Short-Term Earthquake Forecast with the Seismic Sequence Hierarchization Method

All strong earthquakes are preceded by branching structures having different durations whose development scheme is partly largely predictable because it follows a well organized and recognizable pattern. By using a seismic sequence hierarchization method, this study graphically explains the preparation process of an earthquake, called “branching structure”. In addition, criteria apt to distinguish the structures that will produce shocks of average magnitude from strong earthquakes’ will be defined. Based on the temporal oscillations of the magnitude values, we explain the procedure for identifying the developmental stages that characterize the energy accumulation stage of the branching structure, in order to early detect the energy release stage’s trigger point and obtain information on how it will develop over time. The study identifies also some pre-signals (trigger points) of various magnitudes in the energy release stage, which allows us to early predict the foreshocks and mainshock time position. The method we developed constitutes a truly innovative approach for the earthquake forecasting analysis, which dramatically differs from those developed so far, as it considers the structure of the seismic sequence not only as a magnitude values’ oscillation, but also as a sequence of developmental stages that may begin much earlier.

Absorption properties and mechanism of lightweight and broadband electromagnetic wave-absorbing porous carbon by the swelling treatment

Bioderived carbon materials have garnered considerable interest in the fields of microwave absorption and shielding due to their reproducibility and environmental friendliness.In this study,KOH was evenly distributed on biomass Tremella using the swelling induction method,leading to the preparation of a three-dimensional network-structured hierarchical porous carbon(HPC)through carbonization.The achieved microwave absorption intensity is robust at-47.34 dB with a thin thickness of 2.1 mm.Notably,the widest effective absorption bandwidth,reaching 7.0 GHz(11–18 GHz),is attained at a matching thickness of 2.2 mm.The exceptional broadband and reflection loss performance are attributed to the 3D porous networks,interface effects,carbon network defects,and dipole relaxation.HPC has outstanding absorption characteristics due to its excellent impedance matching and high attenuation constant.The uniform pore structures considerably optimize the impedance-matching performance of the material,while the abundance of interfaces and defects enhances the dielectric loss,thereby improving the attenuation constant.Furthermore,the impact of carbonization temperature and swelling rate on microwave absorption performance was systematically investigated.This research presents a strategy for preparing absorbing materials using biomass-derived HPC,showcasing considerable potential in the field of electromagnetic wave absorption.

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.

Carbon nanocages bridged with graphene enable fast kinetics for dual-carbon lithium-ion capacitors

Lithium-ion capacitors(LICs) combining the advantages of lithium-ion batteries and supercapacitors are considered a promising nextgeneration energy storage device. However, the sluggish kinetics of battery-type anode cannot match the capacitor-type cathode, restricting the development of LICs. Herein, hierarchical carbon framework(HCF) anode material composed of 0D carbon nanocage bridged with 2D graphene network are developed via a template-confined synthesis process. The HCF with nanocage structure reduces the Li^(+) transport path and benefits the rapid Li^(+) migration, while 2D graphene network can promote the electron interconnecting of carbon nanocages. In addition, the doped N atoms in HCF facilitate to the adsorption of ions and enhance the pseudo contribution, thus accelerate the kinetics of the anode. The HCF anode delivers high specific capacity, remarkable rate capability. The LIC pouch-cell based on HCF anode and active HCF(a-HCF) cathode can provide a high energy density of 162 Wh kg^(-1) and a superior power density of 15.8 kW kg^(-1), as well as a long cycling life exceeding 15,000cycles. This study demonstrates that the well-defined design of hierarchical carbon framework by incorporating 0D carbon nanocages and 2D graphene network is an effective strategy to promote LIC anode kinetics and hence boost the LIC electrochemical performance.

Composition Engineering Opens an Avenue Toward Efficient and Sustainable Nitrogen Fixation

In this work,we open an avenue toward rational design of potential efficient catalysts for sustainable ammonia synthesis through composition engineering strategy by exploiting the synergistic effects among the active sites as exemplified by diatomic metals anchored graphdiyne via the combination of hierarchical high-throughput screening,first-principles calculations,and molecular dynamics simulations.Totally 43 highly efficient catalysts feature ultralow onset potentials(|U_(onset)|≤0.40 V)with Rh-Hf and Rh-Ta showing negligible onset potentials of 0 and-0.04 V,respectively.Extremely high catalytic activities of Rh-Hf and Rh-Ta can be ascribed to the synergistic effects.When forming heteronuclears,the combinations of relatively weak(such as Rh)and relatively strong(such as Hf or Ta)components usually lead to the optimal strengths of adsorption Gibbs free energies of reaction intermediates.The origin can be ascribed to the mediate d-band centers of Rh-Hf and Rh-Ta,which lead to the optimal adsorption strengths of intermediates,thereby bringing the high catalytic activities.Our work provides a new and general strategy toward the architecture of highly efficient catalysts not only for electrocatalytic nitrogen reduction reaction(eNRR)but also for other important reactions.We expect that our work will boost both experimental and theoretical efforts in this direction.

Enhancing Low-Frequency Microwave Absorption Through Structural Polarization Modulation of MXenes

Two-dimensional carbon-based materials have shown promising electromagnetic wave absorption capabilities in mid-and high-frequency ranges,but face challenges in low-frequency absorption due to limited control over polarization response mecha-nisms and ambiguous resonance behavior.In this study,we pro-pose a novel approach to enhance absorption efficiency in aligned three-dimensional(3D)MXene/CNF(cellulose nanofibers)cavities by modifying polarization properties and manipulating resonance response in the 3D MXene architecture.This controlled polarization mechanism results in a significant shift of the main absorption region from the X-band to the S-band,leading to a remarkable reflection loss value of-47.9 dB in the low-frequency range.Furthermore,our findings revealed the importance of the oriented electromagnetic coupling in influencing electromagnetic response and microwave absorption properties.The present study inspired us to develop a generic strategy for low-frequency tuned absorption in the absence of magnetic element participation,while orientation-induced polarization and the derived magnetic resonance coupling are the key controlling factors of the method.

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.

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.

A Comprehensive Survey of Recent Transformers in Image,Video and Diffusion Models

Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism.This study aims to provide a comprehensive survey of recent transformerbased approaches in image and video applications,as well as diffusion models.We begin by discussing existing surveys of vision transformers and comparing them to this work.Then,we review the main components of a vanilla transformer network,including the self-attention mechanism,feed-forward network,position encoding,etc.In the main part of this survey,we review recent transformer-based models in three categories:Transformer for downstream tasks,Vision Transformer for Generation,and Vision Transformer for Segmentation.We also provide a comprehensive overview of recent transformer models for video tasks and diffusion models.We compare the performance of various hierarchical transformer networks for multiple tasks on popular benchmark datasets.Finally,we explore some future research directions to further improve the field.

Studies of an event-building algorithm of the readout system for the twin TPCs in HFRS

The High-energy Fragment Separator(HFRS),which is currently under construction,is a leading international radioactive beam device.Multiple sets of position-sensitive twin time projection chamber(TPC)detectors are distributed on HFRS for particle identification and beam monitoring.The twin TPCs'readout electronics system operates in a trigger-less mode due to its high counting rate,leading to a challenge of handling large amounts of data.To address this problem,we introduced an event-building algorithm.This algorithm employs a hierarchical processing strategy to compress data during transmission and aggregation.In addition,it reconstructs twin TPCs'events online and stores only the reconstructed particle information,which significantly reduces the burden on data transmission and storage resources.Simulation studies demonstrated that the algorithm accurately matches twin TPCs'events and reduces more than 98%of the data volume at a counting rate of 500 kHz/channel.

Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets

Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution.

Trifunctional robust electrocatalysts based on 3D Fe/N-doped carbon nanocubes encapsulating Co4N nanoparticles for efficient battery-powered water electrolyzers

Herein,we have designed a highly active and robust trifunctional electrocatalyst derived from Prussian blue analogs,where Co_(4)N nanoparticles are encapsulated by Fe embedded in N-doped carbon nanocubes to synthesize hierarchically structured Co_(4)N@Fe/N-C for rechargeable zinc-air batteries and overall water-splitting electrolyzers.As confirmed by theoretical and experimental results,the high intrinsic oxygen reduction reaction,oxygen evolution reaction,and hydrogen evolution reaction activities of Co_(4)N@Fe/N-C were attributed to the formation of the heterointerface and the modulated local electronic structure.Moreover,Co_(4)N@Fe/N-C induced improvement in these trifunctional electrocatalytic activities owing to the hierarchical hollow nanocube structure,uniform distribution of Co_(4)N,and conductive encapsulation by Fe/N-C.Thus,the rechargeable zinc-air battery with Co_(4)N@Fe/N-C delivers a high specific capacity of 789.9 mAh g^(-1) and stable voltage profiles over 500 cycles.Furthermore,the overall water electrolyzer with Co_(4)N@Fe/N-C achieved better durability and rate performance than that with the Pt/C and IrO2 catalysts,delivering a high Faradaic efficiency of 96.4%.Along with the great potential of the integrated water electrolyzer powered by a zinc-air battery for practical applications,therefore,the mechanistic understanding and active site identification provide valuable insights into the rational design of advanced multifunctional electrocatalysts for energy storage and conversion.

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.

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.

Synchronous organic-inorganic co-intercalated ammonium vanadate cathode for advanced aqueous zinc-ion batteries

Vanadium-based cathode materials are attractive for aqueous zinc-ion batteries(AZIBs)owing to the high capacity from their open frameworks and multiple valences.However,the cycle stability and rate capability are still restricted by the low electrical conductivity and trapped diffusion kinetics.Here,we propose an organic-inorganic co-intercalation strategy to regulate the structure of ammonium vanadate(NH_(4)V_(4)O_(10),NVO).The introduction of Al^(3+)and polyaniline(PANI)induces the optimized layered structure and generation of urchin-like hierarchical construction(AP-NVO),based on heterogeneous nucleation and dissolution-recrystallization growth mechanism.Owing to these favorable features,the AP-NVO electrode delivers a desirable discharge capacity of 386 mA h g^(-1) at 1.0 A g^(-1),high-rate capability of 263 mA h g^(-1 )at 5.0 A g^(-1) and excellent cycling stability with 80.4%capacity retention over 2000 cycles at 5.0 A g^(-1).Such satisfactory electrochemical performance is believed to result from the enhanced reaction kinetics provided by the stable layered structure and a high intercalation pseudo-capacitance reaction.These results could provide enlightening insights into the design of layered vanadium oxide cathodematerials.

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.

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.

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.

Nano-Au-decorated hierarchical porous cobalt sulfide derived from ZIF-67 toward optimized oxygen evolution catalysis:Important roles of microstructures and electronic modulation

Enhancing both the number of active sites available and the intrinsic activity of Co-based electrocatalysts simultaneously is a desirable goal.Herein,a ZIF-67-derived hierarchical porous cobalt sulfide decorated by Au nanoparticles(NPs)(denoted as HP-Au@CoxSy@ZIF-67)hybrid is synthesized by low-temperature sulfuration treatment.The well-defined macroporous-mesoporous-microporous structure is obtained based on the combination of polystyrene spheres,as-formed CoxSy nanosheets,and ZIF-67 frameworks.This novel three-dimensional hierarchical structure significantly enlarges the three-phase interfaces,accelerating the mass transfer and exposing the active centers for oxygen evolution reaction.The electronic structure of Co is modulated by Au through charge transfer,and a series of experiments,together with theoretical analysis,is performed to ascertain the electronic modulation of Co by Au.Meanwhile,HP-Au@CoxSy@ZIF-67 catalysts with different amounts of Au were synthesized,wherein Au and NaBH4 reductant result in an interesting“competition effect”to regulate the relative ratio of Co^(2+)/Co^(3+),and moderate Au assists the electrochemical performance to reach the highest value.Consequently,the optimized HP-Au@CoxSy@ZIF-67 exhibits a low overpotential of 340 mV at 10 mA cm^(-2)and a Tafel slope of 42 mV dec-1 for OER in 0.1 M aqueous KOH,enabling efficient water splitting and Zn-air battery performance.The work here highlights the pivotal roles of both microstructural and electronic modulation in enhancing electrocatalytic activity and presents a feasible strategy for designing and optimizing advanced electrocatalysts.

用于高风险环境安全监测和可调热管理的具有分层核壳结构的耐化学品纱线

Chemical resistant textiles are vital for safeguarding humans against chemical hazards in various settings.such as industrialproduction,chemicalaccidents,laboratory activities,and road transportation.However,the ideal integration of chemical resistance,thermal and moisture management,and wearer condition monitoring in conventional chemically protective textiles remains challenging.Herein,the design,manufacturing,and use of stretchable hierarchical core-shell yarns(HCSYs)for integrated chemical resistance,moisture regulation,and smart sensing textiles are demonstrated.These yarns con-tain helically elastic spandex,wrapped silver-plated nylon,and surface-structuredpolytetrafluo-roethylene(PTFE)yarns and are designed and manufactured based on a scalable fabrication process.In addition to their ideal chemical resistance performance,HCSYs can function as multifunctional stretch-able electronics for real-time human motion monitoring and as the basic element of intelligent textiles.Furthermore,a desirable dynamic thermoregulation function is achieved by exploiting the fabric structure with stretching modulation.Our HCSYs may provide prospective opportunities for the future development of smart protective textiles with high durability,flexibility,and scalability.