Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Wearable thermoelectric devices hold significant promise in the realm of self-powered wearable electron-ics,offering applications in energy harvesting,movement tracking,and health monitoring.Nevertheless,developing th...Wearable thermoelectric devices hold significant promise in the realm of self-powered wearable electron-ics,offering applications in energy harvesting,movement tracking,and health monitoring.Nevertheless,developing thermoelectric devices with exceptional flexibility,enduring thermoelectric stability,multi-functional sensing,and comfortable wear remains a challenge.In this work,a stretchable MXene-based thermoelectric fabric is designed to accurately discern temperature and strain stimuli.This is achieved by constructing an adhesive polydopamine(PDA)layer on the nylon fabric surface,which facilitates the subsequent MXene attachment through hydrogen bonding.This fusion results in MXene-based thermo-electric fabric that excels in both temperature sensing and strain sensing.The resultant MXene-based thermoelectric fabric exhibits outstanding temperature detection capability and cyclic stability,while also delivering excellent sensitivity,rapid responsiveness(60 ms),and remarkable durability in strain sens-ing(3200 cycles).Moreover,when affixed to a mask,this MXene-based thermoelectric fabric utilizes the temperature difference between the body and the environment to harness body heat,converting it into electrical energy and accurately discerning the body’s respiratory rate.In addition,the MXene-based ther-moelectric fabric can monitor the state of the body’s joint through its own deformation.Furthermore,it possesses the capability to convert solar energy into heat.These findings indicate that MXene-based ther-moelectric fabric holds great promise for applications in power generation,motion tracking,and health monitoring.展开更多
Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires a...Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body.Herein,we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring.The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device.The hydrogel as core substrate exhibits strong toughness and water retention,and the multimodal sensing of temperature,pressure,and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference.The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability.Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring.Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network.Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved,which provides a promising approach for sleep monitoring.展开更多
A phenylphenothiazine anchored Tb(Ⅲ)-cyclen complex PTP-Cy-Tb for hypochlorite ion(ClO^(-))detection has been designed and prepared.PTP-Cy-Tb shows a weak Tb-based emission with AIE-characteristics in aqueous solutio...A phenylphenothiazine anchored Tb(Ⅲ)-cyclen complex PTP-Cy-Tb for hypochlorite ion(ClO^(-))detection has been designed and prepared.PTP-Cy-Tb shows a weak Tb-based emission with AIE-characteristics in aqueous solutions.After addition of ClO^(-),the fluorescence of PTP-Cy-Tb gives a large enhancement for oxidization the thioether to sulfoxide group.The detection limit of PTP-Cy-Tb toward ClO^(-)is as low as 8.85 nmol/L.The sensing mechanism was detailedly investigated by time of flight mass spectrometer(TOF-MS),Fourier transform infrared spectroscopy(FT-IR)and density functional theory(DFT)calculation.In addition,PTP-Cy-Tb has been successfully used for on-site and real-time detection of ClO^(-)in real water samples by using the smartphone-based visualization method and test strips.展开更多
Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two ...Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two diseases to appear simultaneously in some main production areas.It is necessary to discriminate wheat YR and FHB at the regional scale to accurately locate the disease in space,conduct detailed disease severity monitoring,and scientific control.Four images on different dates were acquired from Sentinel-2,Landsat-8,and Gaofen-1 during the critical period of winter wheat,and 22 remote sensing features that characterize the wheat growth status were then calculated.Meanwhile,6 meteorological parameters that reflect the wheat phenological information were also obtained by combining the site meteorological data and spatial interpolation technology.Then,the principal components(PCs)of comprehensive remote sensing and meteorological features were extracted with principal component analysis(PCA).The PCs-based discrimination models were established to map YR and FHB damage using the random forest(RF)and backpropagation neural network(BPNN).The models’performance was verified based on the disease field truth data(57 plots during the filling period)and 5-fold cross-validation.The results revealed that the PCs obtained after PCA dimensionality reduction outperformed the initial features(IFs)from remote sensing and meteorology in discriminating between the two diseases.Compared to the IFs,the average area under the curve for both micro-average and macro-average ROC curves increased by 0.07 in the PCs-based RF models and increased by 0.16 and 0.13,respectively,in the PCs-based BPNN models.Notably,the PCs-based BPNN discrimination model emerged as the most effective,achieving an overall accuracy of 83.9%.Our proposed discrimination model for wheat YR and FHB,coupled with multi-source remote sensing images and meteorological data,overcomes the limitations of a single-sensor and single-phase remote sensing information in multiple stress discrimination in cloudy and rainy areas.It performs well in revealing the damage spatial distribution of the two diseases at a regional scale,providing a basis for detailed disease severity monitoring,and scientific prevention and control.展开更多
To prepare a conductive polymer actuator with decent performance,a self-built experimental platform for the preparation of polypyrrole film is employed.One of the essential goals is to examine the mechanical character...To prepare a conductive polymer actuator with decent performance,a self-built experimental platform for the preparation of polypyrrole film is employed.One of the essential goals is to examine the mechanical characteristics of the actuator in the presence of various combinations of process parameters,combined with the orthogonal test method of"four factors and three levels".The bending and sensing characteristics of actuators of various sizes are methodically examined using a self-made bending polypyrrole actuator.The functional relationship between the bending displacement and the output voltage signal is established by studying the characteristics of the actuator sensor subjected to various degrees of bending.The experimental results reveal that the bending displacement of the actuator tip almost exhibits a linear variation as a function of length and width.When the voltage reaches 0.8 V,the bending speed of the actuator tends to be stable.Finally,the mechanical properties of the self-assembled polypyrrole actuator are verified by the design and fabrication of the microgripper.展开更多
A novel 3D metal-organic framework(MOF)[Pr_(2)(L)_(3)(H_(2)O)5·H_(2)O]n(Pr-1),(H_(2)L=4,4'-oxybis(benzoic acid))with a rare structure of broken layer net,was constructed under the condition of solvothermal sy...A novel 3D metal-organic framework(MOF)[Pr_(2)(L)_(3)(H_(2)O)5·H_(2)O]n(Pr-1),(H_(2)L=4,4'-oxybis(benzoic acid))with a rare structure of broken layer net,was constructed under the condition of solvothermal synthesis.The struc-ture and crystal net were analyzed and characterized.This rod net of Pr-1 is new to both RCSR and ToposPro data-bases,and is named as rn-12 as suggested.Due to the luminescent properties of H_(2)L and Pr(Ⅲ),the solid-state fluo-rescence property and sensing performance(solvents and metal ions)of Pr-1 were investigated.The sensing experi-ments indicated that Pr-1 could act as a fluorescence sensor to detect Cd^(2+)ions with good sensitivity.In addition,antibacterial activities show that Pr-1 exhibited stronger antibacterial activity against Escherichia coli(E.coli),Staphylococcus aureus(S.aureus),and Bacillus subtilis(B.subtilis)compared to synthetic materials.展开更多
This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based ...This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based on the dictionary D.We establish that matrices adhering to the block D-NSP_(q)condition are both necessary and sufficient for the exact recovery of block sparse signals via l_(2)/l_(q)-synthesis.Additionally,this condition is essential for the stable recovery of signals that are block-compressible with respect to D.This D-NSP_(q)property is identified as the first complete condition for successful signal recovery using l_(2)/l_(q)-synthesis.Furthermore,we assess the theoretical efficacy of the l2/lq-synthesis method under conditions of measurement noise.展开更多
A coordination polymer{[Cd(H_(2)dpa)(bpy)]·3H_(2)O}_(n)(Cd-CP)was designed and hydrothermal synthesized based on 4-(2,4-dicarboxyphenoxy)phthalic acid(H_(4)dpa),2,2'-bipyridine(bpy)and Cd(NO_(3))_(2)·4H_...A coordination polymer{[Cd(H_(2)dpa)(bpy)]·3H_(2)O}_(n)(Cd-CP)was designed and hydrothermal synthesized based on 4-(2,4-dicarboxyphenoxy)phthalic acid(H_(4)dpa),2,2'-bipyridine(bpy)and Cd(NO_(3))_(2)·4H_(2)O.The structure was characterized by single-crystal X-ray diffraction,powder X-ray diffraction,elemental analysis,and infrared spectroscopy.Cd-CP belongs to the monoclinic crystal system with the P2_1/c space group and performs in a 1D double-chain structure.The adjacent double chains further form a 3D supramolecular network structure through hydrogen bonding.Thermogravimetric analysis shows that Cd-CP has good thermal stability.Fluorescence analysis showed that Cd-CP had good choosing selectively and was sensitive to metal ions(Fe^(3+)and Zn^(2+)),2,4,6-trinitrophenylhydrazine(TRI),and pyrimethanil(Pth).Interestingly,when Cd-CP was used for fluorescence detection of metal ions,it was found to have a fluorescence quenching effect on Fe^(3+)but had an obvious enhancement effect on Zn^(2+).Therefore,we designed an“on-off-on”logic gate.In addition,the mechanism of fluorescence sensing has been deeply explored.CCDC:2258625.展开更多
Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investig...Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.展开更多
Stimuli-responsive two-dimensional (2D) covalent organic frameworks (COFs) with precise structures and permanent porosity have been employed as platforms for sensors. The slight change of backbones inside frameworks l...Stimuli-responsive two-dimensional (2D) covalent organic frameworks (COFs) with precise structures and permanent porosity have been employed as platforms for sensors. The slight change of backbones inside frameworks leads to different electronic states by external stimuli, such as solvent, pH, and water. Herein, we introduced an alkynyl-based building block (ETBA) with high planarity to synthesize two imine-based alkynyl-COFs (ETBA-TAPE-COF and ETBA-PYTA-COF) with high yield, good crystallinity, and chemical stability. Due to the presence of acetylene bonds, ETBA-TAPE-COF does not adopt the completely overlapping AA stacking mode. Slight interlayer displacement occurs along the parallel direction relative to the acetylene linkages, which facilitates lower configurational energy. Additionally, the introduction of pyrene group contributes to high π-electron mobility of ETBA-PYTA-COF. The interactions between electron-withdrawing group (ETBA) and electron-donating group (PYTA) during the processes of protonation and intramolecular charge transfer (ICT) endow ETBA-PYTA-COF with excellent acidochromic and solvatochromic properties, respectively. Based on this, a fluorescence sensor is successfully established, which can be used for rapid response to trace amounts of water in organic solvents. In contrast, ETBA-TAPE-COF does not exhibit these photophysical properties due to its higher HOMO–LUMO gap compared to ETBA-PYTA-COF. This work proposes a new strategy for designing and preparing COFs with unique photophysical properties without introducing additional functional groups.展开更多
Ionogels have demonstrated substantial applications in smart wearable systems,soft robotics,and biomedical engineering due to the exceptional ionic conductivity and optical transparency.However,achieving ionogels with...Ionogels have demonstrated substantial applications in smart wearable systems,soft robotics,and biomedical engineering due to the exceptional ionic conductivity and optical transparency.However,achieving ionogels with desirable mechanical properties,environmental stability,and multi-mode sensing remains challenging.Here,we propose a simple strategy for the fabrication of multifunctional silk fabric-based ionogels(BSFIGs).The resulting fabric ionogels exhibits superior mechanical properties,with high tensile strength(11.3 MPa)and work of fracture(2.53 MJ/m^(3)).And its work of fracture still has 1.42 MJ/m^(3)as the notch increased to 50%,indicating its crack growth insensitivity.These ionogels can be used as sensors for strain,temperature,and tactile multimode sensing,demonstrating a gauge factor of 1.19 and a temperature coefficient of resistance of3.17/℃^(-1).Furthermore,these ionogels can be used for the detection of different roughness and as touch screens.The ionogels also exhibit exceptional optical transmittance and environmental stability even at80℃.Our scalable fabrication process broadens the application potential of these multifunctional ionogels in diverse fields,from smart systems to extreme environments.展开更多
Chloroform and other volatile organic pollutants have garnered widespread attention from the public and researchers,because of their potential harm to the respiratory system,nervous system,skin,and eyes.However,resear...Chloroform and other volatile organic pollutants have garnered widespread attention from the public and researchers,because of their potential harm to the respiratory system,nervous system,skin,and eyes.However,research on chloroform vapor sensing is still in its early stages,primarily due to the lack of specific recognition motif.Here we report a mesoporous photonic crystal sensor incorporating carbon dots-based nanoreceptor(HMSS@CDs-PCs)for enhanced chloroform sensing.The colloidal PC packed with hollow mesoporous silica spheres provides an interconnected ordered macro-meso-hierarchical porous structure,ideal for rapid gas sensing utilizing the photonic bandgap shift as the readout signal.The as-synthesized CDs with pyridinic-N-oxide functional groups adsorbed in the hollow mesoporous silica spheres are found to not only serve as the chloroform adsorption sites,but also a molecular glue that prevents crack formation in the colloidal PC.The sensitivity of HMSS@CDs-PCs sensor is 0.79 nm ppm^(-1)and an impressively low limit of detection is 3.22 ppm,which are the best reported values in fast-response chloroform vapor sensor without multi-signal assistance.The positive response time is 7.5 s and the negative response time 9 s.Furthermore,relatively stable sensing can be maintained within a relative humidity of 20%-85%RH and temperature of 25-55℃.This study demonstrates that HMSS@CDs-PCs sensors have practical application potential in indoor and outdoor chloroform vapor detection.展开更多
The rise in gas leakage incidents underscores the urgent need for advanced gas-sensing platforms with ultra-low concentration detection capability.Sensing gate field effect transistor(FET)gas sensors,renowned for the ...The rise in gas leakage incidents underscores the urgent need for advanced gas-sensing platforms with ultra-low concentration detection capability.Sensing gate field effect transistor(FET)gas sensors,renowned for the gas-induced signal amplification without directly exposing the channel to the ambient environment,play a pivotal role in detecting trace-level hazardous gases with high sensitivity and good stability.In this work,carbon nanotubes are employed as the conducting channel,and yttrium oxide(Y_(2)O_(3))is utilized as the gate dielectric layer.Noble metal Pd is incorporated as a sensing gate for hydrogen(H_(2))detection,leveraging its catalytic properties and unique adsorption capability.The fabricated carbon-based FET gas sensor demonstrates a remarkable detection limit of 20×10^(–9) for H_(2) under an air environment,enabling early warning in case of gas leakage.Moreover,the as-prepared sensor exhibited good selectivity,repeatability,and anti-humidity properties.Further experiments elucidate the interaction between H_(2) and sensing electrode under an air/nitrogen environment,providing insights into the underlying oxygen-assisted recoverable sensing mechanism.It is our aspiration for this research to establish a robust experimental foundation for achieving high performance and highly integrated fabrication of trace gas sensors.展开更多
To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measureme...To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measurement has been designed. This system integrates a fluidic detection structure assisted by side-polished fibers(SPFs) with a Sagnac interferometer.展开更多
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
基金supported by the National Natural Science Foundation of China(No.21975107)the China Scholarship Council(No.202206790046).
文摘Wearable thermoelectric devices hold significant promise in the realm of self-powered wearable electron-ics,offering applications in energy harvesting,movement tracking,and health monitoring.Nevertheless,developing thermoelectric devices with exceptional flexibility,enduring thermoelectric stability,multi-functional sensing,and comfortable wear remains a challenge.In this work,a stretchable MXene-based thermoelectric fabric is designed to accurately discern temperature and strain stimuli.This is achieved by constructing an adhesive polydopamine(PDA)layer on the nylon fabric surface,which facilitates the subsequent MXene attachment through hydrogen bonding.This fusion results in MXene-based thermo-electric fabric that excels in both temperature sensing and strain sensing.The resultant MXene-based thermoelectric fabric exhibits outstanding temperature detection capability and cyclic stability,while also delivering excellent sensitivity,rapid responsiveness(60 ms),and remarkable durability in strain sens-ing(3200 cycles).Moreover,when affixed to a mask,this MXene-based thermoelectric fabric utilizes the temperature difference between the body and the environment to harness body heat,converting it into electrical energy and accurately discerning the body’s respiratory rate.In addition,the MXene-based ther-moelectric fabric can monitor the state of the body’s joint through its own deformation.Furthermore,it possesses the capability to convert solar energy into heat.These findings indicate that MXene-based ther-moelectric fabric holds great promise for applications in power generation,motion tracking,and health monitoring.
基金supported by the National Key Research and Development Program of China under Grant(2024YFE0100400)Taishan Scholars Project Special Funds(tsqn202312035)+2 种基金the open research foundation of State Key Laboratory of Integrated Chips and Systems,the Tianjin Science and Technology Plan Project(No.22JCZDJC00630)the Higher Education Institution Science and Technology Research Project of Hebei Province(No.JZX2024024)Jinan City-University Integrated Development Strategy Project under Grant(JNSX2023017).
文摘Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body.Herein,we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring.The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device.The hydrogel as core substrate exhibits strong toughness and water retention,and the multimodal sensing of temperature,pressure,and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference.The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability.Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring.Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network.Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved,which provides a promising approach for sleep monitoring.
基金supported by the National Nature Science Foundation of China(Nos.22061028 and 22361028)Jiangxi Provincial Natural Science Foundation(No.20224ACB203012)。
文摘A phenylphenothiazine anchored Tb(Ⅲ)-cyclen complex PTP-Cy-Tb for hypochlorite ion(ClO^(-))detection has been designed and prepared.PTP-Cy-Tb shows a weak Tb-based emission with AIE-characteristics in aqueous solutions.After addition of ClO^(-),the fluorescence of PTP-Cy-Tb gives a large enhancement for oxidization the thioether to sulfoxide group.The detection limit of PTP-Cy-Tb toward ClO^(-)is as low as 8.85 nmol/L.The sensing mechanism was detailedly investigated by time of flight mass spectrometer(TOF-MS),Fourier transform infrared spectroscopy(FT-IR)and density functional theory(DFT)calculation.In addition,PTP-Cy-Tb has been successfully used for on-site and real-time detection of ClO^(-)in real water samples by using the smartphone-based visualization method and test strips.
基金supported by National Key R&D Program of China(2022YFD2000100)National Natural Science Foundation of China(42401400)Zhejiang Provincial Key Research and Development Program(2023C02018).
文摘Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two diseases to appear simultaneously in some main production areas.It is necessary to discriminate wheat YR and FHB at the regional scale to accurately locate the disease in space,conduct detailed disease severity monitoring,and scientific control.Four images on different dates were acquired from Sentinel-2,Landsat-8,and Gaofen-1 during the critical period of winter wheat,and 22 remote sensing features that characterize the wheat growth status were then calculated.Meanwhile,6 meteorological parameters that reflect the wheat phenological information were also obtained by combining the site meteorological data and spatial interpolation technology.Then,the principal components(PCs)of comprehensive remote sensing and meteorological features were extracted with principal component analysis(PCA).The PCs-based discrimination models were established to map YR and FHB damage using the random forest(RF)and backpropagation neural network(BPNN).The models’performance was verified based on the disease field truth data(57 plots during the filling period)and 5-fold cross-validation.The results revealed that the PCs obtained after PCA dimensionality reduction outperformed the initial features(IFs)from remote sensing and meteorology in discriminating between the two diseases.Compared to the IFs,the average area under the curve for both micro-average and macro-average ROC curves increased by 0.07 in the PCs-based RF models and increased by 0.16 and 0.13,respectively,in the PCs-based BPNN models.Notably,the PCs-based BPNN discrimination model emerged as the most effective,achieving an overall accuracy of 83.9%.Our proposed discrimination model for wheat YR and FHB,coupled with multi-source remote sensing images and meteorological data,overcomes the limitations of a single-sensor and single-phase remote sensing information in multiple stress discrimination in cloudy and rainy areas.It performs well in revealing the damage spatial distribution of the two diseases at a regional scale,providing a basis for detailed disease severity monitoring,and scientific prevention and control.
基金Funded by the National Natural Science Foundation of Hunan Province,Chinal(No.2021JJ60012)。
文摘To prepare a conductive polymer actuator with decent performance,a self-built experimental platform for the preparation of polypyrrole film is employed.One of the essential goals is to examine the mechanical characteristics of the actuator in the presence of various combinations of process parameters,combined with the orthogonal test method of"four factors and three levels".The bending and sensing characteristics of actuators of various sizes are methodically examined using a self-made bending polypyrrole actuator.The functional relationship between the bending displacement and the output voltage signal is established by studying the characteristics of the actuator sensor subjected to various degrees of bending.The experimental results reveal that the bending displacement of the actuator tip almost exhibits a linear variation as a function of length and width.When the voltage reaches 0.8 V,the bending speed of the actuator tends to be stable.Finally,the mechanical properties of the self-assembled polypyrrole actuator are verified by the design and fabrication of the microgripper.
文摘A novel 3D metal-organic framework(MOF)[Pr_(2)(L)_(3)(H_(2)O)5·H_(2)O]n(Pr-1),(H_(2)L=4,4'-oxybis(benzoic acid))with a rare structure of broken layer net,was constructed under the condition of solvothermal synthesis.The struc-ture and crystal net were analyzed and characterized.This rod net of Pr-1 is new to both RCSR and ToposPro data-bases,and is named as rn-12 as suggested.Due to the luminescent properties of H_(2)L and Pr(Ⅲ),the solid-state fluo-rescence property and sensing performance(solvents and metal ions)of Pr-1 were investigated.The sensing experi-ments indicated that Pr-1 could act as a fluorescence sensor to detect Cd^(2+)ions with good sensitivity.In addition,antibacterial activities show that Pr-1 exhibited stronger antibacterial activity against Escherichia coli(E.coli),Staphylococcus aureus(S.aureus),and Bacillus subtilis(B.subtilis)compared to synthetic materials.
基金Supported by The Featured Innovation Projects of the General University of Guangdong Province(2023KTSCX096)The Special Projects in Key Areas of Guangdong Province(ZDZX1088)Research Team Project of Guangdong University of Education(2024KYCXTD018)。
文摘This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based on the dictionary D.We establish that matrices adhering to the block D-NSP_(q)condition are both necessary and sufficient for the exact recovery of block sparse signals via l_(2)/l_(q)-synthesis.Additionally,this condition is essential for the stable recovery of signals that are block-compressible with respect to D.This D-NSP_(q)property is identified as the first complete condition for successful signal recovery using l_(2)/l_(q)-synthesis.Furthermore,we assess the theoretical efficacy of the l2/lq-synthesis method under conditions of measurement noise.
文摘A coordination polymer{[Cd(H_(2)dpa)(bpy)]·3H_(2)O}_(n)(Cd-CP)was designed and hydrothermal synthesized based on 4-(2,4-dicarboxyphenoxy)phthalic acid(H_(4)dpa),2,2'-bipyridine(bpy)and Cd(NO_(3))_(2)·4H_(2)O.The structure was characterized by single-crystal X-ray diffraction,powder X-ray diffraction,elemental analysis,and infrared spectroscopy.Cd-CP belongs to the monoclinic crystal system with the P2_1/c space group and performs in a 1D double-chain structure.The adjacent double chains further form a 3D supramolecular network structure through hydrogen bonding.Thermogravimetric analysis shows that Cd-CP has good thermal stability.Fluorescence analysis showed that Cd-CP had good choosing selectively and was sensitive to metal ions(Fe^(3+)and Zn^(2+)),2,4,6-trinitrophenylhydrazine(TRI),and pyrimethanil(Pth).Interestingly,when Cd-CP was used for fluorescence detection of metal ions,it was found to have a fluorescence quenching effect on Fe^(3+)but had an obvious enhancement effect on Zn^(2+).Therefore,we designed an“on-off-on”logic gate.In addition,the mechanism of fluorescence sensing has been deeply explored.CCDC:2258625.
文摘Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.
基金supported by the National Natural Science Foundation of China(22172055)the Science Fund for Distinguished Young Scholars of Guangdong Province(2023B1515040026)+1 种基金the Key Area Research and Development Program of Guangdong Province(2023B0101200008)the Natural Science Foundation of Guangdong Province(2022A1515011892).
文摘Stimuli-responsive two-dimensional (2D) covalent organic frameworks (COFs) with precise structures and permanent porosity have been employed as platforms for sensors. The slight change of backbones inside frameworks leads to different electronic states by external stimuli, such as solvent, pH, and water. Herein, we introduced an alkynyl-based building block (ETBA) with high planarity to synthesize two imine-based alkynyl-COFs (ETBA-TAPE-COF and ETBA-PYTA-COF) with high yield, good crystallinity, and chemical stability. Due to the presence of acetylene bonds, ETBA-TAPE-COF does not adopt the completely overlapping AA stacking mode. Slight interlayer displacement occurs along the parallel direction relative to the acetylene linkages, which facilitates lower configurational energy. Additionally, the introduction of pyrene group contributes to high π-electron mobility of ETBA-PYTA-COF. The interactions between electron-withdrawing group (ETBA) and electron-donating group (PYTA) during the processes of protonation and intramolecular charge transfer (ICT) endow ETBA-PYTA-COF with excellent acidochromic and solvatochromic properties, respectively. Based on this, a fluorescence sensor is successfully established, which can be used for rapid response to trace amounts of water in organic solvents. In contrast, ETBA-TAPE-COF does not exhibit these photophysical properties due to its higher HOMO–LUMO gap compared to ETBA-PYTA-COF. This work proposes a new strategy for designing and preparing COFs with unique photophysical properties without introducing additional functional groups.
基金supported by the National Natural Science Foundation of China(No.12302192)the Fundamental Research Funds for the Central Universities(No.SWU-KQ22025)+4 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJQN202300222)Natural Science Foundation of Chongqing(No.cstc2021jcyj-msxmX0241)the Fund for Innovative Research Groups of Natural Science Foundation of Hebei Province(No.A2024202045)Key Technologies and Demonstration Application Research Project for Large-scale Lithium-ion Hybrid Energy Storage Equipment(No.HC23118)Major Basic Research Project of Hebei Province Natural Science Foundation(No.A2023202049).
文摘Ionogels have demonstrated substantial applications in smart wearable systems,soft robotics,and biomedical engineering due to the exceptional ionic conductivity and optical transparency.However,achieving ionogels with desirable mechanical properties,environmental stability,and multi-mode sensing remains challenging.Here,we propose a simple strategy for the fabrication of multifunctional silk fabric-based ionogels(BSFIGs).The resulting fabric ionogels exhibits superior mechanical properties,with high tensile strength(11.3 MPa)and work of fracture(2.53 MJ/m^(3)).And its work of fracture still has 1.42 MJ/m^(3)as the notch increased to 50%,indicating its crack growth insensitivity.These ionogels can be used as sensors for strain,temperature,and tactile multimode sensing,demonstrating a gauge factor of 1.19 and a temperature coefficient of resistance of3.17/℃^(-1).Furthermore,these ionogels can be used for the detection of different roughness and as touch screens.The ionogels also exhibit exceptional optical transmittance and environmental stability even at80℃.Our scalable fabrication process broadens the application potential of these multifunctional ionogels in diverse fields,from smart systems to extreme environments.
基金supported by the National Key Research and Development Program of China(No.2022YFB3205500)National Natural Science Foundation of China(Nos.U23A20360 and 62303192)QL wishes to thank Water Research Australia(WRA 1144/21)for funding support.
文摘Chloroform and other volatile organic pollutants have garnered widespread attention from the public and researchers,because of their potential harm to the respiratory system,nervous system,skin,and eyes.However,research on chloroform vapor sensing is still in its early stages,primarily due to the lack of specific recognition motif.Here we report a mesoporous photonic crystal sensor incorporating carbon dots-based nanoreceptor(HMSS@CDs-PCs)for enhanced chloroform sensing.The colloidal PC packed with hollow mesoporous silica spheres provides an interconnected ordered macro-meso-hierarchical porous structure,ideal for rapid gas sensing utilizing the photonic bandgap shift as the readout signal.The as-synthesized CDs with pyridinic-N-oxide functional groups adsorbed in the hollow mesoporous silica spheres are found to not only serve as the chloroform adsorption sites,but also a molecular glue that prevents crack formation in the colloidal PC.The sensitivity of HMSS@CDs-PCs sensor is 0.79 nm ppm^(-1)and an impressively low limit of detection is 3.22 ppm,which are the best reported values in fast-response chloroform vapor sensor without multi-signal assistance.The positive response time is 7.5 s and the negative response time 9 s.Furthermore,relatively stable sensing can be maintained within a relative humidity of 20%-85%RH and temperature of 25-55℃.This study demonstrates that HMSS@CDs-PCs sensors have practical application potential in indoor and outdoor chloroform vapor detection.
基金supported by the National Natural Science Foundation of China(Nos.62071410 and 62101477)Hunan Provincial Natural Science Foundation(No.2021JJ40542).
文摘The rise in gas leakage incidents underscores the urgent need for advanced gas-sensing platforms with ultra-low concentration detection capability.Sensing gate field effect transistor(FET)gas sensors,renowned for the gas-induced signal amplification without directly exposing the channel to the ambient environment,play a pivotal role in detecting trace-level hazardous gases with high sensitivity and good stability.In this work,carbon nanotubes are employed as the conducting channel,and yttrium oxide(Y_(2)O_(3))is utilized as the gate dielectric layer.Noble metal Pd is incorporated as a sensing gate for hydrogen(H_(2))detection,leveraging its catalytic properties and unique adsorption capability.The fabricated carbon-based FET gas sensor demonstrates a remarkable detection limit of 20×10^(–9) for H_(2) under an air environment,enabling early warning in case of gas leakage.Moreover,the as-prepared sensor exhibited good selectivity,repeatability,and anti-humidity properties.Further experiments elucidate the interaction between H_(2) and sensing electrode under an air/nitrogen environment,providing insights into the underlying oxygen-assisted recoverable sensing mechanism.It is our aspiration for this research to establish a robust experimental foundation for achieving high performance and highly integrated fabrication of trace gas sensors.
基金supported by the National Natural Science Foundation of China(Nos.61705027,62375031 and 52075131)the Chongqing Science and Technology Commission Basic Research Project(No.CSTC-2020jcyj-msxm0603)the Chongqing Municipal Education Commission Science and Technology Research Program(No.KJQN202000609)。
文摘To address the temperature cross-talk issue in detecting heavy metal ions in natural waters, a highly-integrated and fully fiber-optic metal ion sensing system capable of temperature-concentration decoupling measurement has been designed. This system integrates a fluidic detection structure assisted by side-polished fibers(SPFs) with a Sagnac interferometer.
