Large-aperture optical components are of paramount importance in domains such as integrated circuits,photolithography,aerospace,and inertial confinement fusion.However,measuring their surface profiles relies predomina...Large-aperture optical components are of paramount importance in domains such as integrated circuits,photolithography,aerospace,and inertial confinement fusion.However,measuring their surface profiles relies predominantly on the phase-shifting approach,which involves collecting multiple interferograms and imposes stringent demands on environmental stability.These issues significantly hinder its ability to achieve real-time and dynamic high-precision measurements.Therefore,this study proposes a high-precision large-aperture single-frame interferometric surface profile measurement(LA-SFISPM)method based on deep learning and explores its capability to realize dynamic measurements with high accuracy.The interferogram is matched to the phase by training the data measured using the small aperture.The consistency of the surface features of the small and large apertures is enhanced via contrast learning and feature-distribution alignment.Hence,high-precision phase reconstruction of large-aperture optical components can be achieved without using a phase shifter.The experimental results show that for the tested mirror withΦ=820 mm,the surface profile obtained from LA-SFISPM is subtracted point-by-point from the ground truth,resulting in a maximum single-point error of 4.56 nm.Meanwhile,the peak-to-valley(PV)value is 0.0758λ,and the simple repeatability of root mean square(SR-RMS)value is 0.00025λ,which aligns well with the measured results obtained by ZYGO.In particular,a significant reduction in the measurement time(reduced by a factor of 48)is achieved compared with that of the traditional phase-shifting method.Our proposed method provides an efficient,rapid,and accurate method for obtaining the surface profiles of optical components with different diameters without employing a phase-shifting approach,which is highly desired in large-aperture interferometric measurement systems.展开更多
The elliptic integral method(EIM) is an efficient analytical approach for analyzing large deformations of elastic beams. However, it faces the following challenges.First, the existing EIM can only handle cases with kn...The elliptic integral method(EIM) is an efficient analytical approach for analyzing large deformations of elastic beams. However, it faces the following challenges.First, the existing EIM can only handle cases with known deformation modes. Second,the existing EIM is only applicable to Euler beams, and there is no EIM available for higher-precision Timoshenko and Reissner beams in cases where both force and moment are applied at the end. This paper proposes a general EIM for Reissner beams under arbitrary boundary conditions. On this basis, an analytical equation for determining the sign of the elliptic integral is provided. Based on the equation, we discover a class of elliptic integral piecewise points that are distinct from inflection points. More importantly, we propose an algorithm that automatically calculates the number of inflection points and other piecewise points during the nonlinear solution process, which is crucial for beams with unknown or changing deformation modes.展开更多
With the intensifying competition in the integrated circuit(IC)industry,the high turnover rate of integrated circuit engineers has become a prominent issue affecting the technological continuity of high-precision,spec...With the intensifying competition in the integrated circuit(IC)industry,the high turnover rate of integrated circuit engineers has become a prominent issue affecting the technological continuity of high-precision,specialized,and innovative enterprises.As a representative of such enterprises,JL Technology has faced challenges to its R&D efficiency due to talent loss in recent years.This study takes this enterprise as a case to explore feasible paths to reduce turnover rates through optimizing training and career development systems.The research designs a method combining learning maps and talent maps,utilizes a competency model to clarify the direction for engineers’skill improvement,implements talent classification management using a nine-grid model,and achieves personalized training through Individual Development Plans(IDPs).Analysis of the enterprise’s historical data reveals that the main reasons for turnover are unclear career development paths and insufficient resources for skill improvement.After pilot implementation,the turnover rate in core departments decreased by 12%,and employee satisfaction with training increased by 24%.The results indicate that matching systematic talent reviews with dynamic learning resources can effectively enhance engineers’sense of belonging.This study provides a set of highly operational management tools for small and medium-sized high-precision,specialized,and innovative technology enterprises,verifies their applicability in such enterprises,and offers replicable experiences for similar enterprises to optimize their talent strategies[1].展开更多
Highly sensitive and stable acetylcholinesterase detection is critical for diagnosing and treating various neurotransmission-related diseases.In this study,a novel colorimetric-fluorescent dual-mode biosensor based on...Highly sensitive and stable acetylcholinesterase detection is critical for diagnosing and treating various neurotransmission-related diseases.In this study,a novel colorimetric-fluorescent dual-mode biosensor based on highly dispersive trimetal-modified graphite-phase carbon nitride nanocomposites for acetylcholinesterase detection was designed and synthesized by phosphorus doping and a mixed-metal MOF strategy.The specific surface area of trimetal-modified graphite-phase carbon nitride nanocomposites increased from 15.81 to 96.69 g m^(-2),and its thermal stability,interfacial charge transfer,and oxidation-reduction capability were enhanced compared with those of graphite-phase carbon nitride.First-principles density functional theory calculations and steady-state kinetic analysis are applied to investigate the electronic structures and efficient peroxidase-mimicking properties of trimetal-modified graphite-phase carbon nitride nanocomposites.The oxidation of 3,3',5,5'-tetramethylbenzidin was inhibited by thiocholine,which originates from the decomposition of thiocholine iodide by Acetyl-cholinesterase(AChE),resulting in changes in fluorescence and absorbance intensity.Due to the independence and complementarity of the signals,a highly precise colorimetric-fluorescent dual-mode biosensor with a linear range for detecting AChE of 4-20μU mL^(-1) and detection limits of 0.13μU mL^(-1)(colorimetric)and 0.04μU mL^(-1)(fluorescence)was developed.The spiking recovery of AChE in actual samples was 99.0%-100.4%.Therefore,a highly accurate,specific,and stable dual-mode biosensor is available for AChE detection,and this biosensor has the potential for the analysis of other biomarkers.展开更多
Shot peening is commonly employed for surface deformation strengthening of cylindrical surface part.Therefore,it is critical to understand the effects of shot peening on residual stress and surface topography.Compared...Shot peening is commonly employed for surface deformation strengthening of cylindrical surface part.Therefore,it is critical to understand the effects of shot peening on residual stress and surface topography.Compared to flat surface,cylindrical surface shot peening has two significant features:(i)the curvature of the cylindrical surface and the scattering of the shot stream cause dis-tributed impact velocities;(i)the rotation of the part results in a periodic variation of the impact velocity component.Therefore,it is a challenge to quickly and accurately predict the shot peening residual stress and surface topography of cylindrical surface.This paper developed a high-precision model which considers the more realistic shot peening process.Firstly,a kinematic analysis model was developed to simulate the relative movement of numerous shots and cylindrical surface.Then,the spatial distribution and time-varying impact information was calculated.Subsequently,the impact information was used for finite element modeling to predict residual stress and surface topography.The proposed kinematic analysis method was validated by comparison with the dis-crete element method.Meanwhile,9310 high strength steel rollers shot peening test verified the effectiveness of the model in predicting the residual stress and surface topography.In addition,the effects of air pressure and attack angle on the residual stress and surface topography were investigated.This work could provide a functional package for efficient prediction of the surface integrity and guide industrial application in cylindrical surface shot peening.展开更多
The complex wiring,bulky data collection devices,and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices.To tackle these chal...The complex wiring,bulky data collection devices,and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices.To tackle these challenges,this work develops an artificial intelligenceassisted,wireless,flexible,and wearable mechanoluminescent strain sensor system(AIFWMLS)by integration of deep learning neural network-based color data processing system(CDPS)with a sandwich-structured flexible mechanoluminescent sensor(SFLC)film.The SFLC film shows remarkable and robust mechanoluminescent performance with a simple structure for easy fabrication.The CDPS system can rapidly and accurately extract and interpret the color of the SFLC film to strain values with auto-correction of errors caused by the varying color temperature,which significantly improves the accuracy of the predicted strain.A smart glove mechanoluminescent sensor system demonstrates the great potential of the AIFWMLS system in human gesture recognition.Moreover,the versatile SFLC film can also serve as a encryption device.The integration of deep learning neural network-based artificial intelligence and SFLC film provides a promising strategy to break the“color to strain value”bottleneck that hinders the practical application of flexible colorimetric strain sensors,which could promote the development of wearable and flexible strain sensors from laboratory research to consumer markets.展开更多
Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,a...Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.展开更多
Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the m...Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.展开更多
Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,etc.With the development of sensor technology,wireless communication,smart...Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,etc.With the development of sensor technology,wireless communication,smart monitoring terminal,cloud storage/computing technology,and artificial intelligence,smart gas sensors represent the future of gassensing due to their merits of real-time multifunctional monitoring,earlywarning function,and intelligent and automated feature.Various electronicand optoelectronic gas sensors have been developed for high-performancesmart gas analysis.With the development of smart terminals and the maturityof integrated technology,flexible and wearable gas sensors play an increasingrole in gas analysis.This review highlights recent advances of smart gassensors in diverse applications.The structural components and fundamentalprinciples of electronic and optoelectronic gas sensors are described,andflexible and wearable gas sensor devices are highlighted.Moreover,sensorarray with artificial intelligence algorithms and smart gas sensors in“Internet of Things”paradigm are introduced.Finally,the challengesand perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.展开更多
Nowadays,force sensors play an important role in industrial production,electronic information,medical health,and many other fields.Two-dimensional material-based filed effect transistor(2D-FET)sensors are competitive ...Nowadays,force sensors play an important role in industrial production,electronic information,medical health,and many other fields.Two-dimensional material-based filed effect transistor(2D-FET)sensors are competitive with nano-level size,lower power consumption,and accurate response.However,few of them has the capability of impulse detection which is a path function,expressing the cumulative effect of the force on the particle over a period of time.Herein we fabricated the flexible polymethyl methacrylate(PMMA)gate dielectric MoS_(2)-FET for force and impulse sensor application.We systematically investigated the responses of the sensor to constant force and varying forces,and achieved the conversion factors of the drain current signals(I_(ds))to the detected impulse(I).The applied force was detected and recorded by I_(ds)with a low power consumption of~30 nW.The sensitivity of the device can reach~8000%and the 4×1 sensor array is able to detect and locate the normal force applied on it.Moreover,there was almost no performance loss for the device as left in the air for two months.展开更多
In order to improve the reliability of the spacecraft micro cold gas propulsion system and realize the precise control of the spacecraft attitude and orbit, a micro-thrust, high-precision cold gas thruster is carried ...In order to improve the reliability of the spacecraft micro cold gas propulsion system and realize the precise control of the spacecraft attitude and orbit, a micro-thrust, high-precision cold gas thruster is carried out, at the same time due to the design requirements of the spacecraft, this micro-thrust should be continuous working more than 60 minutes, the traditional solenoid valve used for the thrusts can’t complete the mission, so a long-life micro latching valve is developed as the control valve for this micro thruster, because the micro latching valve can keep its position when it cuts off the outage. Firstly, the authors introduced the design scheme and idea of the thruster. Secondly, the performance of the latching valve and the flow characteristics of the nozzle were simulated. Finally, from the experimental results and compared with the numerical study, it shows that the long-life micro cold gas thruster developed in this paper meets the mission requirements.展开更多
Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds.Accurate data analysis is crucial for converting external stimuli from each artificial sense in...Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds.Accurate data analysis is crucial for converting external stimuli from each artificial sense into user-relevant information,yet conventional signal processing methods struggle with the massive scale,noise,and artificial sensory systems characteristics of data generated by artificial sensory devices.Integrating artificial intelligence(AI)is essential for addressing these challenges and enhancing the performance of artificial sensory systems,making it a rapidly growing area of research in recent years.However,no studies have systematically categorized the output functions of these systems or analyzed the associated AI algorithms and data processing methods.In this review,we present a systematic overview of the latest AI techniques aimed at enhancing the cognitive capabilities of artificial sensory systems replicating the five human senses:touch,taste,vision,smell,and hearing.We categorize the AI-enabled capabilities of artificial sensory systems into four key areas:cognitive simulation,perceptual enhancement,adaptive adjustment,and early warning.We introduce specialized AI algorithms and raw data processing methods for each function,designed to enhance and optimize sensing performance.Finally,we offer a perspective on the future of AI-integrated artificial sensory systems,highlighting technical challenges and potential real-world application scenarios for further innovation.Integration of AI with artificial sensory systems will enable advanced multimodal perception,real-time learning,and predictive capabilities.This will drive precise environmental adaptation and personalized feedback,ultimately positioning these systems as foundational technologies in smart healthcare,agriculture,and automation.展开更多
Earth sensors are widely used in spacecraft for attitude determination. They need to have a very large field of view(FOV)( > 120°) and relatively low accuracy while being used in the aircrafts around orbit. A ...Earth sensors are widely used in spacecraft for attitude determination. They need to have a very large field of view(FOV)( > 120°) and relatively low accuracy while being used in the aircrafts around orbit. A triple-FOV infrared earth sensor is proposed in this paper. It uses three pieces of standard infrared detectors with a wavelength range of 14;16μm,to sense the horizontal circle by detecting the infrared light emitted from the earth. From which,the geocentric vector can be obtained. A mathematic model is established and a validation model is set up to provide input parameters for the mathematic model. The simulation results of the two models show that the output of the mathematic model coincides with the known parameters. Based on the above analysis,a prototype has been built and tested. The test results show that the angle measurement error is about 0. 002° and hence such a triple-FOV earth sensor is capable to provide high-precision position information for autonomous navigation.展开更多
In recent decades,capacitive pressure sensors(CPSs)with high sensitivity have demonstrated significant potential in applications such as medical monitoring,artificial intelligence,and soft robotics.Efforts to enhance ...In recent decades,capacitive pressure sensors(CPSs)with high sensitivity have demonstrated significant potential in applications such as medical monitoring,artificial intelligence,and soft robotics.Efforts to enhance this sensitivity have predominantly focused on material design and structural optimization,with surface microstructures such as wrinkles,pyramids,and micro-pillars proving effective.Although finite element modeling(FEM)has guided enhancements in CPS sensitivity across various surface designs,a theoretical understanding of sensitivity improvements remains underexplored.This paper employs sinusoidal wavy surfaces as a representative model to analytically elucidate the underlying mechanisms of sensitivity enhancement through contact mechanics.These theoretical insights are corroborated by FEM and experimental validations.Our findings underscore that optimizing material properties,such as Young’s modulus and relative permittivity,alongside adjustments in surface roughness and substrate thickness,can significantly elevate the sensitivity.The optimal performance is achieved when the amplitude-to-wavelength ratio(H/)is about 0.2.These results offer critical insights for designing ultrasensitive CPS devices,paving the way for advancements in sensor technology.展开更多
The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an over...The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an overwhelming tendency,providing powerful tools for remote health monitoring and personal health management.Among many candidates,two-dimensional(2D)materials stand out due to several exotic mechanical,electrical,optical,and chemical properties that can be efficiently integrated into atomic-thin films.While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene,the rapid development of new 2D materials with exotic properties has opened up novel applications,particularly in smart interaction and integrated functionalities.This review aims to consolidate recent progress,highlight the unique advantages of 2D materials,and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices.We begin with an in-depth analysis of the advantages,sensing mechanisms,and potential applications of 2D materials in wearable biodevice fabrication.Following this,we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body.Special attention is given to showcasing the integration of multi-functionality in 2D smart devices,mainly including self-power supply,integrated diagnosis/treatment,and human–machine interaction.Finally,the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of2D materials for advanced biodevices.展开更多
CuO nanoparticles were successfully synthesized via a two-jet electrospun method,and then screen-printed on silver-carbon electrodes,forming CuO-modified Ag-C(CuO/Ag-C)disposable strip electrodes.In natural environmen...CuO nanoparticles were successfully synthesized via a two-jet electrospun method,and then screen-printed on silver-carbon electrodes,forming CuO-modified Ag-C(CuO/Ag-C)disposable strip electrodes.In natural environment condition for glucose detection,the obtained CuO/Ag-C electrodes show a high sensitivity of 540 nA·mM^(-1)·cm^(-2),and a low limit of detection(0.68 mM)in a wide linear response range of 0.68 mM and 3 mM(signal/noise=3),respectively.In addition,the CuO/Ag-C electrodes also exhibit excellent anti-interference,air stability and repeatability.As a result,the fabrication of CuO nanoparticles via an electrospun process and the technique of screen-printed electrodes are of great significance for glucose detection.展开更多
Liquid leakage of pipeline networks not only results in considerableresource wastage but also leads to environmental pollution and ecological imbalance.In response to this global issue, a bioinspired superhydrophobic ...Liquid leakage of pipeline networks not only results in considerableresource wastage but also leads to environmental pollution and ecological imbalance.In response to this global issue, a bioinspired superhydrophobic thermoplastic polyurethane/carbon nanotubes/graphene nanosheets flexible strain sensor (TCGS) hasbeen developed using a combination of micro-extrusion compression molding andsurface modification for real-time wireless detection of liquid leakage. The TCGSutilizes the synergistic effects of Archimedean spiral crack arrays and micropores,which are inspired by the remarkable sensory capabilities of scorpions. This designachieves a sensitivity of 218.13 at a strain of 2%, which is an increase of 4300%. Additionally, it demonstrates exceptional durability bywithstanding over 5000 usage cycles. The robust superhydrophobicity of the TCGS significantly enhances sensitivity and stability indetecting small-scale liquid leakage, enabling precise monitoring of liquid leakage across a wide range of sizes, velocities, and compositionswhile issuing prompt alerts. This provides critical early warnings for both industrial pipelines and potential liquid leakage scenariosin everyday life. The development and utilization of bioinspired ultrasensitive flexible strain sensors offer an innovative and effectivesolution for the early wireless detection of liquid leakage.展开更多
Sensors are the source of information technology and the first unit of intelligent systems,providing real-world"data"for artificial intelligence.They play a crucial role in various aspects of the national ec...Sensors are the source of information technology and the first unit of intelligent systems,providing real-world"data"for artificial intelligence.They play a crucial role in various aspects of the national economy and the people's livelihood,such as national defense security and the development of new quality productive forces.This paper provides a comprehensive survey of how sensors should adapt to the current upsurge of artificial intelligence,analyzing their technical connotations,application characteristics,and inherent limitations.Furthermore,with a sensor-oriented mindset,it is proposed that sensors will dominate information technology,upgrade connotations,advance ubiquitous bionic intelligence and engage in a"symbiotic dance"with artificial intelligence.This overview provides a promising direction for the higher-level development of sensors and artificial intelligence.展开更多
The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language proc...The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.展开更多
This study presents a breakthrough in flexible strain sensor technology with the development of an ultrahigh sensitivity and wide-range sensor,addressing the critical challenge of reconciling sensitivity with measurem...This study presents a breakthrough in flexible strain sensor technology with the development of an ultrahigh sensitivity and wide-range sensor,addressing the critical challenge of reconciling sensitivity with measurement range.Inspired by the structure of bamboo slips,we introduce a novel approach that utilises liquid metal to modulate the electrical pathways within a cracked platinum fabric electrode.The resulting sensor demonstrates a gauge factor greater than 108 and a strain measurement capability exceeding 100%.The integration of patterned liquid metal enables customisable tuning of the sensor’s response,while the porous fabric structure ensures superior comfort and air permeability for the wearer.Our design not only optimises the sensor’s performance but also enhances the electrical stability that is essential for practical applications.Through systematic investigation,we reveal the intrinsic mechanisms governing the sensor’s response,offering valuable insights for the design of wearable strain sensors.The sensor’s exceptional performance across a spectrum of applications,from micro-strain to large-strain detection,highlights its potential for a wide range of real-world uses,demonstrating a significant advancement in the field of flexible electronics.展开更多
基金funded by the National Natural Science Foundation of China Instrumentation Program(52327806)Youth Fund of the National Nature Foundation of China(62405020)China Postdoctoral Science Foundation(2024M764131).
文摘Large-aperture optical components are of paramount importance in domains such as integrated circuits,photolithography,aerospace,and inertial confinement fusion.However,measuring their surface profiles relies predominantly on the phase-shifting approach,which involves collecting multiple interferograms and imposes stringent demands on environmental stability.These issues significantly hinder its ability to achieve real-time and dynamic high-precision measurements.Therefore,this study proposes a high-precision large-aperture single-frame interferometric surface profile measurement(LA-SFISPM)method based on deep learning and explores its capability to realize dynamic measurements with high accuracy.The interferogram is matched to the phase by training the data measured using the small aperture.The consistency of the surface features of the small and large apertures is enhanced via contrast learning and feature-distribution alignment.Hence,high-precision phase reconstruction of large-aperture optical components can be achieved without using a phase shifter.The experimental results show that for the tested mirror withΦ=820 mm,the surface profile obtained from LA-SFISPM is subtracted point-by-point from the ground truth,resulting in a maximum single-point error of 4.56 nm.Meanwhile,the peak-to-valley(PV)value is 0.0758λ,and the simple repeatability of root mean square(SR-RMS)value is 0.00025λ,which aligns well with the measured results obtained by ZYGO.In particular,a significant reduction in the measurement time(reduced by a factor of 48)is achieved compared with that of the traditional phase-shifting method.Our proposed method provides an efficient,rapid,and accurate method for obtaining the surface profiles of optical components with different diameters without employing a phase-shifting approach,which is highly desired in large-aperture interferometric measurement systems.
基金supported by the National Natural Science Foundation of China (Nos. 12172388 and 12472400)the Guangdong Basic and Applied Basic Research Foundation of China(No. 2025A1515011975)the Scientific Research Project of Guangdong Polytechnic Normal University of China (No. 2023SDKYA010)
文摘The elliptic integral method(EIM) is an efficient analytical approach for analyzing large deformations of elastic beams. However, it faces the following challenges.First, the existing EIM can only handle cases with known deformation modes. Second,the existing EIM is only applicable to Euler beams, and there is no EIM available for higher-precision Timoshenko and Reissner beams in cases where both force and moment are applied at the end. This paper proposes a general EIM for Reissner beams under arbitrary boundary conditions. On this basis, an analytical equation for determining the sign of the elliptic integral is provided. Based on the equation, we discover a class of elliptic integral piecewise points that are distinct from inflection points. More importantly, we propose an algorithm that automatically calculates the number of inflection points and other piecewise points during the nonlinear solution process, which is crucial for beams with unknown or changing deformation modes.
文摘With the intensifying competition in the integrated circuit(IC)industry,the high turnover rate of integrated circuit engineers has become a prominent issue affecting the technological continuity of high-precision,specialized,and innovative enterprises.As a representative of such enterprises,JL Technology has faced challenges to its R&D efficiency due to talent loss in recent years.This study takes this enterprise as a case to explore feasible paths to reduce turnover rates through optimizing training and career development systems.The research designs a method combining learning maps and talent maps,utilizes a competency model to clarify the direction for engineers’skill improvement,implements talent classification management using a nine-grid model,and achieves personalized training through Individual Development Plans(IDPs).Analysis of the enterprise’s historical data reveals that the main reasons for turnover are unclear career development paths and insufficient resources for skill improvement.After pilot implementation,the turnover rate in core departments decreased by 12%,and employee satisfaction with training increased by 24%.The results indicate that matching systematic talent reviews with dynamic learning resources can effectively enhance engineers’sense of belonging.This study provides a set of highly operational management tools for small and medium-sized high-precision,specialized,and innovative technology enterprises,verifies their applicability in such enterprises,and offers replicable experiences for similar enterprises to optimize their talent strategies[1].
基金supported by the Hubei University of Technology Graduate Research Innovation Project(No.2022048)the Natural Science Foundation Project of Hubei Province(grant No.2022CFB533)+1 种基金the Scientific Research Plan of Education Department of Hubei Province(grant No.D20222702)the Natural Science Project of Xiaogan city(grant No.XGKJ20210100014).
文摘Highly sensitive and stable acetylcholinesterase detection is critical for diagnosing and treating various neurotransmission-related diseases.In this study,a novel colorimetric-fluorescent dual-mode biosensor based on highly dispersive trimetal-modified graphite-phase carbon nitride nanocomposites for acetylcholinesterase detection was designed and synthesized by phosphorus doping and a mixed-metal MOF strategy.The specific surface area of trimetal-modified graphite-phase carbon nitride nanocomposites increased from 15.81 to 96.69 g m^(-2),and its thermal stability,interfacial charge transfer,and oxidation-reduction capability were enhanced compared with those of graphite-phase carbon nitride.First-principles density functional theory calculations and steady-state kinetic analysis are applied to investigate the electronic structures and efficient peroxidase-mimicking properties of trimetal-modified graphite-phase carbon nitride nanocomposites.The oxidation of 3,3',5,5'-tetramethylbenzidin was inhibited by thiocholine,which originates from the decomposition of thiocholine iodide by Acetyl-cholinesterase(AChE),resulting in changes in fluorescence and absorbance intensity.Due to the independence and complementarity of the signals,a highly precise colorimetric-fluorescent dual-mode biosensor with a linear range for detecting AChE of 4-20μU mL^(-1) and detection limits of 0.13μU mL^(-1)(colorimetric)and 0.04μU mL^(-1)(fluorescence)was developed.The spiking recovery of AChE in actual samples was 99.0%-100.4%.Therefore,a highly accurate,specific,and stable dual-mode biosensor is available for AChE detection,and this biosensor has the potential for the analysis of other biomarkers.
基金the National Natural Science Foundation of China (No.U22B2086)the National Science and Technology Major Project through (No.2019-VII-0017-0158).
文摘Shot peening is commonly employed for surface deformation strengthening of cylindrical surface part.Therefore,it is critical to understand the effects of shot peening on residual stress and surface topography.Compared to flat surface,cylindrical surface shot peening has two significant features:(i)the curvature of the cylindrical surface and the scattering of the shot stream cause dis-tributed impact velocities;(i)the rotation of the part results in a periodic variation of the impact velocity component.Therefore,it is a challenge to quickly and accurately predict the shot peening residual stress and surface topography of cylindrical surface.This paper developed a high-precision model which considers the more realistic shot peening process.Firstly,a kinematic analysis model was developed to simulate the relative movement of numerous shots and cylindrical surface.Then,the spatial distribution and time-varying impact information was calculated.Subsequently,the impact information was used for finite element modeling to predict residual stress and surface topography.The proposed kinematic analysis method was validated by comparison with the dis-crete element method.Meanwhile,9310 high strength steel rollers shot peening test verified the effectiveness of the model in predicting the residual stress and surface topography.In addition,the effects of air pressure and attack angle on the residual stress and surface topography were investigated.This work could provide a functional package for efficient prediction of the surface integrity and guide industrial application in cylindrical surface shot peening.
基金funded by the National Natural Science Foundation of China(52475580)the Special Foundation of the Taishan Scholar Project(tsqn202211077,tsqn202311077)+3 种基金Shandong Provincial Excellent Overseas Young Scholar Foundation(2023HWYQ-069)the Shandong Provincial Natural Science Foundation(ZR2023ME118,ZR2023QF080)the Natural Science Foundation of Qingdao City(23-2-1-219-zyyd-jch,23-2-1-111-zyyd-jch)the Fundamental Research Funds for the Central Universities(23CX06032A).
文摘The complex wiring,bulky data collection devices,and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices.To tackle these challenges,this work develops an artificial intelligenceassisted,wireless,flexible,and wearable mechanoluminescent strain sensor system(AIFWMLS)by integration of deep learning neural network-based color data processing system(CDPS)with a sandwich-structured flexible mechanoluminescent sensor(SFLC)film.The SFLC film shows remarkable and robust mechanoluminescent performance with a simple structure for easy fabrication.The CDPS system can rapidly and accurately extract and interpret the color of the SFLC film to strain values with auto-correction of errors caused by the varying color temperature,which significantly improves the accuracy of the predicted strain.A smart glove mechanoluminescent sensor system demonstrates the great potential of the AIFWMLS system in human gesture recognition.Moreover,the versatile SFLC film can also serve as a encryption device.The integration of deep learning neural network-based artificial intelligence and SFLC film provides a promising strategy to break the“color to strain value”bottleneck that hinders the practical application of flexible colorimetric strain sensors,which could promote the development of wearable and flexible strain sensors from laboratory research to consumer markets.
基金supported by the National Natural Science Foundation of China(No.62404111)Natural Science Foundation of Jiangsu Province(No.BK20240635)+2 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.24KJB510025)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY223157 and NY223156)Opening Project of Advanced Inte-grated Circuit Package and Testing Research Center of Jiangsu Province(No.NTIKFJJ202303).
文摘Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.
文摘Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.
基金supported by the National Natural Science Foundation of China(No.22376159)the Fundamental Research Funds for the Central Universities.
文摘Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,etc.With the development of sensor technology,wireless communication,smart monitoring terminal,cloud storage/computing technology,and artificial intelligence,smart gas sensors represent the future of gassensing due to their merits of real-time multifunctional monitoring,earlywarning function,and intelligent and automated feature.Various electronicand optoelectronic gas sensors have been developed for high-performancesmart gas analysis.With the development of smart terminals and the maturityof integrated technology,flexible and wearable gas sensors play an increasingrole in gas analysis.This review highlights recent advances of smart gassensors in diverse applications.The structural components and fundamentalprinciples of electronic and optoelectronic gas sensors are described,andflexible and wearable gas sensor devices are highlighted.Moreover,sensorarray with artificial intelligence algorithms and smart gas sensors in“Internet of Things”paradigm are introduced.Finally,the challengesand perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.
基金financially supported by the National Natural Science Foundation of China(Nos.52272160,U2330112,and 52002254)Sichuan Science and Technology Foundation(Nos.2020YJ0262,2021YFH0127,2022YFH0083,2022YFSY0045,and 2023YFSY0002)+1 种基金the Chunhui Plan of Ministry of Education,Fundamental Research Funds for the Central Universities,China(No.YJ201893)the Foundation of Key Laboratory of Lidar and Device,Sichuan Province,China(No.LLD2023-006)。
文摘Nowadays,force sensors play an important role in industrial production,electronic information,medical health,and many other fields.Two-dimensional material-based filed effect transistor(2D-FET)sensors are competitive with nano-level size,lower power consumption,and accurate response.However,few of them has the capability of impulse detection which is a path function,expressing the cumulative effect of the force on the particle over a period of time.Herein we fabricated the flexible polymethyl methacrylate(PMMA)gate dielectric MoS_(2)-FET for force and impulse sensor application.We systematically investigated the responses of the sensor to constant force and varying forces,and achieved the conversion factors of the drain current signals(I_(ds))to the detected impulse(I).The applied force was detected and recorded by I_(ds)with a low power consumption of~30 nW.The sensitivity of the device can reach~8000%and the 4×1 sensor array is able to detect and locate the normal force applied on it.Moreover,there was almost no performance loss for the device as left in the air for two months.
文摘In order to improve the reliability of the spacecraft micro cold gas propulsion system and realize the precise control of the spacecraft attitude and orbit, a micro-thrust, high-precision cold gas thruster is carried out, at the same time due to the design requirements of the spacecraft, this micro-thrust should be continuous working more than 60 minutes, the traditional solenoid valve used for the thrusts can’t complete the mission, so a long-life micro latching valve is developed as the control valve for this micro thruster, because the micro latching valve can keep its position when it cuts off the outage. Firstly, the authors introduced the design scheme and idea of the thruster. Secondly, the performance of the latching valve and the flow characteristics of the nozzle were simulated. Finally, from the experimental results and compared with the numerical study, it shows that the long-life micro cold gas thruster developed in this paper meets the mission requirements.
基金supported by the National Research Foundation(NRF)grant funded by the Korean government(MSIT)(RS-2023-00211580,RS-2023-00237308).
文摘Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds.Accurate data analysis is crucial for converting external stimuli from each artificial sense into user-relevant information,yet conventional signal processing methods struggle with the massive scale,noise,and artificial sensory systems characteristics of data generated by artificial sensory devices.Integrating artificial intelligence(AI)is essential for addressing these challenges and enhancing the performance of artificial sensory systems,making it a rapidly growing area of research in recent years.However,no studies have systematically categorized the output functions of these systems or analyzed the associated AI algorithms and data processing methods.In this review,we present a systematic overview of the latest AI techniques aimed at enhancing the cognitive capabilities of artificial sensory systems replicating the five human senses:touch,taste,vision,smell,and hearing.We categorize the AI-enabled capabilities of artificial sensory systems into four key areas:cognitive simulation,perceptual enhancement,adaptive adjustment,and early warning.We introduce specialized AI algorithms and raw data processing methods for each function,designed to enhance and optimize sensing performance.Finally,we offer a perspective on the future of AI-integrated artificial sensory systems,highlighting technical challenges and potential real-world application scenarios for further innovation.Integration of AI with artificial sensory systems will enable advanced multimodal perception,real-time learning,and predictive capabilities.This will drive precise environmental adaptation and personalized feedback,ultimately positioning these systems as foundational technologies in smart healthcare,agriculture,and automation.
基金financially supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA121503 and No.2012AA120603)the National Natural Science Foundation of China(No.61377012)the Tsinghua University Initiative Scientific Research Program(No.20131089242)。
文摘Earth sensors are widely used in spacecraft for attitude determination. They need to have a very large field of view(FOV)( > 120°) and relatively low accuracy while being used in the aircrafts around orbit. A triple-FOV infrared earth sensor is proposed in this paper. It uses three pieces of standard infrared detectors with a wavelength range of 14;16μm,to sense the horizontal circle by detecting the infrared light emitted from the earth. From which,the geocentric vector can be obtained. A mathematic model is established and a validation model is set up to provide input parameters for the mathematic model. The simulation results of the two models show that the output of the mathematic model coincides with the known parameters. Based on the above analysis,a prototype has been built and tested. The test results show that the angle measurement error is about 0. 002° and hence such a triple-FOV earth sensor is capable to provide high-precision position information for autonomous navigation.
基金supported by the National Natural Science Foundation of China(Grant No.12272369)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0620101).
文摘In recent decades,capacitive pressure sensors(CPSs)with high sensitivity have demonstrated significant potential in applications such as medical monitoring,artificial intelligence,and soft robotics.Efforts to enhance this sensitivity have predominantly focused on material design and structural optimization,with surface microstructures such as wrinkles,pyramids,and micro-pillars proving effective.Although finite element modeling(FEM)has guided enhancements in CPS sensitivity across various surface designs,a theoretical understanding of sensitivity improvements remains underexplored.This paper employs sinusoidal wavy surfaces as a representative model to analytically elucidate the underlying mechanisms of sensitivity enhancement through contact mechanics.These theoretical insights are corroborated by FEM and experimental validations.Our findings underscore that optimizing material properties,such as Young’s modulus and relative permittivity,alongside adjustments in surface roughness and substrate thickness,can significantly elevate the sensitivity.The optimal performance is achieved when the amplitude-to-wavelength ratio(H/)is about 0.2.These results offer critical insights for designing ultrasensitive CPS devices,paving the way for advancements in sensor technology.
基金the support from the National Natural Science Foundation of China(22272004,62272041)the Fundamental Research Funds for the Central Universities(YWF-22-L-1256)+1 种基金the National Key R&D Program of China(2023YFC3402600)the Beijing Institute of Technology Research Fund Program for Young Scholars(No.1870011182126)。
文摘The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an overwhelming tendency,providing powerful tools for remote health monitoring and personal health management.Among many candidates,two-dimensional(2D)materials stand out due to several exotic mechanical,electrical,optical,and chemical properties that can be efficiently integrated into atomic-thin films.While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene,the rapid development of new 2D materials with exotic properties has opened up novel applications,particularly in smart interaction and integrated functionalities.This review aims to consolidate recent progress,highlight the unique advantages of 2D materials,and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices.We begin with an in-depth analysis of the advantages,sensing mechanisms,and potential applications of 2D materials in wearable biodevice fabrication.Following this,we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body.Special attention is given to showcasing the integration of multi-functionality in 2D smart devices,mainly including self-power supply,integrated diagnosis/treatment,and human–machine interaction.Finally,the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of2D materials for advanced biodevices.
基金Funded by Cofoe Medical Technology Co.,Ltd and the Scientific Research Start-up Funds of Hexi University(No.KYQD2022006)。
文摘CuO nanoparticles were successfully synthesized via a two-jet electrospun method,and then screen-printed on silver-carbon electrodes,forming CuO-modified Ag-C(CuO/Ag-C)disposable strip electrodes.In natural environment condition for glucose detection,the obtained CuO/Ag-C electrodes show a high sensitivity of 540 nA·mM^(-1)·cm^(-2),and a low limit of detection(0.68 mM)in a wide linear response range of 0.68 mM and 3 mM(signal/noise=3),respectively.In addition,the CuO/Ag-C electrodes also exhibit excellent anti-interference,air stability and repeatability.As a result,the fabrication of CuO nanoparticles via an electrospun process and the technique of screen-printed electrodes are of great significance for glucose detection.
基金the National Natural Science Foundation of China(Grant No.52203037,52103031,and 52073107)the Natural Science Foundation of Hubei Province of China(Grant No.2022CFB649)the National Key Research and Development Program of China(Grant No.2022YFC3901902).
文摘Liquid leakage of pipeline networks not only results in considerableresource wastage but also leads to environmental pollution and ecological imbalance.In response to this global issue, a bioinspired superhydrophobic thermoplastic polyurethane/carbon nanotubes/graphene nanosheets flexible strain sensor (TCGS) hasbeen developed using a combination of micro-extrusion compression molding andsurface modification for real-time wireless detection of liquid leakage. The TCGSutilizes the synergistic effects of Archimedean spiral crack arrays and micropores,which are inspired by the remarkable sensory capabilities of scorpions. This designachieves a sensitivity of 218.13 at a strain of 2%, which is an increase of 4300%. Additionally, it demonstrates exceptional durability bywithstanding over 5000 usage cycles. The robust superhydrophobicity of the TCGS significantly enhances sensitivity and stability indetecting small-scale liquid leakage, enabling precise monitoring of liquid leakage across a wide range of sizes, velocities, and compositionswhile issuing prompt alerts. This provides critical early warnings for both industrial pipelines and potential liquid leakage scenariosin everyday life. The development and utilization of bioinspired ultrasensitive flexible strain sensors offer an innovative and effectivesolution for the early wireless detection of liquid leakage.
基金funded by National Natural Science Foundation of China(52175492)Pilot Project for the Establishment of Virtual Teaching and Research Offices in Beijing's Higher Education Institutions(Grant No.4313054 and 4313055)Beijing Undergraduate Teaching Reform and Innovation Project of Higher Education(Grant No.ZF211B2002 and ZF211B2405).
文摘Sensors are the source of information technology and the first unit of intelligent systems,providing real-world"data"for artificial intelligence.They play a crucial role in various aspects of the national economy and the people's livelihood,such as national defense security and the development of new quality productive forces.This paper provides a comprehensive survey of how sensors should adapt to the current upsurge of artificial intelligence,analyzing their technical connotations,application characteristics,and inherent limitations.Furthermore,with a sensor-oriented mindset,it is proposed that sensors will dominate information technology,upgrade connotations,advance ubiquitous bionic intelligence and engage in a"symbiotic dance"with artificial intelligence.This overview provides a promising direction for the higher-level development of sensors and artificial intelligence.
基金the National Research Foundation(NRF)Singapore mid-sized center grant(NRF-MSG-2023-0002)FrontierCRP grant(NRF-F-CRP-2024-0006)+2 种基金A*STAR Singapore MTC RIE2025 project(M24W1NS005)IAF-PP project(M23M5a0069)Ministry of Education(MOE)Singapore Tier 2 project(MOE-T2EP50220-0014).
文摘The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.
基金support from the National Key R&D Program of China(2021YFB3200700)the National Natural Science Foundation of China(Grant No.0214100221,51925503).
文摘This study presents a breakthrough in flexible strain sensor technology with the development of an ultrahigh sensitivity and wide-range sensor,addressing the critical challenge of reconciling sensitivity with measurement range.Inspired by the structure of bamboo slips,we introduce a novel approach that utilises liquid metal to modulate the electrical pathways within a cracked platinum fabric electrode.The resulting sensor demonstrates a gauge factor greater than 108 and a strain measurement capability exceeding 100%.The integration of patterned liquid metal enables customisable tuning of the sensor’s response,while the porous fabric structure ensures superior comfort and air permeability for the wearer.Our design not only optimises the sensor’s performance but also enhances the electrical stability that is essential for practical applications.Through systematic investigation,we reveal the intrinsic mechanisms governing the sensor’s response,offering valuable insights for the design of wearable strain sensors.The sensor’s exceptional performance across a spectrum of applications,from micro-strain to large-strain detection,highlights its potential for a wide range of real-world uses,demonstrating a significant advancement in the field of flexible electronics.
