Template-free synthesis of hierarchically macro-mesoporous Mn-TiO2 catalysts for selective reduction of NO with NH3

This study described a template-flee method for the synthesis of hierarchically macro-mesoporous Mn- TiO2 catalysts. The promoting effect of Mn doping and the hierarchically macro-mesoporous architecture on TiO2 based catalysts was also investigated for the selective reduction of NO with NH3. The results show that the catalytic performance of TiO2 based catalysts was improved greatly after Mn doping. Meanwhile, the Mn- TiO2 catalyst with the hierarchically macro-mesoporous architecture has a better catalytic activity than that without such an architecture.

Hierarchically porous Co@N-doped carbon fiber assembled by MOF-derived hollow polyhedrons enables effective electronic/mass transport:An advanced 1D oxygen reduction catalyst for Zn-air battery

Developing advanced oxygen reduction reaction(ORR)electrocatalysts with rapid mass/electron transport as well as conducting relevant kinetics investigations is essential for energy technologies,but both still face ongoing challenges.Herein,a facile approach was reported for achieving the highly dispersed Co nanoparticles anchored hierarchically porous N-doped carbon fibers(Co@N-HPCFs),which were assembled by core-shell MOFs-derived hollow polyhedrons.Notably,the unique one-dimensional(1D)carbon fibers with hierarchical porosity can effectively improve the exposure of active sites and facilitate the electron transfer and mass transfer,resulting in the enhanced reaction kinetics.As a result,the ORR performance of the optimal Co@N-HPCF catalysts remarkably outperforms that of commercial Pt/C in alkaline solution,reaching a limited diffusion current density(J)of 5.85 m A cm^(-2)and a half-wave potential(E_(1/2))of 0.831 V.Particularly,the prepared Co@N-HPCF catalysts can be used as an excellent air-cathode for liquid/solid-state Zn-air batteries,exhibiting great potentiality in portable/wearable energy devices.Furthermore,the reaction kinetic during ORR process is deeply explored by finite element simulation,so as to intuitively grasp the kinetic control region,diffusion control region,and mixing control region of the ORR process,and accurately obtain the relevant kinetic parameters.This work offers an effective strategy and a reliable theoretical basis for the engineering of first-class ORR electrocatalysts with fast electronic/mass transport.

Amine-functionalized hierarchically porous carbon supported Pd nanocatalysts for highly efficient H2 generation from formic acid with fast-diffusion channels

Formic acid(FA)has come to be considered a potential candidate for hydrogen storage,and the development of efficient catalysts for H2releasing is crucial for realizing the sustainable process from FA.Herein,we have developed the ultrafine Pd nanoparticle(NPs)with amine-functionalized carbon as a support,which was found to show an excellent catalytic activity in H_(2)generation from FA dehydrogenation.The synergetic mechanism between amine-group and Pd active site was demonstrated to facilitate H2generation byβ-hydride elimination.Moreover,the texture of support for Pd NPs also plays an important role in determining the reactivity of FA,since the diffusion of gaseous products makes the kinetics of diffusion as a challenge in this high performance Pd catalysts.As a result,the as-prepared Pd/NH_(2)-TPC catalyst with the small sized Pd nanoparticles and the hierarchically porous structures shows a turnover of frequency(TOF)value of 4312 h^(-1)for the additive-free FA dehydrogenation at room temperature,which is comparable to the most promising heterogeneous catalysts.Our results demonstrated that the intrinsic catalytic activities of active site as well as the porous structure of support are both important factors in determining catalytic performances in H2generation from FA dehydrogenation,which is also helpful to develop high-activity catalysts for other advanced gas-liquid-solid reactions systems.

A hierarchically structured tin-cobalt composite with an enhanced electronic effect for high-performance CO_(2) electroreduction in a wide potential range

Earth-abundant and nontoxic Sn-based materials have been regarded as promising catalysts for the electrochemical conversion of CO_(2)to C1 products,e.g.,CO and formate.However,it is still difficult for Snbased materials to obtain satisfactory performance at low-to-moderate overpotentials.Herein,a simple and facile electrospinning technique is utilized to prepare a composite of a bimetallic Sn-Co oxide/carbon matrix with a hollow nanotube structure(Sn Co-HNT).Sn Co-HNT can maintain>90%faradaic efficiencies for C1 products within a wide potential range from-0.6 VRHE to-1.2 VRHE,and a highest 94.1%selectivity towards CO in an H-type cell.Moreover,a 91.2%faradaic efficiency with a 241.3 m A cm^(-2)partial current density for C1 products could be achieved using a flow cell.According to theoretical calculations,the fusing of Sn/Co oxides on the carbon matrix accelerates electron transfer at the atomic level,causing electron deficiency of Sn centers and reversible variation between Co^(2+)and Co^(3+)centers.The synergistic effect of the Sn/Co composition improves the electron affinity of the catalyst surface,which is conducive to the adsorption and stabilization of key intermediates and eventually increases the catalytic activity in CO_(2)electroreduction.This study could provide a new strategy for the construction of oxide-derived catalysts for CO_(2)electroreduction.

Hierarchically porous Fe/N/S/C nanospheres with high-content of Fe-Nx for enhanced ORR and Zn-air battery performance

Heteroatom-doped carbon-based transition-metal single-atom catalysts(SACs) are promising electrocatalysts for oxygen reduction reaction(ORR). Herein, with the aid of hierarchically porous silica as hard template, a facile and general melting perfusion and mesopore-confined pyrolysis method was reported to prepare single-atomic Fe/N–S-doped carbon catalyst(FeNx/NC-S) with hierarchically porous structure and well-defined morphology. The FeNx/NC-S exhibited excellent ORR activity with a half-wave potential(E_(1/2)) of 0.92 V, and a lower overpotential of 320 mV at a current density of 10 mA cm^(-2)for OER under alkaline condition. The remarkable electrocatalysis performance can be attributed to the hierarchically porous carbon nanospheres with S doping and high content of Fe-Nx sites(up to 3.7 wt% of Fe), resulting from the nano-confinement effect of the hierarchically porous silica spheres(NKM-5) during the pyrolysis process. The rechargeable Zn-air battery with FeNx/NC-S as a cathode catalyst demonstrated a superior power density of 194.5 mW cm-2charge–discharge stability. This work highlights a new avenue to design advanced SACs for efficient sustainable energy storage and conversion.

Facile synthesis of hierarchical NaX zeolite from natural kaolinite for efficient Knoevenagel condensation

Zeolite catalysts have found extensive applications in the synthesis of various fine chemicals.However,the micropores of zeolites impose diffusion limitations on bulky molecules,greatly reducing the catalytic efficiency.Herein,we explore an economic and environmentally friendly method for synthesizing hierarchical NaX zeolite that exhibits improved catalytic performance in the Knoevenagel condensation reaction for producing the useful fine chemical 2-cyano-3-phenylacrylate.The synthesis was achieved via a low-temperature activation of kaolinite and subsequent in-situ transformation strategy without any template or seed.Systematic characterizations reveal that the synthesized NaX zeolite has both intercrystalline and intra-crystalline mesopores,smaller crystal size,and larger external specific surface area compared to commercial NaX zeolite.Detailed mechanism investigations show that the inter-crystalline mesopores are generated by stacking smaller crystals formed from in-situ crystallization of the depolymerized kaolinite,and the intra-crystalline mesopores are inherited from the pores in the depolymerized kaolinite.This synthesis strategy provides an energy-saving and effective way to construct hierarchical zeolites,which may gain wide applications in fine chemical manufacturing.

Kinetic-Thermodynamic Promotion Engineering toward High-Density Hierarchical and Zn-Doping Activity-Enhancing ZnNiO@CF for High-Capacity Desalination

Despite the promising potential of transition metal oxides(TMOs)as capacitive deionization(CDI)electrodes,the actual capacity of TMOs electrodes for sodium storage is significantly lower than the theoretical capacity,posing a major obstacle.Herein,we prepared the kinetically favorable Zn_(x)Ni_(1−x)O electrode in situ growth on carbon felt(Zn_(x)Ni_(1−x)O@CF)through constraining the rate of OH^(−)generation in the hydrothermal method.Zn_(x)Ni_(1−x)O@CF exhibited a high-density hierarchical nanosheet structure with three-dimensional open pores,benefitting the ion transport/electron transfer.And tuning the moderate amount of redox-inert Zn-doping can enhance surface electroactive sites,actual activity of redox-active Ni species,and lower adsorption energy,promoting the adsorption kinetic and thermodynamic of the Zn_(0.2)Ni_(0.8)O@CF.Benefitting from the kinetic-thermodynamic facilitation mechanism,Zn_(0.2)Ni_(0.8)O@CF achieved ultrahigh desalination capacity(128.9 mgNaCl g^(-1)),ultra-low energy consumption(0.164 kW h kgNaCl^(-1)),high salt removal rate(1.21 mgNaCl g^(-1) min^(-1)),and good cyclability.The thermodynamic facilitation and Na^(+)intercalation mechanism of Zn_(0.2)Ni_(0.8)O@CF are identified by the density functional theory calculations and electrochemical quartz crystal microbalance with dissipation monitoring,respectively.This research provides new insights into controlling electrochemically favorable morphology and demonstrates that Zn-doping,which is redox-inert,is essential for enhancing the electrochemical performance of CDI electrodes.

Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

The optimization of the rule base of a fuzzy logic system (FLS) based on evolutionary algorithm has achievednotable results. However, due to the diversity of the deep structure in the hierarchical fuzzy system (HFS) and thecorrelation of each sub fuzzy system, the uncertainty of the HFS’s deep structure increases. For the HFS, a largenumber of studies mainly use fixed structures, which cannot be selected automatically. To solve this problem, thispaper proposes a novel approach for constructing the incremental HFS. During system design, the deep structureand the rule base of the HFS are encoded separately. Subsequently, the deep structure is adaptively mutated basedon the fitness value, so as to realize the diversity of deep structures while ensuring reasonable competition amongthe structures. Finally, the differential evolution (DE) is used to optimize the deep structure of HFS and theparameters of antecedent and consequent simultaneously. The simulation results confirm the effectiveness of themodel. Specifically, the root mean square errors in the Laser dataset and Friedman dataset are 0.0395 and 0.0725,respectively with rule counts of rules is 8 and 12, respectively.When compared to alternative methods, the resultsindicate that the proposed method offers improvements in accuracy and rule counts.

The Immense Impact of Reverse Edges on Large Hierarchical Networks

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

Verifying hierarchical network nonlocality in general quantum networks

Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.

Structural Engineering of Hierarchical Magnetic/Carbon Nanocomposites via In Situ Growth for High-Efficient Electromagnetic Wave Absorption

Materials exhibiting high-performance electromagnetic wave absorption have garnered considerable scientific and technological attention,yet encounter significant challenges.Developing new materials and innovative structural design concepts is crucial for expanding the application field of electromagnetic wave absorption.Particularly,hierarchical structure engineering has emerged as a promising approach to enhance the physical and chemical properties of materials,providing immense potential for creating versatile electromagnetic wave absorption materials.Herein,an exceptional multi-dimensional hierarchical structure was meticulously devised,unleashing the full microwave attenuation capabilities through in situ growth,selfreduction,and multi-heterogeneous interface integration.The hierarchical structure features a three-dimensional carbon framework,where magnetic nanoparticles grow in situ on the carbon skeleton,creating a necklace-like structure.Furthermore,magnetic nanosheets assemble within this framework.Enhanced impedance matching was achieved by precisely adjusting component proportions,and intelligent integration of diverse interfaces bolstered dielectric polarization.The obtain Fe_(3)O_(4)-Fe nanoparticles/carbon nanofibers/Al-Fe_(3)O_(4)-Fe nanosheets composites demonstrated outstanding performance with a minimum reflection loss(RLmin)value of−59.3 dB and an effective absorption bandwidth(RL≤−10 dB)extending up to 5.6 GHz at 2.2 mm.These notable accomplishments offer fresh insights into the precision design of high-efficient electromagnetic wave absorption materials.

Achieving the synergistic of strength and ductility in Mg-15Gd-1Zn-0.4Zr alloy with hierarchical structure

Currently,the hierarchical structure is one of the most effective means to enhance the strength and plasticity of metal materials,since the strain localization can be effectively delayed by the coordination of the unique microstructure.In this study,a hierarchical structure of Mg-15Gd-1Zn-0.4Zr(GZ151K)alloys containing grain,twin,and precipitation structural units was prepared by ultrasonic surface rolling process(USRP)and recrystallization annealing(RU).The results showed that the stress gradient generated by USRP formed a twin gradient structure,which will activate the twin-assisted precipitation(TAP)effect and twin-induced recrystallization(TIR)effect during RU.Then,the twin gradient structure transformed into a twin-precipitation gradient structure,and finally into a hierarchical structure with grain-twinprecipitation as the increasement of recrystallization degree.Besides,the dual gradient structure with twin and precipitation structural units had the highest strength and microhardness owing to the precipitation strengthening.However,the hierarchical structure with grain,twin,and precipitation structural units exhibited the most excellent combination of strength and plasticity under grain refinement and precipitation strengthening.

A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.

Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion

In the realm of data privacy protection,federated learning aims to collaboratively train a global model.However,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global model.Utilizing shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized solution.Nonetheless,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model performance.To tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local classifiers.Additionally,FedFCD employs a hierarchical optimization strategy to flexibly optimize model parameters.Through experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of FedFCD.For instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning approaches.Furthermore,extended experiments confirm the robustness of FedFCD across various hyperparameter values.

Universal architecture and defect engineering dual strategy for hierarchical antimony phosphate composite toward fast and durable sodium storage

Antimony(Sb)-ba sed anode materials are feasible candidates for sodium-ion batteries(SIBs) due to their high theoretical specific capacity and excellent electrical conductivity.However,they still suffer from volume distortion,structural collapse,and ionic conduction interruption upon cycling.Herein,a hierarchical array-like nanofiber structure was designed to address these limitations by combining architecture engineering and anion tuning strategy,in which SbPO_(4-x) with oxygen vacancy nanosheet arrays are anchored on the surface of interwoven carbon nanofibers(SbPO_(4-x)@CNFs).In particular,bulky PO_(4)^(3-) anions mitigate the large volume distortion and generate Na_(3)PO_(4) with high ionic conductivity,collectively improving cyclic stability and ionic transport efficiency.The abundant oxygen vacancies substantially boost the intrinsic electronic conductivity of SbPO_4,further accelerating the reaction dynamics.In addition,hierarchical fibrous structures provide abundant active sites,construct efficient conducting networks,and enhance the electron/ion transport capacity.Benefiting from the advanced structural design,the SbPO_(4-x)@CNFs electrodes exhibit outstanding cycling stability(1000 cycles at 1.0 A g^(-1) with capacity decay of 0.05% per cycle) and rapid sodium storage performance(293.8 mA h g^(-1) at 5.0 A g^(-1)).Importantly,systematic in-/ex-situ techniques have revealed the "multi-step conversion-alloying" reaction process and the "battery-capacitor dual-mode" sodium-storage mechanism.This work provides valuable insights into the design of anode materials for advanced SIBs with elevated stability and superior rate performance.

Leveraging Uncertainty for Depth-Aware Hierarchical Text Classification

Hierarchical Text Classification(HTC)aims to match text to hierarchical labels.Existing methods overlook two critical issues:first,some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target.Second,error propagation occurs when a misclassification at a parent node propagates down the hierarchy,ultimately leading to inaccurate predictions at the leaf nodes.To address these limitations,we propose an uncertainty-guided HTC depth-aware model called DepthMatch.Specifically,we design an early stopping strategy with uncertainty to identify incomplete matching between text and labels,classifying them into the corresponding parent node labels.This approach allows us to dynamically determine the classification depth by leveraging evidence to quantify and accumulate uncertainty.Experimental results show that the proposed DepthMatch outperforms recent strong baselines on four commonly used public datasets:WOS(Web of Science),RCV1-V2(Reuters Corpus Volume I),AAPD(Arxiv Academic Paper Dataset),and BGC.Notably,on the BGC dataset,it improvesMicro-F1 andMacro-F1 scores by at least 1.09%and 1.74%,respectively.

A hierarchical salt-rejection strategy for sustainable and high-efficiency solar-driven desalination

Solar steam generation(SSG)is widely regarded as one of the most sustainable technologies for seawater desalination.However,salt fouling severely compromises the evaporation performance and lifetime of evaporators,limiting their practical applications.Herein,we propose a hierarchical salt-rejection(HSR)strategy to prevent salt precipitation during long-term evaporation while maintaining a rapid evaporation rate,even in high-salinity brine.The salt diffusion process is segmented into three steps—insulation,branching diffusion,and arterial transport—that significantly enhance the salt-resistance properties of the evaporator.Moreover,the HSR strategy overcomes the tradeoff between salt resistance and evaporation rate.Consequently,a high evaporation rate of 2.84 kg m^(-2) h^(-1),stable evaporation for 7 days cyclic tests in 20 wt%NaCl solution,and continuous operation for 170 h in natural seawater under 1 sun illumination were achieved.Compared with control evaporators,the HSR evaporator exhibited a>54%enhancement in total water evaporation mass during 24 h continuous evaporation in 20 wt%salt water.Furthermore,a water collection device equipped with the HSR evaporator realized a high water purification rate(1.1 kg m^(-2) h^(-1)),highlighting its potential for agricultural applications.

Kinetic-boosted CO_(2) electroreduction to formate via synergistic electric-thermal field on hierarchical bismuth with amorphous layer

Electrocatalytic converting CO_(2) into chemical products has emerged as a promising approach to achieving carbon neutrality.Herein,we report a bismuth-based catalyst with high curvature terminal and amorphous layer which fabricated via two-step electrodeposition achieves stable formate output in a wide voltage window of 600 mV.The Faraday efficiency(FE) of formate reached up to 99.4% at-0.8 V vs.RHE and it remained constant for more than 92 h at-15 mA cm^(-2).More intriguingly,FE formate of95.4% can be realized at a current density of industrial grade(-667.7 mA cm^(-2)) in flow cell.The special structure promoted CO_(2) adsorption and reduced its activation energy and enhanced the electric-thermal field and K^(+) enrichment which accelerated the reaction kinetics.In situ spectroscopy and theoretical calculation further confirmed that the introduction of amorphous structure is beneficial to adsorpting CO_(2)and stabling*OCHO intermediate.This work provides special insights to fabricate efficient electrocatalysts by means of structural and crystal engineering and makes efforts to realize the industrialization of bismuth-based catalysts.

Cross-Target Stance Detection with Sentiments-Aware Hierarchical Attention Network

The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language patterns.Traditional stance detection methods often struggle with understanding limited context and have insufficient generalization across diverse sentiments and semantic structures.This paper focuses on effectively mining and utilizing sentimentsemantics knowledge for stance knowledge transfer and proposes a sentiment-aware hierarchical attention network(SentiHAN)for cross-target stance detection.SentiHAN introduces an improved hierarchical attention network designed to maximize the use of high-level representations of targets and texts at various fine-grain levels.This model integrates phrase-level combinatorial sentiment knowledge to effectively bridge the knowledge gap between known and unknown targets.By doing so,it enables a comprehensive understanding of stance representations for unknown targets across different sentiments and semantic structures.The model’s ability to leverage sentimentsemantics knowledge enhances its performance in detecting stances that may not be directly observable from the immediate context.Extensive experimental results indicate that SentiHAN significantly outperforms existing benchmark methods in terms of both accuracy and robustness.Moreover,the paper employs ablation studies and visualization techniques to explore the intricate relationship between sentiment and stance.These analyses further confirm the effectiveness of sentence-level combinatorial sentiment knowledge in improving stance detection capabilities.

Integrated adsorption and photocatalytic removal of methylene blue dye from aqueous solution by hierarchical Nb_(2)O_(5)@PAN/PVDF/ANO composite nanofibers

This work presents the development of hierarchical niobium pentoxide(Nb_(2)O_(5))-based composite nanofiber membranes for integrated adsorption and photocatalytic degradation of methylene blue(MB)pollutants from aqueous solutions.The Nb_(2)O_(5) nanorods were vertically grown using a hydrothermal process on a base electrospun nanofibrous membrane made of polyacrylonitrile/polyvinylidene fluoride/ammonium niobate(V)oxalate hydrate(Nb_(2)O_(5)@PAN/PVDF/ANO).They were characterized using field-emission scanning electron microscopy(FE-SEM),X-ray diffraction(XRD)analysis,and Fourier transform infrared(FTIR)spectroscopy.These composite nanofibers possessed a narrow optical bandgap energy of 3.31 eV and demonstrated an MB degradation efficiency of 96%after 480 min contact time.The pseudo-first-order kinetic study was also conducted,in which Nb_(2)O_(5)@PAN/PVDF/ANO nanofibers have kinetic constant values of 1.29×10^(-2) min^(-1) and 0.30×10^(-2) min^(-1) for adsorption and photocatalytic degradation of MB aqueous solutions,respectively.These values are 17.7 and 7.8 times greater than those of PAN/PVDF/ANO nanofibers without Nb_(2)O_(5) nanostructures.Besides their outstanding photocatalytic performance,the developed membrane materials exhibit advantageous characteristics in recycling,which subsequently widen their practical use in environmental remediation applications.