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
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
Phase singularities(PSs)in topological darkness-based sensors have received significant attention in optical sensing due to their rapid,ultra-sensitive,and label-free detection capabilities.Here,we present both experi...Phase singularities(PSs)in topological darkness-based sensors have received significant attention in optical sensing due to their rapid,ultra-sensitive,and label-free detection capabilities.Here,we present both experimental and theoretical investigations of an ultrasensitive and multiplexed phase-sensitive sensor utilizing dual topological PSs in the visible and near-infrared regions.This sensor uses a simple structure,which consists of an ultra-thin highly absorbing film deposited on a metal substrate.We demonstrate the achievement of dual-polarization darkness points for s-and p-polarizations at different incident angles.Furthermore,we theoretically explain the double topological PSs accompanied by a perfect±π-jump near a zero-reflection point,based on the temporal coupled-mode formalism.To validate its multifunctional capabilities,humidity sensing tests were carried out.The results demonstrate that the sensor has a detection limit reaching the level of 0.12‰.These findings go beyond the scope of conventional interference optical coatings and highlight the potential applications of this technology in gas sensing and biosensing domains.展开更多
While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used imag...While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.展开更多
Traditional resistive semiconductor gas sensors suffer from high operating temperatures and poor selectivity.Thus,to address these issues,a highly selective nitrogen dioxide(NO_(2))sensor based on lead sulfide(PbS)qua...Traditional resistive semiconductor gas sensors suffer from high operating temperatures and poor selectivity.Thus,to address these issues,a highly selective nitrogen dioxide(NO_(2))sensor based on lead sulfide(PbS)quantum dots(QDs)–lead molybdate(PbMoO_(4))–molybdenum disulfide(MoS_(2))ternary nanocomposites operating at room temperature was fabricated herein.The ternary nanocomposites were synthesized using an in situ method,yielding Pb S QDs with an average size of~10 nm and PbMoO_(4)nanoparticles in the 10-to 20-nm range,uniformly distributed on ultrathin MoS_(2)nanosheets with an average thickness of~7 nm.The optimized sensor demonstrated a significant improvement in response to 1 ppm NO_(2)at 25℃,achieving a response of 44.5%,which was approximately five times higher than that of the pure MoS_(2)-based sensor(8.5%).The sensor also achieved relatively short response/recovery times and full recovery properties.Notably,the optimal sensor displayed extraordinary selectivity toward NO_(2),showing negligible responses to different interfering gases.Density functional theory(DFT)calculations were conducted to elucidate the underlying sensing mechanism,which was attributed to the enhanced specific surface area,the receptor function of both PbS QDs and PbMoO_(4)nanoparticles,and the transducer function of MoS_(2) nanosheets.展开更多
Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric featur...Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.展开更多
Reasonably constructing an atomic interface is pronouncedly essential for surface-related gas-sensing reaction.Herein,we present an ingen-ious feedback-regulation system by changing the interactional mode between sing...Reasonably constructing an atomic interface is pronouncedly essential for surface-related gas-sensing reaction.Herein,we present an ingen-ious feedback-regulation system by changing the interactional mode between single Pt atoms and adjacent S species for high-efficiency SO_(2)sensing.We found that the single Pt sites on the MoS_(2)surface can induce easier volatiliza-tion of adjacent S species to activate the whole inert S plane.Reversely,the activated S species can provide a feedback role in tailoring the antibonding-orbital electronic occupancy state of Pt atoms,thus creating a combined system involving S vacancy-assisted single Pt sites(Pt-Vs)to synergistically improve the adsorption ability of SO_(2)gas molecules.Further-more,in situ Raman,ex situ X-ray photoelectron spectroscopy testing and density functional theory analysis demonstrate the intact feedback-regulation system can expand the electron transfer path from single Pt sites to whole Pt-MoS_(2)supports in SO_(2)gas atmosphere.Equipped with wireless-sensing modules,the final Pt1-MoS_(2)-def sensors array can further realize real-time monitoring of SO_(2)levels and cloud-data storage for plant growth.Such a fundamental understanding of the intrinsic link between atomic interface and sensing mechanism is thus expected to broaden the rational design of highly effective gas sensors.展开更多
Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material...Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control,optimize gas flow distribution and improve ironmaking efficiency.It has been challengeable to obtain high-quality optical burden surface images under high-temperature,high-dust,and extremelydim(less than 0.001 Lux)environment.Based on a novel endoscopic sensing detection idea,a reverse telephoto structure starlight imaging system with large field of view and large aperture is designed.Combined with a water-air dual cooling intelligent self-maintenance protection device and the imaging system,a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment.Based on an endoscope imaging area model,a material flow trajectory model and a gas-dust coupling distribution model,an optimal installation position and posture configuration method for the endoscope is proposed,which maximizes the effective imaging area and ensures large-area,safe and stable imaging of the device in a confined space.Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions,providing key data support for green iron smelting.展开更多
Exhaled ammonia(NH_(3))can be used as a crucial biomarker of kidney and liver diseases.However,the high humidity in the detection conditions remains a challenge for accurate detection by gas sensors.Herein,a copper-ba...Exhaled ammonia(NH_(3))can be used as a crucial biomarker of kidney and liver diseases.However,the high humidity in the detection conditions remains a challenge for accurate detection by gas sensors.Herein,a copper-based metal-organic framework(CH_(3)-Cu-BTC)with methyl(CH_(3)^(-))functionalization of trimesic acid was synthesized for NH_(3) colorimetric sensing.The CH_(3)-Cu-BTC exhibited a strong response for 5 ppm NH_(3) with high selectivity under high relative humidity(75%RH).Density functional theory(DFT)simulations indicated that the NH_(3) molecules interacted more strongly with CH_(3)-Cu-BTC than H_(2)O molecules did,and the corresponding color switching was attributed to the lone-pair electron in NH_(3) changing the coordination environment of Cu^(2+)ions,leading to an obviously visible color switching response from ruby green to blue.Based on the tailor-made pore chemistry,the precise detection of trace amounts of NH_(3) in exhaled air was realized through functionalized MOF materials.The strategy used in this study not only offers a new pathway for the rapid detection of low concentration NH_(3) under humid conditions,but also shows a method for early respiration diagnosis of kidney and liver diseases.展开更多
Single atom catalysts(SACs)have garnered significant attention in the field of catalysis over the past decade due to their exceptional atom utilization efficiency and distinct physical and chemical properties.For the ...Single atom catalysts(SACs)have garnered significant attention in the field of catalysis over the past decade due to their exceptional atom utilization efficiency and distinct physical and chemical properties.For the semiconductor-based electrical gas sensor,the core is the catalysis process of target gas molecules on the sensitive materials.In this context,the SACs offer great potential for highly sensitive and selective gas sensing,however,only some of the bubbles come to the surface.To facilitate practical applications,we present a comprehensive review of the preparation strategies for SACs,with a focus on overcoming the challenges of aggregation and low loading.Extensive research efforts have been devoted to investigating the gas sensing mechanism,exploring sensitive materials,optimizing device structures,and refining signal post-processing techniques.Finally,the challenges and future perspectives on the SACs based gas sensing are presented.展开更多
We for the first time demonstrate ground-based remote sensing of Nitrous Oxide(N_(2)O)over Hefei in eastern China from high resolution Fourier Transform Infra-Red(FTIR)solar spectra.We have retrieved Column-averaged A...We for the first time demonstrate ground-based remote sensing of Nitrous Oxide(N_(2)O)over Hefei in eastern China from high resolution Fourier Transform Infra-Red(FTIR)solar spectra.We have retrieved Column-averaged Abundance of N_(2)O(XN_(2)O)from both Near-Infrared(NIR,4000 to 11,000 cm−1)and Mid-Infrared(MIR,2400 to 3200 cm−1)solar spectra and inspected their agreement.Generally,NIR and MIR measurements agree well with a correlation coefficient of 0.86 and an average difference of(1.33±4.05)ppbv(NIR-MIR).By correcting the bias of the two datasets,we combine the NIR and MIR measurements to investigate seasonality and inter-annual trend of XN_(2)O over Hefei.The observed monthly mean time series of XN_(2)O minimize in June and maximize in September,with values of(316.55±12.22)ppbv and(322.05±12.93)ppbv,respectively.The XN_(2)O time series from 2015 to 2020 showed an inter-annual trend of(0.53±0.10)%/year over Hefei,China.We also compared the FTIR XN_(2)O observations with GEOS-Chem model XN_(2)O simulations.They are in reasonable agreement with a correlation coefficient(R)of 0.71,but GEOS-Chem model underestimated the seasonality of the observations.This study can enhance current knowledge of ground-based high-resolution FTIR remote sensing of N_(2)O in the atmosphere and contribute to generating a new reliable N_(2)O dataset for climate change research.展开更多
A novel tetra-nuclear Tb-organic network,named as [Tb_(4)(2-pyia)_(6)(HAc)_(0.5)(2,2'-bipy)(H_(2)O)_(4.5)]·2,2'-bipy·H_(2)O(1),was synthesized hydrothermally based on 5-(pyridin-2-ylmethoxy) isophtha...A novel tetra-nuclear Tb-organic network,named as [Tb_(4)(2-pyia)_(6)(HAc)_(0.5)(2,2'-bipy)(H_(2)O)_(4.5)]·2,2'-bipy·H_(2)O(1),was synthesized hydrothermally based on 5-(pyridin-2-ylmethoxy) isophthalic acid(H_(2)pyia) and 2,2'-bipyridine(2,2'-bipy) ligands,and characterized by single crystal X-ray diffraction,thermogravimetric(TG) analyses,powder X-ray diffraction(PXRD) and infrared(IR) technology.1 possesses a two-dimensional network based on the tetra-nuclear inorganic building units,and the π-πstacking interactions between the pyia^(2-) ligands and the vip 2,2'-bipy molecules play an important role in the forming of 3D supramolecular structure.1 exhibits excellent fluorescent sensing performance for Fe^(3+)(1.26×10^(-8) mol/L),Cr_(2)O_(7)^(2-)(8.1×10^(-7) mol/L),2,4,6-trinitrophenol(TNP)(2.71×10^(-8) mol/L)and tetracycline(TCT)(2.76×10^(-7) mol/L) in aqueous solution with lower detection concentrations.The sensing mechanisms of 1 were investigated by density functional theory(DFT) calculations,ultraviolet-visible(UV-Vis) diffuse reflectance spectroscopy,PXRD and fluorescent lifetime analyses.The activated product of 1 was prepared by heating at 255℃ under constant pressure and used to photo-catalytically degrade TCT.Both 1 and the activated one have good photocatalytic degradation performance for TCT with degradation rates of 84.29% and 96.07%,respectively.The photocatalytic mechanisms were discovered by UV-Vis diffuse reflectance spectroscopy and radical trap experiments.The Tb-organic framework might be an excellent multifunctional fluorescent sensor and a good photocatalytic agent for TCT degradation in the future.展开更多
When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in inco...When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.展开更多
Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose ...Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.展开更多
A high-sensitivity magnetic sensing system based on giant magneto-impedance(GMI)effect is designed and fabricated.The system comprises a GMI sensor equipped with a gradient probe and an signal acquisition and processi...A high-sensitivity magnetic sensing system based on giant magneto-impedance(GMI)effect is designed and fabricated.The system comprises a GMI sensor equipped with a gradient probe and an signal acquisition and processing module.A segmented superposition algorithm is used to increase target signal and reduce the random noise.The results show that under unshielded,room temperature conditions,the system achieves successful detection of weak magnetic fields down to 2 pT with a notable sensitivity of 1.84×10^(8)V/T(G=1000).By applying 17 overlays,the segmented superposition algorithm increases the power proportion of the target signal at 31 Hz from6.89%to 45.91%,surpassing the power proportion of the 2 Hz low-frequency interference signal.Simultaneously,it reduces the power proportion of the 20 Hz random noise.The segmented superposition process effectively cancels out certain random noise elements,leading to a reduction in their respective power proportions.This high-sensitivity magnetic sensing system features a simple structure,and is easy to operate,making it highly valuable for both practical applications and broader dissemination.展开更多
We explore an end-to-end wavefront sensing approach based on deep learning,which aims to deal with the high-order turbulence and the discontinuous aberration caused by optical system obstructions commonly encountered ...We explore an end-to-end wavefront sensing approach based on deep learning,which aims to deal with the high-order turbulence and the discontinuous aberration caused by optical system obstructions commonly encountered in real-world ground-based telescope observations.We have considered factors such as the entrance pupil wavefront containing high-order turbulence and discontinuous aberrations due to obstruction by the secondary mirror and spider,realistically simulating the observation conditions of ground-based telescopes.By comparing with the Marechal criterion(0.075λ),we validate the effectiveness of the proposed approach.Experimental results show that the deep learning wavefront sensing approach can correct the distorted wavefront affect by high-order turbulence to close to the diffraction limit.We also analyze the limitations of this approach,using the direct zonal phase output method,where the residual wavefront stems from the fitting error.Furthermore,we have explored the wavefront reconstruction accuracy of different noise intensities and the central obstruction ratios.Within a noise intensity range of 1%–1.9%,the root mean square error(RMSE)of the residual wavefront is less than Marechal criterion.In the range of central obstruction ratios from 0.0 to 0.3 commonly used in ground-based telescopes,the RMSE of the residual wavefront is greater than 0.039λand less than 0.041λ.This research provides an efficient and valid wavefront sensing approach for high-resolution observation with ground-based telescopes.展开更多
Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous human...Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.展开更多
Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrat...Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.展开更多
Water erosion is a serious problem that leads to soil degradation,loss,and the destruction of structures.Assessing the risk of erosion and determining the affected areas has become crucial in order to understand the m...Water erosion is a serious problem that leads to soil degradation,loss,and the destruction of structures.Assessing the risk of erosion and determining the affected areas has become crucial in order to understand the main factors influencing its evolution and to minimize its impacts.This study focuses on evaluating the risk of erosion in the Assif el mal watershed,which is located in the High Atlas Mountains.The Erosion Potential Model(EPM)is used to estimate soil losses depending on various parameters such as lithology,hydrology,topography,and morphometry.Geographic information systems and remote sensing techniques are employed to map areas with high erosive potential and their relationship with the distribution of factors involved.Different digital elevation models are also used in this study to highlight the impact of data quality on the accuracy of the results.The findings reveal that approximately 59%of the total area in the Assif el mal basin has low to very low potential for soil losses,while 22%is moderately affected and 19.9%is at high to very high risk.It is therefore crucial to implement soil conservation measures to mitigate and prevent erosion risks.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
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.
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.
基金supported by the National Key R&D Program of China(2022YFA1404701)Program of Shanghai Academic Research Leader under Grant(22XD1422100)+4 种基金National Natural Science Foundation of China(62075231,12141303,12073018)Shanghai Science and Technology Committee(20JC1414603,23dz2260100)Shanghai Pujiang Program(21PJ1411400)China Postdoctoral Science Foundation(2021M703335)Young Elite Scientists Sponsorship Program by CAST(YESS20220355).
文摘Phase singularities(PSs)in topological darkness-based sensors have received significant attention in optical sensing due to their rapid,ultra-sensitive,and label-free detection capabilities.Here,we present both experimental and theoretical investigations of an ultrasensitive and multiplexed phase-sensitive sensor utilizing dual topological PSs in the visible and near-infrared regions.This sensor uses a simple structure,which consists of an ultra-thin highly absorbing film deposited on a metal substrate.We demonstrate the achievement of dual-polarization darkness points for s-and p-polarizations at different incident angles.Furthermore,we theoretically explain the double topological PSs accompanied by a perfect±π-jump near a zero-reflection point,based on the temporal coupled-mode formalism.To validate its multifunctional capabilities,humidity sensing tests were carried out.The results demonstrate that the sensor has a detection limit reaching the level of 0.12‰.These findings go beyond the scope of conventional interference optical coatings and highlight the potential applications of this technology in gas sensing and biosensing domains.
文摘While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.
基金supported by the National Natural Science Foundation of China(No.52274255)Fundamental Research Funds for the Central Universities,China(Nos.N2401003,N2301003,N2201008,N2201004,and N2301025)+3 种基金Liaoning Revitalization Talents Program,China(No.XLYC2202028)Postdoctoral Foundation of Northeastern University,ChinaYoung Elite Scientists Sponsorship Program by CAST(No.2022QNRC001)China Postdoctoral Science Foundation(No.2022M720025)。
文摘Traditional resistive semiconductor gas sensors suffer from high operating temperatures and poor selectivity.Thus,to address these issues,a highly selective nitrogen dioxide(NO_(2))sensor based on lead sulfide(PbS)quantum dots(QDs)–lead molybdate(PbMoO_(4))–molybdenum disulfide(MoS_(2))ternary nanocomposites operating at room temperature was fabricated herein.The ternary nanocomposites were synthesized using an in situ method,yielding Pb S QDs with an average size of~10 nm and PbMoO_(4)nanoparticles in the 10-to 20-nm range,uniformly distributed on ultrathin MoS_(2)nanosheets with an average thickness of~7 nm.The optimized sensor demonstrated a significant improvement in response to 1 ppm NO_(2)at 25℃,achieving a response of 44.5%,which was approximately five times higher than that of the pure MoS_(2)-based sensor(8.5%).The sensor also achieved relatively short response/recovery times and full recovery properties.Notably,the optimal sensor displayed extraordinary selectivity toward NO_(2),showing negligible responses to different interfering gases.Density functional theory(DFT)calculations were conducted to elucidate the underlying sensing mechanism,which was attributed to the enhanced specific surface area,the receptor function of both PbS QDs and PbMoO_(4)nanoparticles,and the transducer function of MoS_(2) nanosheets.
基金financially supported by the Youth Innovation Promotion Association CAS(No.2021325)the National Natural Science Foundation of China(Nos.52179117,U21A20159)the Research project of Panzhihua Iron and Steel Group Mining Co.,Ltd.(No.2021-P6-D2-05)。
文摘Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.
基金This work was supported by the National Natural Science Foundation of China(62271299)Shanghai Sailing Program(22YF1413400).Shanghai Engineering Research Center for We thank the Integrated Circuits and Advanced Display Materials.
文摘Reasonably constructing an atomic interface is pronouncedly essential for surface-related gas-sensing reaction.Herein,we present an ingen-ious feedback-regulation system by changing the interactional mode between single Pt atoms and adjacent S species for high-efficiency SO_(2)sensing.We found that the single Pt sites on the MoS_(2)surface can induce easier volatiliza-tion of adjacent S species to activate the whole inert S plane.Reversely,the activated S species can provide a feedback role in tailoring the antibonding-orbital electronic occupancy state of Pt atoms,thus creating a combined system involving S vacancy-assisted single Pt sites(Pt-Vs)to synergistically improve the adsorption ability of SO_(2)gas molecules.Further-more,in situ Raman,ex situ X-ray photoelectron spectroscopy testing and density functional theory analysis demonstrate the intact feedback-regulation system can expand the electron transfer path from single Pt sites to whole Pt-MoS_(2)supports in SO_(2)gas atmosphere.Equipped with wireless-sensing modules,the final Pt1-MoS_(2)-def sensors array can further realize real-time monitoring of SO_(2)levels and cloud-data storage for plant growth.Such a fundamental understanding of the intrinsic link between atomic interface and sensing mechanism is thus expected to broaden the rational design of highly effective gas sensors.
基金the National Natural Science Foundation of China(62273359)the General Project of Hunan Natural Science Foundation of China(2022JJ30748)the National Major Scientific Research Equipment of China(61927803)。
文摘Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control,optimize gas flow distribution and improve ironmaking efficiency.It has been challengeable to obtain high-quality optical burden surface images under high-temperature,high-dust,and extremelydim(less than 0.001 Lux)environment.Based on a novel endoscopic sensing detection idea,a reverse telephoto structure starlight imaging system with large field of view and large aperture is designed.Combined with a water-air dual cooling intelligent self-maintenance protection device and the imaging system,a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment.Based on an endoscope imaging area model,a material flow trajectory model and a gas-dust coupling distribution model,an optimal installation position and posture configuration method for the endoscope is proposed,which maximizes the effective imaging area and ensures large-area,safe and stable imaging of the device in a confined space.Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions,providing key data support for green iron smelting.
基金the financial support from the National Natural Science Foundation of China(Nos.22090062,22278287,22278288)the Shanxi Province 136 Revitalization Medical Project(General Surgery Department)+1 种基金the Shanxi Provincial Guiding Science and Technology Special Project(No.2021XM42)the Basic Research Program of Shanxi Province(No.202103021224341)。
文摘Exhaled ammonia(NH_(3))can be used as a crucial biomarker of kidney and liver diseases.However,the high humidity in the detection conditions remains a challenge for accurate detection by gas sensors.Herein,a copper-based metal-organic framework(CH_(3)-Cu-BTC)with methyl(CH_(3)^(-))functionalization of trimesic acid was synthesized for NH_(3) colorimetric sensing.The CH_(3)-Cu-BTC exhibited a strong response for 5 ppm NH_(3) with high selectivity under high relative humidity(75%RH).Density functional theory(DFT)simulations indicated that the NH_(3) molecules interacted more strongly with CH_(3)-Cu-BTC than H_(2)O molecules did,and the corresponding color switching was attributed to the lone-pair electron in NH_(3) changing the coordination environment of Cu^(2+)ions,leading to an obviously visible color switching response from ruby green to blue.Based on the tailor-made pore chemistry,the precise detection of trace amounts of NH_(3) in exhaled air was realized through functionalized MOF materials.The strategy used in this study not only offers a new pathway for the rapid detection of low concentration NH_(3) under humid conditions,but also shows a method for early respiration diagnosis of kidney and liver diseases.
基金supported by the National Key Research and Development Program of China(2022YFB3204700)the National Natural Science Foundation of China(52122513)+2 种基金the Natural Science Foundation of Heilongjiang Province(YQ2021E022)the Natural Science Foundation of Chongqing(2023NSCQ-MSX2286)the Fundamental Research Funds for the Central Universities(HIT.BRET.2021010)。
文摘Single atom catalysts(SACs)have garnered significant attention in the field of catalysis over the past decade due to their exceptional atom utilization efficiency and distinct physical and chemical properties.For the semiconductor-based electrical gas sensor,the core is the catalysis process of target gas molecules on the sensitive materials.In this context,the SACs offer great potential for highly sensitive and selective gas sensing,however,only some of the bubbles come to the surface.To facilitate practical applications,we present a comprehensive review of the preparation strategies for SACs,with a focus on overcoming the challenges of aggregation and low loading.Extensive research efforts have been devoted to investigating the gas sensing mechanism,exploring sensitive materials,optimizing device structures,and refining signal post-processing techniques.Finally,the challenges and future perspectives on the SACs based gas sensing are presented.
基金supported by the Youth Innovation Promotion Association,CAS[No.2019434]National Natural Science Foundation of China[No.U21A2027]+3 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences[No.XDA23020301]the Key Research and Development Project of Anhui Province[No.202104i07020002]the Major Projects of High Resolution Earth Observation Systems of National Science and Technology[No.05-Y30B01-9001-19/20-3]the Sino-German Mobility programme[No.M-0036].
文摘We for the first time demonstrate ground-based remote sensing of Nitrous Oxide(N_(2)O)over Hefei in eastern China from high resolution Fourier Transform Infra-Red(FTIR)solar spectra.We have retrieved Column-averaged Abundance of N_(2)O(XN_(2)O)from both Near-Infrared(NIR,4000 to 11,000 cm−1)and Mid-Infrared(MIR,2400 to 3200 cm−1)solar spectra and inspected their agreement.Generally,NIR and MIR measurements agree well with a correlation coefficient of 0.86 and an average difference of(1.33±4.05)ppbv(NIR-MIR).By correcting the bias of the two datasets,we combine the NIR and MIR measurements to investigate seasonality and inter-annual trend of XN_(2)O over Hefei.The observed monthly mean time series of XN_(2)O minimize in June and maximize in September,with values of(316.55±12.22)ppbv and(322.05±12.93)ppbv,respectively.The XN_(2)O time series from 2015 to 2020 showed an inter-annual trend of(0.53±0.10)%/year over Hefei,China.We also compared the FTIR XN_(2)O observations with GEOS-Chem model XN_(2)O simulations.They are in reasonable agreement with a correlation coefficient(R)of 0.71,but GEOS-Chem model underestimated the seasonality of the observations.This study can enhance current knowledge of ground-based high-resolution FTIR remote sensing of N_(2)O in the atmosphere and contribute to generating a new reliable N_(2)O dataset for climate change research.
基金Project supported by the National Natural Science Foundation of China(22063010)the Youth Innovation Team of Shaanxi Universities。
文摘A novel tetra-nuclear Tb-organic network,named as [Tb_(4)(2-pyia)_(6)(HAc)_(0.5)(2,2'-bipy)(H_(2)O)_(4.5)]·2,2'-bipy·H_(2)O(1),was synthesized hydrothermally based on 5-(pyridin-2-ylmethoxy) isophthalic acid(H_(2)pyia) and 2,2'-bipyridine(2,2'-bipy) ligands,and characterized by single crystal X-ray diffraction,thermogravimetric(TG) analyses,powder X-ray diffraction(PXRD) and infrared(IR) technology.1 possesses a two-dimensional network based on the tetra-nuclear inorganic building units,and the π-πstacking interactions between the pyia^(2-) ligands and the vip 2,2'-bipy molecules play an important role in the forming of 3D supramolecular structure.1 exhibits excellent fluorescent sensing performance for Fe^(3+)(1.26×10^(-8) mol/L),Cr_(2)O_(7)^(2-)(8.1×10^(-7) mol/L),2,4,6-trinitrophenol(TNP)(2.71×10^(-8) mol/L)and tetracycline(TCT)(2.76×10^(-7) mol/L) in aqueous solution with lower detection concentrations.The sensing mechanisms of 1 were investigated by density functional theory(DFT) calculations,ultraviolet-visible(UV-Vis) diffuse reflectance spectroscopy,PXRD and fluorescent lifetime analyses.The activated product of 1 was prepared by heating at 255℃ under constant pressure and used to photo-catalytically degrade TCT.Both 1 and the activated one have good photocatalytic degradation performance for TCT with degradation rates of 84.29% and 96.07%,respectively.The photocatalytic mechanisms were discovered by UV-Vis diffuse reflectance spectroscopy and radical trap experiments.The Tb-organic framework might be an excellent multifunctional fluorescent sensor and a good photocatalytic agent for TCT degradation in the future.
基金This work was supported in part by the Key Project of Natural Science Research of Anhui Provincial Department of Education under Grant KJ2017A416in part by the Fund of National Sensor Network Engineering Technology Research Center(No.NSNC202103).
文摘When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.
基金funded by the Chongqing Normal University Startup Foundation for PhD(22XLB021)supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2023B40).
文摘Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.
基金National Natural Science Foundation of China(No.51977214)。
文摘A high-sensitivity magnetic sensing system based on giant magneto-impedance(GMI)effect is designed and fabricated.The system comprises a GMI sensor equipped with a gradient probe and an signal acquisition and processing module.A segmented superposition algorithm is used to increase target signal and reduce the random noise.The results show that under unshielded,room temperature conditions,the system achieves successful detection of weak magnetic fields down to 2 pT with a notable sensitivity of 1.84×10^(8)V/T(G=1000).By applying 17 overlays,the segmented superposition algorithm increases the power proportion of the target signal at 31 Hz from6.89%to 45.91%,surpassing the power proportion of the 2 Hz low-frequency interference signal.Simultaneously,it reduces the power proportion of the 20 Hz random noise.The segmented superposition process effectively cancels out certain random noise elements,leading to a reduction in their respective power proportions.This high-sensitivity magnetic sensing system features a simple structure,and is easy to operate,making it highly valuable for both practical applications and broader dissemination.
基金supported by the National Natural Science Foundation of China(NSFC)(U2031140).
文摘We explore an end-to-end wavefront sensing approach based on deep learning,which aims to deal with the high-order turbulence and the discontinuous aberration caused by optical system obstructions commonly encountered in real-world ground-based telescope observations.We have considered factors such as the entrance pupil wavefront containing high-order turbulence and discontinuous aberrations due to obstruction by the secondary mirror and spider,realistically simulating the observation conditions of ground-based telescopes.By comparing with the Marechal criterion(0.075λ),we validate the effectiveness of the proposed approach.Experimental results show that the deep learning wavefront sensing approach can correct the distorted wavefront affect by high-order turbulence to close to the diffraction limit.We also analyze the limitations of this approach,using the direct zonal phase output method,where the residual wavefront stems from the fitting error.Furthermore,we have explored the wavefront reconstruction accuracy of different noise intensities and the central obstruction ratios.Within a noise intensity range of 1%–1.9%,the root mean square error(RMSE)of the residual wavefront is less than Marechal criterion.In the range of central obstruction ratios from 0.0 to 0.3 commonly used in ground-based telescopes,the RMSE of the residual wavefront is greater than 0.039λand less than 0.041λ.This research provides an efficient and valid wavefront sensing approach for high-resolution observation with ground-based telescopes.
基金the National Natural Science Foundation of China(42001408,61806097).
文摘Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.
文摘Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.
文摘Water erosion is a serious problem that leads to soil degradation,loss,and the destruction of structures.Assessing the risk of erosion and determining the affected areas has become crucial in order to understand the main factors influencing its evolution and to minimize its impacts.This study focuses on evaluating the risk of erosion in the Assif el mal watershed,which is located in the High Atlas Mountains.The Erosion Potential Model(EPM)is used to estimate soil losses depending on various parameters such as lithology,hydrology,topography,and morphometry.Geographic information systems and remote sensing techniques are employed to map areas with high erosive potential and their relationship with the distribution of factors involved.Different digital elevation models are also used in this study to highlight the impact of data quality on the accuracy of the results.The findings reveal that approximately 59%of the total area in the Assif el mal basin has low to very low potential for soil losses,while 22%is moderately affected and 19.9%is at high to very high risk.It is therefore crucial to implement soil conservation measures to mitigate and prevent erosion risks.
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.
