Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management...Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management.Existing outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex structures.Herein,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by it.It can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous operation.Moreover,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption methods.Its potential extends to acting as other self-powered environmental sensors.This multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.展开更多
Triboelectric energy harvesters offer an efficient way to convert mechanical energy harvested by every-day human body actions into electrical energy.Triboelectric nanogenerators(TENGs)are an attractive solution for po...Triboelectric energy harvesters offer an efficient way to convert mechanical energy harvested by every-day human body actions into electrical energy.Triboelectric nanogenerators(TENGs)are an attractive solution for power supply concerns in the development of portable electronic gadgets and self-powered sensor applications.Herein,a dielectric calcium copper titanate(CaCu_(3)Ti_(4)O_(12)(CCTO))ceramic material was synthesized by a solid-state reaction process.The synthesized particles were embedded in poly-dimethylsiloxane(PDMS)polymer to form a CCTO/PDMS flexible composite film(FCF)-based TENG,called a CCTO FCF-TENG,which is light-weight,simple,and suitable for use.The dielectric properties,surface charge density,and electrical conductivity of the FCF were greatly improved by the addition of the CCTO particles into the PDMS,resulting in excellent electrical output performance of the corresponding CCTO FCF-TENG.The CCTO FCF-TENG device was constructed with the CCTO/PDMS FCF,which functioned ver-tically against a cellulose paper to optimize a high and stable electrical output.Furthermore,the filler concentration and film thickness optimization was studied more to achieve the highest output power of the CCTO FCF-TENG.The optimized CCTO FCF-TENG exhibited the highest electrical output voltage,cur-rent,charge density,and power density of-250 V,-6.5μA,-70μC/m^(2),and-3.15 W/m 2,respectively.The mechanical stability and durability of the CCTO FCF-TENG were systematically analyzed.The practical and real-time applications of the packed CCTO FCF-TENG were systematically investigated under various harsh environmental conditions.Finally,the packed CCTO FCF-TENG successfully powered several low-power portable electronics and was also used as a self-powered sensor to sense biomechanical actions in everyday human body activities.展开更多
Self-powered flexible devices with skin-like multiple sensing ability have attracted great attentions due to their broad applications in the Internet of Things(IoT).Various methods have been proposed to enhance mechan...Self-powered flexible devices with skin-like multiple sensing ability have attracted great attentions due to their broad applications in the Internet of Things(IoT).Various methods have been proposed to enhance mechano-optic or electric performance of the flexible devices;however,it remains challenging to realize the display and accurate recognition of motion trajectories for intelligent control.Here,we present a fully self-powered mechanoluminescent-triboelectric bimodal sensor based on micronanostructured mechanoluminescent elastomer,which can patterned-display the force trajectories.The deformable liquid metals used as stretchable electrode make the stress transfer stable through overall device to achieve outstanding mechanoluminescence(with a gray value of 107 under a stimulus force as low as 0.3 N and more than 2000 cycles reproducibility).Moreover,a microstructured surface is constructed which endows the resulted composite with significantly improved triboelectric performances(voltage increases from 8 to 24 V).Based on the excellent bimodal sensing performances and durability of the obtained composite,a highly reliable intelligent control system by machine learning has been developed for controlling trolley,providing an approach for advanced visual interaction devices and smart wearable electronics in the future IoT era.展开更多
The low-altitude transport has demonstrated significant growth potential driven by rapid advancements in unmanned aerial vehicles(UAVs)technology.Herein,rotor UAVs are increasingly favored by consumers due to their un...The low-altitude transport has demonstrated significant growth potential driven by rapid advancements in unmanned aerial vehicles(UAVs)technology.Herein,rotor UAVs are increasingly favored by consumers due to their unique advantages.The UAVs motion is altered by adjusting propeller speed,which is governed by motor speed.Consequently,motor speed is a key factor influencing flight performance that is susceptible to environmental interference.Accurate and real-time monitoring of motor speed is essential.Conventional speed sensors are bulky,reliant on external power,and challenging to integration into compact UAVs systems.They also suffer from insufficient accuracy and unstable measurements,particularly with small motors.This article introduces a self-powered digital aircraft rotational speed sensor(SDARSS)utilizing a rotating triboelectric nanogenerators(TENGs)to address current challenges.This sensor is lightweight,energy-efficient,and self-powered,weighing only 2.185 g and measuring 3.43 mm in thickness,with an accuracy exceeding 99.94%.It measures speeds up to 10,000 revolutions per minute(rpm)with exceptional precision and stability.The sensor enables real-time monitoring of UAVs motor speeds,which is crucial for enhancing flight safety.展开更多
Triboelectric nanogenerator(TENG)provides a new solution to the energy supply by harvesting high entropy energy.However,wearable electronic devices have high requirements for flexible,humidity-resistant,and low-cost T...Triboelectric nanogenerator(TENG)provides a new solution to the energy supply by harvesting high entropy energy.However,wearable electronic devices have high requirements for flexible,humidity-resistant,and low-cost TENG.Here,environmentfriendly and multi-functional wheat starch TENG(S-TENG)was made by a simple and green method.The open-circuit voltage and short-circuit current of S-TENG are 151.4 V and 47.1μA,respectively.S-TENG can be used not only to drive and intelligently control electronic equipment,but also to effectively harvest energy from body movements and wind.In addition,the output of S-TENG was not negatively affected with the increase in environmental humidity,but increased abnormally.In the range of 20%RH–80%RH,S-TENG can be potentially used as a sensitive self-powered humidity sensor.The S-TENG paves the way for large-scale preparation of multi-functional biomaterials-based TENG,and practical application of self-powered sensing and wearable devices.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
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
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
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
Today,energy is essential for every aspect of human life,including clothing,food,housing and transportation.However,traditional energy resources are insufficient to meet our modern needs.Self-powered sensing devices e...Today,energy is essential for every aspect of human life,including clothing,food,housing and transportation.However,traditional energy resources are insufficient to meet our modern needs.Self-powered sensing devices emerge as promising alternatives,offering sustained operation without relying on external power sources.Leveraging advancements in materials and manufacturing research,these devices can autonomously harvest energy from various sources.In this review,we focus on the current landscape of self-powered wearable sensors,providing a concise overview of energy harvesting technologies,conversion mechanisms,structural or material innovations,and energy storage platforms.Then,we present experimental advances in different energy sources,showing their underlying mechanisms,and the potential for energy acquisition.Furthermore,we discuss the applications of self-powered flexible sensors in diverse fields such as medicine,sports,and food.Despite significant progress in this field,widespread commercialization will necessitate enhanced sensor detection abilities,improved design factors for adaptable devices,and a balance between sensitivity and standardization.展开更多
Looking toward world technology trends over the next few decades, self-powered sensing networks are a key field of technological and economic driver for global industries. Since 2006, Zhong Lin Wang's group has pr...Looking toward world technology trends over the next few decades, self-powered sensing networks are a key field of technological and economic driver for global industries. Since 2006, Zhong Lin Wang's group has proposed a novel concept of nanogenerators(NGs), including piezoelectric nanogenerator and triboelectric nanogenerator, which could convert a mechanical trigger into an electric output. Considering motion ubiquitously exists in the surrounding environment and for any most common materials used every day, NGs could be inherently served as an energy source for our daily increasing requirements or as one of self-powered environmental sensors. In this regard, by coupling the piezoelectric or triboelectric properties with semiconducting gas sensing characterization, a new research field of self-powered gas sensing has been proposed. Recent works have shown promising concept to realize NG-based self-powered gas sensors that are capable of detecting gas environment without the need of external power sources to activate the gas sensors or to actively generate a readout signal. Compared with conventional sensors, these self-powered gas sensors keep the approximate performance.Meanwhile, these sensors drastically reduce power consumption and additionally reduce the required space for integration,which are significantly suitable for the wearable devices. This paper gives a brief summary about the establishment and latest progress in the fundamental principle, updated progress and potential applications of NG-based self-powered gas sensing system. The development trend in this field is envisaged, and the basic configurations are also introduced.展开更多
Due to its ability to convert body heat into electricity,organic thermoelectric material is considered a promising and smart maintenance-free power source to charge wearable electronics.However,developing flexible n-t...Due to its ability to convert body heat into electricity,organic thermoelectric material is considered a promising and smart maintenance-free power source to charge wearable electronics.However,developing flexible n-type organic thermoelectric materials and wearable p/n junction thermoelectric devices remains challenging.In this work,two insulated polyamides(PA6 and PA66)that have been widely used as fiber materials are employed as novel dopants for converting p-type single-walled carbon nanotubes(SWCNTs)to n-type thermoelectric materials.Because of the electron transferability of the amide group,polyamide-doped SWCNTs exhibit excellent thermopower values as large as-56.0μV K^(-1) for PA66,and-54.5μV K^(-1) for PA6.Thermoelectric devices with five p/n junctions connected in series are fabricated.The testing device produces a thermoelectric voltage of 43.1 mV and generates 1.85μW thermoelectric power under temperature gradients of approximately 80 K.Furthermore,they display charming capability for temperature recognition and monitoring human activities as sensors.These promising results suggest that the flexible polyamide-doped SWCNT composites herein have high application potential as wearable thermoelectric electronics.展开更多
This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environment...This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environmental energy self-harvesting, and intelligent perception, and can operate stably for a long time in complex and changing environments. The self-powered system of the sensor can meet the needs of long-term bridge service performance monitoring, significantly improving the coverage and efficiency of monitoring. By optimizing the sensor system design, the maximum energy conversion of the energy harvesting unit is achieved. In order to verify the function and practicality of the new SPWL monitoring sensor, this study combined the actual bridge engineering, carried out a bridge monitoring case study, and developed an SPWL monitoring scheme based on the bridge structure principle. Compared with traditional monitoring methods, this technology significantly improves the sustainability and performance of infrastructure monitoring based on the new SPWL sensor, fully demonstrating the excellent monitoring capabilities of this type of sensor, and providing strong support for the development of intelligent transportation and intelligent infrastructure.展开更多
Quantitative determination of tetracycline(TC)in environment and foods is of great importance,as excessive residues might have negative effects on human health and environmental risks.Herein,a selfpowered molecularly ...Quantitative determination of tetracycline(TC)in environment and foods is of great importance,as excessive residues might have negative effects on human health and environmental risks.Herein,a selfpowered molecularly imprinted photoelectrochemical(PEC)sensor based on the Zn O/C photoanode and the Fe-doped CuBi_(2)O_(4)(CBFO)photocathode is developed for the sensitive detection of TC.The photocathodic current can be amplified by the efficient electron transfer caused by the Fermi energy level gap between the photoanode and photocathode.Furthermore,molecularly imprinted polymers(MIPs)at photocathode can selectivity identify the TC templates and thus improve the specificity.Under the optimal conditions,the sensor has a linear range of 10^(-2)-1.0×10^(5) nmol/L,and a limit of detection(LOD)of 0.007 nmol/L(S/N=3).More crucially,the milk sample detection is carried out using the as-prepared sensor,and the outcome is satisfactory.The research gives us a novel sensing platform for quick and accurate antibiotic(like TC)in environment and food monitoring.展开更多
Polar semiconductors,particularly the emerging polar two-dimensional(2D)halide perovskites,have motivated immense interest in diverse photoelectronic devices due to their distinguishing polarizationgenerated photoelec...Polar semiconductors,particularly the emerging polar two-dimensional(2D)halide perovskites,have motivated immense interest in diverse photoelectronic devices due to their distinguishing polarizationgenerated photoelectric effects.However,the constraints on the organic cation's choice are still subject to limitations of polar 2D halide perovskites due to the size of the inorganic pocket between adjacent corner-sharing octahedra.Herein,a mixed spacer cation ordering strategy is employed to assemble a polar 2D halide perovskite NMAMAPb Br_(4)(NMPB,NMA is N-methylbenzene ammonium,MA is methylammonium)with alternating cation in the interlayer space.Driven by the incorporation of a second MA cation,the perovskite layer transformed from a 2D Pb_(7)Br_(24)anionic network with corner-and face-sharing octahedra to a flat 2D PbBr_(4)perovskite networks only with corner-sharing octahedra.In the crystal structure of NMPB,the asymmetric hydrogen-bonding interactions between ordered mixed-spacer cations and 2D perovskite layers give rise to a second harmonic generation response and a large polarization of 1.3μC/cm^(2).More intriguingly,the ordered 2D perovskite networks endow NMPB with excellent self-powered polarization-sensitive detection performance,showing a considerable polarization-related dichroism ratio up to 1.87.The reconstruction of an inorganic framework within a crystal through mixed cation ordering offers a new synthetic tool for templating perovskite lattices with controlled properties,overcoming limitations of conventional cation choice.展开更多
A new self-powered/self-cleaned atmosphere monitoring system has been fabricated from TiO_(2)nanoparticles through combining hydrovoltaic,gas sensing and photocatalytic effects.The TiO_(2)nanoparticle film can convert...A new self-powered/self-cleaned atmosphere monitoring system has been fabricated from TiO_(2)nanoparticles through combining hydrovoltaic,gas sensing and photocatalytic effects.The TiO_(2)nanoparticle film can convert natural thermal energy into electricity(hydrovoltaic effect)by the spontaneous water evaporation.The hydrovoltaic/gas-sensing coupling effect of TiO_(2)nanoparticle offers the waterevaporation-powered gas detection performance,and the outputting voltage/current has a good response to the surrounding gas atmosphere,directly acting as the gas sensing signal.The zeta potential of TiO_(2)is changed by the surface adsorption of gas molecules,and thus affects the electricity output of the system.The outputting electricity can directly power a wireless transmitter for transmitting the sensing information to external platform,and the whole system can work independently without electricity power supply.The rainwater can be used as the fuel of the system,and thus the system can be used outdoors without scheduled maintenance.Moreover,the photocatalytic activity of TiO_(2)can effectively degrade the organic pollutants on the film under photo illumination,leading to a self-clean behavior of the system.The system can probably promote the development of green sensing techniques with evaporation-induced ability.展开更多
Smart actuators integrated with sensing functions are taking a significant role in constructing intelligent robots.However,the detection of sensing signals in most actuators requires external electrical power,lacking ...Smart actuators integrated with sensing functions are taking a significant role in constructing intelligent robots.However,the detection of sensing signals in most actuators requires external electrical power,lacking in the self-powered feature.Herein,we report a graphene-based light-driven actuator with self-powered sensing function,which is designed by integrating a photothermoelectric generator into the actuator intelligently.When one part of the actuator is irradiated by near-infrared light,it shows a deformation with bending curvature up to 1.5 cm^(−1),owing to the mismatch volume changes between two layers of the actuator.Meanwhile,the temperature difference across the actuator generates a voltage signal due to the photo-thermoelectric effect.The Seebeck coefficient is higher than 40μV/K.Furthermore,the self-powered voltage signal is consistent with the deformation trend,which can be used to characterize the deformation state of actuator without external electrical power.We further demonstrate a gripper and a bionic hand.Their deformations mimic the motions of human hand(or finger),even making complex gestures.Concurrently,they can output self-powered voltage signals for sensing.We hope this research will pave a new way for selfpowered devices,state-of-the-art intelligent robots,and other integrated multi-functional systems.展开更多
The rapid development of the global economy and population growth are accompanied by the production of numerous waste textiles.This leads to a waste of limited resources and serious environmental pollution problems ca...The rapid development of the global economy and population growth are accompanied by the production of numerous waste textiles.This leads to a waste of limited resources and serious environmental pollution problems caused by improper disposal.The rational recycling of wasted textiles and their transformation into high-value-added emerging products,such as smart wearable devices,is fascinating.Here,we propose a novel roadmap for turning waste cotton fabrics into three-dimensional elastic fiber-based thermoelectric aerogels by a one-step lyophilization process with decoupled self-powered temperature-compression strain dual-parameter sensing properties.The thermoelectric aerogel exhibits a fast compression response time of 0.2 s,a relatively high Seebeck coefficient of 43μV·K^(-1),and an ultralow thermal conductivity of less than 0.04 W·m^(-1)·K^(-1).The cross-linking of trimethoxy(methyl)silane(MTMS)and cellulose endowed the aerogel with excellent elasticity,allowing it to be used as a compressive strain sensor for guessing games and facial expression recognition.In addition,based on the thermoelectric effect,the aerogel can perform temperature detection and differentiation in self-powered mode with the output thermal voltage as the stimulus signal.Furthermore,the wearable system,prepared by connecting the aerogel-prepared array device with a wireless transmission module,allows for temperature alerts in a mobile phone application without signal interference due to the compressive strains generated during gripping.Hence,our strategy is significant for reducing global environmental pollution and provides a revelatory path for transforming waste textiles into high-value-added smart wearable devices.展开更多
Real-time onboard health monitoring systems are critical for the railway industry to maintain high service quality and operational safety.However,the issue with power supplies for monitoring sensors persists,especiall...Real-time onboard health monitoring systems are critical for the railway industry to maintain high service quality and operational safety.However,the issue with power supplies for monitoring sensors persists,especially for freight trains that lack onboard power.Here,we propose a hybrid piezoelectric-triboelectric rotary generator(HPT-RG)for energy harvesting and vehicle speed sensing.The HPT-RG incorporates a rotational self-adaptive technique that softens the equivalent stiffness,enabling the piezoelectric non-resonant beam to surpass resonance limitations in a low-frequency region.The experiments demonstrate the feasibility of using the HPT-RG as an energy harvesting module to collect the rotational energy of the freight rail transport and power the wireless temperature sensors.To allow multiple monitoring in confined spaces on trains,a triboelectric sensing module is added to the HPT-RG to sense the operation speed and mileage of vehicles.Furthermore,the generator exhibits favorable mechanical durability under more than 600 h of official testing on the train bogie axle.The proposed HPT-RG is essential for creating a truly self-powered,maintenance-free,and zero-carbon onboard wireless monitoring system on freight railways.展开更多
Developing flexible bioelectronics is essential to the realization of artificial intelligence devices and biomedical applications, such as wearables, but their potential is limited by sustainable energy supply. An enz...Developing flexible bioelectronics is essential to the realization of artificial intelligence devices and biomedical applications, such as wearables, but their potential is limited by sustainable energy supply. An enzymatic biofuel cell(BFC) is promising for power supply, but its use is limited by the challenges of incorporating multiple enzymes and rigid platforms. This paper shows the first example of screen-printable nanocomposite inks engineered for a single-enzyme-based energy-harvesting device and a self-powered biosensor driven by glucose on bioanode and biocathode. The anode ink is modified with naphthoquinone and multiwalled carbon nanotubes(MWCNTs), whereas the cathode ink is modified with Prussian blue/MWCNT hybrid before immobilizing with glucose oxidase. The flexible bioanode and the biocathode consume glucose. This BFC yields an open circuit voltage of 0.45 V and a maximum power density of 266 μW cm-2. The wearable device coupled with a wireless portable system can convert chemical energy into electric energy and detect glucose in artificial sweat. The self-powered sensor can detect glucose concentrations up to 10 mM. Common interfering substances,including lactate, uric acid, ascorbic acid, and creatinine, have no effect on this self-powered biosensor. Additionally, the device can endure multiple mechanical deformations. New advances in ink development and flexible platforms enable a wide range of applications, including on-body electronics, self-sustainable applications, and smart fabrics.展开更多
基金supported by the Research Platform for biomedical and Health Technology, NUS (Suzhou) Research Institute (RP-BHT-Prof. LEE Chengkuo)RIE Advanced Manufacturing and Engineering (AME) Programmatic Grant (Grant A18A4b0055)+1 种基金RIE 2025-Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) (Grant I2301E0027)Reimagine Research Scheme projects, National University of Singapore, A-0009037-03-00 and A-0009454-01-00 and Reimagine Research Scheme projects, National University of Singapore, A-0004772-00-00 and A-0004772-01-00。
文摘Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management.Existing outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex structures.Herein,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by it.It can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous operation.Moreover,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption methods.Its potential extends to acting as other self-powered environmental sensors.This multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIP)(No.2018R1A6A1A03025708)。
文摘Triboelectric energy harvesters offer an efficient way to convert mechanical energy harvested by every-day human body actions into electrical energy.Triboelectric nanogenerators(TENGs)are an attractive solution for power supply concerns in the development of portable electronic gadgets and self-powered sensor applications.Herein,a dielectric calcium copper titanate(CaCu_(3)Ti_(4)O_(12)(CCTO))ceramic material was synthesized by a solid-state reaction process.The synthesized particles were embedded in poly-dimethylsiloxane(PDMS)polymer to form a CCTO/PDMS flexible composite film(FCF)-based TENG,called a CCTO FCF-TENG,which is light-weight,simple,and suitable for use.The dielectric properties,surface charge density,and electrical conductivity of the FCF were greatly improved by the addition of the CCTO particles into the PDMS,resulting in excellent electrical output performance of the corresponding CCTO FCF-TENG.The CCTO FCF-TENG device was constructed with the CCTO/PDMS FCF,which functioned ver-tically against a cellulose paper to optimize a high and stable electrical output.Furthermore,the filler concentration and film thickness optimization was studied more to achieve the highest output power of the CCTO FCF-TENG.The optimized CCTO FCF-TENG exhibited the highest electrical output voltage,cur-rent,charge density,and power density of-250 V,-6.5μA,-70μC/m^(2),and-3.15 W/m 2,respectively.The mechanical stability and durability of the CCTO FCF-TENG were systematically analyzed.The practical and real-time applications of the packed CCTO FCF-TENG were systematically investigated under various harsh environmental conditions.Finally,the packed CCTO FCF-TENG successfully powered several low-power portable electronics and was also used as a self-powered sensor to sense biomechanical actions in everyday human body activities.
基金the National Natural Science Foundation of China(52173112 and 51873123)Sichuan Provincial Natural Science Fund for Distinguished Young Scholars(2021JDJQ0017)the Program for Featured Directions of Engineering Multidisciplines of Sichuan University(No:2020SCUNG203)for financial support。
文摘Self-powered flexible devices with skin-like multiple sensing ability have attracted great attentions due to their broad applications in the Internet of Things(IoT).Various methods have been proposed to enhance mechano-optic or electric performance of the flexible devices;however,it remains challenging to realize the display and accurate recognition of motion trajectories for intelligent control.Here,we present a fully self-powered mechanoluminescent-triboelectric bimodal sensor based on micronanostructured mechanoluminescent elastomer,which can patterned-display the force trajectories.The deformable liquid metals used as stretchable electrode make the stress transfer stable through overall device to achieve outstanding mechanoluminescence(with a gray value of 107 under a stimulus force as low as 0.3 N and more than 2000 cycles reproducibility).Moreover,a microstructured surface is constructed which endows the resulted composite with significantly improved triboelectric performances(voltage increases from 8 to 24 V).Based on the excellent bimodal sensing performances and durability of the obtained composite,a highly reliable intelligent control system by machine learning has been developed for controlling trolley,providing an approach for advanced visual interaction devices and smart wearable electronics in the future IoT era.
基金supported by the National Natural Science Foundation of China(No.52203324)the National Key R&D Program of China(No.2023YFB2604600)+1 种基金the National Key R&D Project from the Minister of Science and Technology(No.2021YFA1201601)Thanks to the Georgia Institute of Technology for providing software support。
文摘The low-altitude transport has demonstrated significant growth potential driven by rapid advancements in unmanned aerial vehicles(UAVs)technology.Herein,rotor UAVs are increasingly favored by consumers due to their unique advantages.The UAVs motion is altered by adjusting propeller speed,which is governed by motor speed.Consequently,motor speed is a key factor influencing flight performance that is susceptible to environmental interference.Accurate and real-time monitoring of motor speed is essential.Conventional speed sensors are bulky,reliant on external power,and challenging to integration into compact UAVs systems.They also suffer from insufficient accuracy and unstable measurements,particularly with small motors.This article introduces a self-powered digital aircraft rotational speed sensor(SDARSS)utilizing a rotating triboelectric nanogenerators(TENGs)to address current challenges.This sensor is lightweight,energy-efficient,and self-powered,weighing only 2.185 g and measuring 3.43 mm in thickness,with an accuracy exceeding 99.94%.It measures speeds up to 10,000 revolutions per minute(rpm)with exceptional precision and stability.The sensor enables real-time monitoring of UAVs motor speeds,which is crucial for enhancing flight safety.
基金supported by the National Key R&D Project from Ministry of Science and Technology,China(Nos.2016YFA0202702 and 2016YFA0202701)the Key Research Program of Frontier Sciences,CAS(No.ZDBS-LY-DQC025)。
文摘Triboelectric nanogenerator(TENG)provides a new solution to the energy supply by harvesting high entropy energy.However,wearable electronic devices have high requirements for flexible,humidity-resistant,and low-cost TENG.Here,environmentfriendly and multi-functional wheat starch TENG(S-TENG)was made by a simple and green method.The open-circuit voltage and short-circuit current of S-TENG are 151.4 V and 47.1μA,respectively.S-TENG can be used not only to drive and intelligently control electronic equipment,but also to effectively harvest energy from body movements and wind.In addition,the output of S-TENG was not negatively affected with the increase in environmental humidity,but increased abnormally.In the range of 20%RH–80%RH,S-TENG can be potentially used as a sensitive self-powered humidity sensor.The S-TENG paves the way for large-scale preparation of multi-functional biomaterials-based TENG,and practical application of self-powered sensing and wearable devices.
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 the Shanghai Collaborative Innovation Centre for Tumour Energy Therapy Technology and Equipment。
文摘Today,energy is essential for every aspect of human life,including clothing,food,housing and transportation.However,traditional energy resources are insufficient to meet our modern needs.Self-powered sensing devices emerge as promising alternatives,offering sustained operation without relying on external power sources.Leveraging advancements in materials and manufacturing research,these devices can autonomously harvest energy from various sources.In this review,we focus on the current landscape of self-powered wearable sensors,providing a concise overview of energy harvesting technologies,conversion mechanisms,structural or material innovations,and energy storage platforms.Then,we present experimental advances in different energy sources,showing their underlying mechanisms,and the potential for energy acquisition.Furthermore,we discuss the applications of self-powered flexible sensors in diverse fields such as medicine,sports,and food.Despite significant progress in this field,widespread commercialization will necessitate enhanced sensor detection abilities,improved design factors for adaptable devices,and a balance between sensitivity and standardization.
基金supported by Natural Science Foundation of China(NSFC)(Grant No.U1432249)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)+1 种基金supported by Collaborative Innovation Center of Suzhou Nano Science&Technologysponsored by Qing Lan Project
文摘Looking toward world technology trends over the next few decades, self-powered sensing networks are a key field of technological and economic driver for global industries. Since 2006, Zhong Lin Wang's group has proposed a novel concept of nanogenerators(NGs), including piezoelectric nanogenerator and triboelectric nanogenerator, which could convert a mechanical trigger into an electric output. Considering motion ubiquitously exists in the surrounding environment and for any most common materials used every day, NGs could be inherently served as an energy source for our daily increasing requirements or as one of self-powered environmental sensors. In this regard, by coupling the piezoelectric or triboelectric properties with semiconducting gas sensing characterization, a new research field of self-powered gas sensing has been proposed. Recent works have shown promising concept to realize NG-based self-powered gas sensors that are capable of detecting gas environment without the need of external power sources to activate the gas sensors or to actively generate a readout signal. Compared with conventional sensors, these self-powered gas sensors keep the approximate performance.Meanwhile, these sensors drastically reduce power consumption and additionally reduce the required space for integration,which are significantly suitable for the wearable devices. This paper gives a brief summary about the establishment and latest progress in the fundamental principle, updated progress and potential applications of NG-based self-powered gas sensing system. The development trend in this field is envisaged, and the basic configurations are also introduced.
基金supported by the National Natural Science Foundation of China(Project no.51973120)the Natural Science Foun-dation of Guangdong Province(No.2019A1515010613)+1 种基金the Shenzhen Science and Technology Research Grant(Nos.JCYJ20170818093417096 and JCYJ20180305125649693)the Shenzhen Science and Technology Program(No.20220809111527001).
文摘Due to its ability to convert body heat into electricity,organic thermoelectric material is considered a promising and smart maintenance-free power source to charge wearable electronics.However,developing flexible n-type organic thermoelectric materials and wearable p/n junction thermoelectric devices remains challenging.In this work,two insulated polyamides(PA6 and PA66)that have been widely used as fiber materials are employed as novel dopants for converting p-type single-walled carbon nanotubes(SWCNTs)to n-type thermoelectric materials.Because of the electron transferability of the amide group,polyamide-doped SWCNTs exhibit excellent thermopower values as large as-56.0μV K^(-1) for PA66,and-54.5μV K^(-1) for PA6.Thermoelectric devices with five p/n junctions connected in series are fabricated.The testing device produces a thermoelectric voltage of 43.1 mV and generates 1.85μW thermoelectric power under temperature gradients of approximately 80 K.Furthermore,they display charming capability for temperature recognition and monitoring human activities as sensors.These promising results suggest that the flexible polyamide-doped SWCNT composites herein have high application potential as wearable thermoelectric electronics.
文摘This paper aims to study a novel smart self-powered wireless lightweight (SPWL) bridge health monitoring sensor, which integrates key technologies such as large-scale, low-power wireless data transmission, environmental energy self-harvesting, and intelligent perception, and can operate stably for a long time in complex and changing environments. The self-powered system of the sensor can meet the needs of long-term bridge service performance monitoring, significantly improving the coverage and efficiency of monitoring. By optimizing the sensor system design, the maximum energy conversion of the energy harvesting unit is achieved. In order to verify the function and practicality of the new SPWL monitoring sensor, this study combined the actual bridge engineering, carried out a bridge monitoring case study, and developed an SPWL monitoring scheme based on the bridge structure principle. Compared with traditional monitoring methods, this technology significantly improves the sustainability and performance of infrastructure monitoring based on the new SPWL sensor, fully demonstrating the excellent monitoring capabilities of this type of sensor, and providing strong support for the development of intelligent transportation and intelligent infrastructure.
基金supported by the Fuxiaquan Collaborative Innovation Platform(No.K30001)Major Scientific Research Program for Young and Middle-aged Health Professionals of Fujian Province,China(No.2022ZQNZD007)Youth Innovation Technology Project of Higher School in Shandong Province(Food Nanotechnology Innovation Team)。
文摘Quantitative determination of tetracycline(TC)in environment and foods is of great importance,as excessive residues might have negative effects on human health and environmental risks.Herein,a selfpowered molecularly imprinted photoelectrochemical(PEC)sensor based on the Zn O/C photoanode and the Fe-doped CuBi_(2)O_(4)(CBFO)photocathode is developed for the sensitive detection of TC.The photocathodic current can be amplified by the efficient electron transfer caused by the Fermi energy level gap between the photoanode and photocathode.Furthermore,molecularly imprinted polymers(MIPs)at photocathode can selectivity identify the TC templates and thus improve the specificity.Under the optimal conditions,the sensor has a linear range of 10^(-2)-1.0×10^(5) nmol/L,and a limit of detection(LOD)of 0.007 nmol/L(S/N=3).More crucially,the milk sample detection is carried out using the as-prepared sensor,and the outcome is satisfactory.The research gives us a novel sensing platform for quick and accurate antibiotic(like TC)in environment and food monitoring.
基金supported by the National Natural Science Foundation of China(Nos.22193042,22125110,22075285,52473283,21921001,U21A2069)the Key Research Program of Frontier Sciences of the Chinese Academy of Sciences(No.ZDBS-LY-SLH024)the Youth Innovation Promotion of Chinese Academy of Sciences(No.2020307)。
文摘Polar semiconductors,particularly the emerging polar two-dimensional(2D)halide perovskites,have motivated immense interest in diverse photoelectronic devices due to their distinguishing polarizationgenerated photoelectric effects.However,the constraints on the organic cation's choice are still subject to limitations of polar 2D halide perovskites due to the size of the inorganic pocket between adjacent corner-sharing octahedra.Herein,a mixed spacer cation ordering strategy is employed to assemble a polar 2D halide perovskite NMAMAPb Br_(4)(NMPB,NMA is N-methylbenzene ammonium,MA is methylammonium)with alternating cation in the interlayer space.Driven by the incorporation of a second MA cation,the perovskite layer transformed from a 2D Pb_(7)Br_(24)anionic network with corner-and face-sharing octahedra to a flat 2D PbBr_(4)perovskite networks only with corner-sharing octahedra.In the crystal structure of NMPB,the asymmetric hydrogen-bonding interactions between ordered mixed-spacer cations and 2D perovskite layers give rise to a second harmonic generation response and a large polarization of 1.3μC/cm^(2).More intriguingly,the ordered 2D perovskite networks endow NMPB with excellent self-powered polarization-sensitive detection performance,showing a considerable polarization-related dichroism ratio up to 1.87.The reconstruction of an inorganic framework within a crystal through mixed cation ordering offers a new synthetic tool for templating perovskite lattices with controlled properties,overcoming limitations of conventional cation choice.
基金the National Natural Science Foundation of China(No.11674048)Sichuan Science and Technology Program(2020JDJQ0026)。
文摘A new self-powered/self-cleaned atmosphere monitoring system has been fabricated from TiO_(2)nanoparticles through combining hydrovoltaic,gas sensing and photocatalytic effects.The TiO_(2)nanoparticle film can convert natural thermal energy into electricity(hydrovoltaic effect)by the spontaneous water evaporation.The hydrovoltaic/gas-sensing coupling effect of TiO_(2)nanoparticle offers the waterevaporation-powered gas detection performance,and the outputting voltage/current has a good response to the surrounding gas atmosphere,directly acting as the gas sensing signal.The zeta potential of TiO_(2)is changed by the surface adsorption of gas molecules,and thus affects the electricity output of the system.The outputting electricity can directly power a wireless transmitter for transmitting the sensing information to external platform,and the whole system can work independently without electricity power supply.The rainwater can be used as the fuel of the system,and thus the system can be used outdoors without scheduled maintenance.Moreover,the photocatalytic activity of TiO_(2)can effectively degrade the organic pollutants on the film under photo illumination,leading to a self-clean behavior of the system.The system can probably promote the development of green sensing techniques with evaporation-induced ability.
基金supported by the National Natural Science Foundation of China(Nos.51773039 and 11974076)Natural Science Foundation of Fujian Province(No.2020J02036)Program for New Century Excellent Talents in University of Fujian Province(No.J1-1318).
文摘Smart actuators integrated with sensing functions are taking a significant role in constructing intelligent robots.However,the detection of sensing signals in most actuators requires external electrical power,lacking in the self-powered feature.Herein,we report a graphene-based light-driven actuator with self-powered sensing function,which is designed by integrating a photothermoelectric generator into the actuator intelligently.When one part of the actuator is irradiated by near-infrared light,it shows a deformation with bending curvature up to 1.5 cm^(−1),owing to the mismatch volume changes between two layers of the actuator.Meanwhile,the temperature difference across the actuator generates a voltage signal due to the photo-thermoelectric effect.The Seebeck coefficient is higher than 40μV/K.Furthermore,the self-powered voltage signal is consistent with the deformation trend,which can be used to characterize the deformation state of actuator without external electrical power.We further demonstrate a gripper and a bionic hand.Their deformations mimic the motions of human hand(or finger),even making complex gestures.Concurrently,they can output self-powered voltage signals for sensing.We hope this research will pave a new way for selfpowered devices,state-of-the-art intelligent robots,and other integrated multi-functional systems.
基金supported by the grants(51973027 and 52003044)from the National Natural Science Foundation of Chinathe Fundamental Research Funds for the Central Universities(2232023A-05)+4 种基金the International Cooperation Fund of Science and Technology Commission of Shanghai Municipality(21130750100)Major Scientific and Technological Innovation Projects of Shandong Province(2021CXGC011004)This work has also been supported by the State Key Laboratory for Modification of Chemical Fibers and Polymer Materials(KF2216)the Donghua University Distinguished Young Professor Program to Prof.Liming Wangthe Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University(CUSF-DH-D-2022040)to Xinyang He.
文摘The rapid development of the global economy and population growth are accompanied by the production of numerous waste textiles.This leads to a waste of limited resources and serious environmental pollution problems caused by improper disposal.The rational recycling of wasted textiles and their transformation into high-value-added emerging products,such as smart wearable devices,is fascinating.Here,we propose a novel roadmap for turning waste cotton fabrics into three-dimensional elastic fiber-based thermoelectric aerogels by a one-step lyophilization process with decoupled self-powered temperature-compression strain dual-parameter sensing properties.The thermoelectric aerogel exhibits a fast compression response time of 0.2 s,a relatively high Seebeck coefficient of 43μV·K^(-1),and an ultralow thermal conductivity of less than 0.04 W·m^(-1)·K^(-1).The cross-linking of trimethoxy(methyl)silane(MTMS)and cellulose endowed the aerogel with excellent elasticity,allowing it to be used as a compressive strain sensor for guessing games and facial expression recognition.In addition,based on the thermoelectric effect,the aerogel can perform temperature detection and differentiation in self-powered mode with the output thermal voltage as the stimulus signal.Furthermore,the wearable system,prepared by connecting the aerogel-prepared array device with a wireless transmission module,allows for temperature alerts in a mobile phone application without signal interference due to the compressive strains generated during gripping.Hence,our strategy is significant for reducing global environmental pollution and provides a revelatory path for transforming waste textiles into high-value-added smart wearable devices.
基金supported by the National Natural Science Foundation of China(Grant Nos.12302022,12172248,12021002,and 12132010)Tianjin Research Program of Application Foundation and Advanced Technology(Grant No.22JCQNJC00780)+1 种基金the State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures(Grant No.KF2024-09)the IoT Standards and Application Key Laboratory of the Ministry of Industry and Information Technology(Grant No.202306).
文摘Real-time onboard health monitoring systems are critical for the railway industry to maintain high service quality and operational safety.However,the issue with power supplies for monitoring sensors persists,especially for freight trains that lack onboard power.Here,we propose a hybrid piezoelectric-triboelectric rotary generator(HPT-RG)for energy harvesting and vehicle speed sensing.The HPT-RG incorporates a rotational self-adaptive technique that softens the equivalent stiffness,enabling the piezoelectric non-resonant beam to surpass resonance limitations in a low-frequency region.The experiments demonstrate the feasibility of using the HPT-RG as an energy harvesting module to collect the rotational energy of the freight rail transport and power the wireless temperature sensors.To allow multiple monitoring in confined spaces on trains,a triboelectric sensing module is added to the HPT-RG to sense the operation speed and mileage of vehicles.Furthermore,the generator exhibits favorable mechanical durability under more than 600 h of official testing on the train bogie axle.The proposed HPT-RG is essential for creating a truly self-powered,maintenance-free,and zero-carbon onboard wireless monitoring system on freight railways.
基金supported by National Research Council of Thailand NRCT (grant number: N41A640129), Prince of Songkla University, Hat Yai, Thailandthe Talent Management Project of Prince of Songkla Universitythe Center of Excellence for Innovation in Chemistry (PERCH-CIC), Ministry of Higher Education, Science, Research, and Innovation (MHESI)。
文摘Developing flexible bioelectronics is essential to the realization of artificial intelligence devices and biomedical applications, such as wearables, but their potential is limited by sustainable energy supply. An enzymatic biofuel cell(BFC) is promising for power supply, but its use is limited by the challenges of incorporating multiple enzymes and rigid platforms. This paper shows the first example of screen-printable nanocomposite inks engineered for a single-enzyme-based energy-harvesting device and a self-powered biosensor driven by glucose on bioanode and biocathode. The anode ink is modified with naphthoquinone and multiwalled carbon nanotubes(MWCNTs), whereas the cathode ink is modified with Prussian blue/MWCNT hybrid before immobilizing with glucose oxidase. The flexible bioanode and the biocathode consume glucose. This BFC yields an open circuit voltage of 0.45 V and a maximum power density of 266 μW cm-2. The wearable device coupled with a wireless portable system can convert chemical energy into electric energy and detect glucose in artificial sweat. The self-powered sensor can detect glucose concentrations up to 10 mM. Common interfering substances,including lactate, uric acid, ascorbic acid, and creatinine, have no effect on this self-powered biosensor. Additionally, the device can endure multiple mechanical deformations. New advances in ink development and flexible platforms enable a wide range of applications, including on-body electronics, self-sustainable applications, and smart fabrics.
