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.

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.

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.

High-Performance All-Printed Flexible Micro-Supercapacitors with Hierarchical Encapsulation

Printed micro-supercapacitors(MSCs)have shown broad prospect in flexible and wearable electronics.Most of previous studies focused on printing the electrochemically active materials paying less attention to other key components like current collectors and electrolytes.This study presents an allprinting strategy to fabricate in-plane flexible and substrate-free MSCs with hierarchical encapsulation.This new type of“all-in-one”MSC is constructed by encapsulating the in-plane interdigital current collectors and electrodes within the polyvinyl-alcohol-based hydrogel electrolyte via sequential printing.The bottom electrolyte layer of this fully printed MSCs helps protect the device from the limitation of conventional substrate,showing excellent flexibility.The MSCs maintain a high capacitance retention of 96.84%even in a completely folded state.An optimal electrochemical performance can be achieved by providing ample and shorter transport paths for ions.The MSCs using commercial activated carbon as the active material are endowed with a high specific areal capacitance of 1892.90 mF cm^(-2)at a current density of 0.3 mA cm^(-2),and an outstanding volumetric energy density of 9.20 mWh cm^(-3)at a volumetric power density of 6.89 mW cm^(-3).For demonstration,a thermo-hygrometer is stably powered by five MSCs which are connected in series and wrapped onto a glass rod.This low-cost and versatile all-printing strategy is believed to diversify the application fields of MSCs with high capacitance and excellent flexibility.

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.

Lignite-Based Hierarchical Porous C/SiO_(x)Composites as High-Performance Anode for Potassium-Ion Batteries

Silicon oxide(SiO_(x),0<x≤2)has been recognized as a prominent anode material in lithium-ion batteries and sodium-ion batteries due to its high theoretical capacity,suitable electrochemical potential,and earth abundance.However,it is intrinsically poor electronic conductivity and excessive volume expansion during potassiation/depotassiation process hinder its application in potassium-ion batteries.Herein,we reported a hierarchical porous C/SiO_(x)potassium-ion batteries anode using lignite as raw material via a one-step carbonization and activation method.The amorphous C skeleton around SiO_(x)particles can effectively buffer the volume expansion,and improve the ionic/electronic conductivity and structural integrity,achieving outstanding rate capability and cyclability.As expected,the obtained C/SiO_(x)composite delivers a superb specific capacity of 370 mAh g^(-1)at 0.1 A g^(-1)after 100 cycles as well as a highly reversible capacity of 208 mAh g^(-1)after 1200 cycles at 1.0 A g^(-1).Moreover,the potassium ion storage mechanism of C/SiO_(x)electrodes was investigated by ex-situ X-ray diffraction and transmission electron microscopy,revealing the formation of reversible products of K_(6.8)Si_(45.3)and K_(4)SiO_(4),accompanied by generation of irreversible K2O after the first cycle.This work sheds light on designing low-cost Si-based anode materials for high-performance potassium-ion batteries and beyond.

Hierarchical Self‑Assembly of Injectable Alginate Supramolecular Nanofibril Hydrogels for Hemostasis In Vivo

Biomass-based supramolecular hydrogels are widely used in the biomedical field due to their favorable biocompatibility and outstanding mechanical properties.However,the preparation of injectable polysaccharide-based hydrogels has proven to be a significant challenge.Here we have employed a simple poor-solvent strategy to prepare alginate supramolecular hydrogels via a hierarchical self-assembly process,including micellization,micelles alignment to form nanofilament,and nanofibrils formation.The alginate supramolecular fibrillar hydrogels exhibit excellent mechanical properties and shear recoverability,meeting the requirements of injectable hydrogels.Furthermore,the presence of alginate and its fibrillar structures imparts superb hemostasis properties and the inherent biocompatibility to these hydrogels.Therefore,this simple and intriguing approach has the potential to develop polysaccharide-based hydrogels for hemostasis in wound within the biomedical fields.

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.

Hierarchical structures on platinum-iridium substrates enhancing conducting polymer adhesion

Conducting polymers(CPs),including poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)(PEDOT:PSS),are promising coating materials for neural electrodes.However,the weak adhesion of CP coatings to substrates such as platinum-iridium is a significant challenge that limits their practical application.To address this issue,we used femtosecond laser-prepared hierarchical structures on platinum-iridium(Pt-Ir)substrates to enhance the adhesion of PEDOT:PSS coatings.Next,we used cyclic voltammetry(CV)stress and accelerated aging tests to evaluate the stability of both drop cast and electrodeposited PEDOT:PSS coatings on Pt-Ir substrates,both with and without hierarchical structures.Our results showed that after 2000 CV cycles or five weeks of aging at 60℃,the morphology and electrochemical properties of the coatings on the Pt-Ir substrates with hierarchical structures remained relatively stable.In contrast,we found that smooth Pt-Ir substrate surfaces caused delamination of the PEDOT:PSS coating and exhibited both decreased charge storage capacity and increased impedance.Overall,enhancing the stability of PEDOT:PSS coatings used on common platinum-iridium neural electrodes offers great potential for improving their electrochemical performance and developing new functionalities.

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.

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.

A Hierarchical Modeling Framework for Electrochemical Behaviors in Lithium-Ion Batteries with Detailed Structures

The accurate representation of lithium plating and aging phenomena has posed a persistent challenge within the battery research community.Empirical evidence underscores the pivotal role of cell structure in influencing aging behaviors and lithium plating within lithium-ion batteries(LIBs).Available lithium-ion plating models often falter in detailed description when integrating the structural intricacies.To address this challenge,this study proposes an innovative hierarchical model that intricately incorporates the layered rolling structure in cells.Notably,our model demonstrates a remarkable capacity to predict the non-uniform distribution of current density and overpotential along the rolling direction of LIBs.Subsequently,we delve into an insightful exploration of the structural factors that influence lithium plating behavior,leveraging the foundation laid by our established model.Furthermore,we easily update the hierarchical model by considering aging factors.This aging model effectively anticipates capacity fatigue and lithium plating tendencies across individual layers of LIBs,all while maintaining computational efficiency.In light of our findings,this model yields novel perspectives on capacity fatigue dynamics and local lithium plating behaviors,offering a substantial advancement compared to existing models.This research paves the way for more efficient and tailored LIB design and operation,with broad implications for energy storage technologies.

Defect-induced synthesis of nanoscale hierarchically porous metal-organic frameworks with tunable porosity for enhanced volatile organic compound adsorption

Nanoscale hierarchically porous metal-organic frameworks(NH-MOFs)synergistically combine the advantages of nanoscale MOFs and hierarchically porous MOFs,resulting in remarkable characteristics such as increased specific surface area,greater porosity,and enhanced exposure of active sites.Herein,nanoscale hierarchically porous UIO-66(UIO-66_X)was synthesized using a defect-induced strategy that employed ethylene diamine tetraacetic acid(EDTA)as a modulator.The introduced EDTA occupies the coordination sites of organic ligands,promoting the formation and growth of UIO-66 crystal nuclei and inducing defects during synthesis.The as-synthesized UIO-66_X crystals exhibit a uniform distribution with an average size of approximately 100 nm.In addition,the total pore volume attains a remarkable value of 0.95 cm^(3)g^(-1),with mesopores constituting 36.8% of the structure.Furthermore,the porosities of UIO-66_X can be easily tuned by controlling the molar ratio of EDTA/Zr^(4+).In addition,the as-synthesized UIO-66_X exhibits excellent adsorption capacities for n-hexane(344 mg g^(-1))and pxylene(218 mg g^(-1)),which are 44.5% and 27.5% higher than those of conventional UIO-66,respectively.Finally,the adsorption behavior of n-hexane and p-xylene molecules in UIO-66_X was investigated using density functional theory simulations.

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.