnetic properties.Moreover,surface supported atoms are also widely explored for high active and selecting catalysts.Severe deformation,even dipping into the surface,of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces.However,most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions.Here,we develop an accurate graph neural network machine learning potential(MLP)from ab initio data by active learning architecture through fine-tuning pre-trained models,and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters(1-8 atoms)on perfect and various defective MoS2 monolayers.Interestingly,Mo clusters can always sink and embed themselves into MoS2 layers.In contrast,S clusters float on perfect surfaces.On the defective surface,a few S atoms will fill the vacancy and rest S clusters float on the top.Such significant structural reconstructions should be carefully taken into account.
基金financially supported by the Key Research and Development Program of Hubei Province(No.2022BAA026)the Open Project of Hubei Key Laboratory of Novel Reactor and Green Chemical Technology(No.NRGC202203)+3 种基金the Open/Innovation Project of Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education(No.LCX202203)the Open Project of Key Laboratory of Green Chemical Engineering Process of Ministry of Education(No.GCX2022005)the Open/Innovation Project of Key Laboratory of Novel Biomass-Based Environmental and Energy Materials in Petroleum and Chemical Industry(No.2022BEEA06)the Innovation and Entrepreneurship Training Program Funded by Wuhan Institute of Technology(No.202310490007)
文摘Taking advantage of the relatively automatic and easy operation procedure,continuous-flow catalysis has become a promising wastewater treatment technique for organic dye removal.However,developing suitable packing catalysts with favorable activity and low flow resistance remains a challenging task for the construction of continuous-flow catalytic systems.In this paper,we report the preparation of a catalytic module,in which palladium clusters(PdC)are incorporated on defect-rich nitrogen-doped holey graphene(NHG)co-assembled withaluminum silicate fibers(ASFs)(PdC/NHG-ASFs).The resultant PdC/NHG-ASFs composite catalyst exhibits an assembly morphology and can be facilely integrated into a glass reactor to construct an efficient fixed-bed system for continuous-flow catalysis.The corresponding catalytic system demonstrates high processing capacity and excellent durability for the reduction of six N-containing organic dyes owing to the robust hierarchical structure and dualactive components(i.e.,NHG and PdC)of the PdC/NHGASFs composite.The processing rate of the fixed-bed system constructed with the PdC/NHG-ASFs catalyst for the reduction of a representative dye(i.e.,4-nitrophenol)was 1.45×10^(-3)mmol·mg^(-1)·min^(-1),surpassing those previously reported for systems based on metal catalysts.Theoretical calculations show that the activity enhancement in nitroarene reduction reaction originate from the synergistic effect of the two active components.The integration of heterogeneous catalysis and flow-chemistry techniques provides a rational design concept for environmental catalysis,offering a more efficient,scalable,and sustainable approach.
文摘Dispersing metals from nanoparticles to clusters is often achieved using ligand protection methods,which exhibit unique properties such as suppressing structure-sensitive side reactions.However,this method is limited by the use of different metal precursor salts corresponding to different ligands.An alternative approach,the ion exchange(IE)method,can overcome this limitation to some extent.Nevertheless,there is still an urgent need to address the stabilization of metals(especially precious metals)by using IE method.Here,we reported a Pt cluster catalyst prepared mainly by anchoring Pt atoms via O located near the framework Zn in zincosilicate zeolites and riveted by zeolite surface rings after reduction(reduced Pt/Zn-3-IE).The catalyst can achieve an initial propane conversion of 26%in a pure propane atmosphere at 550℃and shows little deactivation even after 7.5 d of operation.Moreover,the alteration of catalyst by the introduction of framework Zn was also highlighted and interpreted.
