This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors.Data collection and behavioral analysis are achieved using m...This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors.Data collection and behavioral analysis are achieved using machine learning(ML)algorithms through accelerometer sensors.However,behavioral analysis poses challenges due to the complexity of cow activities.The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints.Shorter windows may lack sufficient information,reducing algorithm performance.Additionally,the sensor’s position on the cowsmay shift during practical use,altering the collected accelerometer data.This study addresses these challenges by employing a 3-s data window to analyze cow behaviors,specifically Feeding,Lying,Standing,and Walking.Data synchronization between accelerometer sensors placed on the neck and leg compensates for the lack of information in short data windows.Features such as the Vector of Dynamic Body Acceleration(VeDBA),Mean,Variance,and Kurtosis are utilized alongside the Decision Tree(DT)algorithm to address energy efficiency and ensure computational effectiveness.This study also evaluates the impact of sensor misalignment on behavior classification.Simulated datasets with varying levels of sensor misalignment were created,and the system’s classification accuracy exceeded 0.95 for the four behaviors across all datasets(including original and simulated misalignment datasets).Sensitivity(Sen)and PPV for all datasets were above 0.9.The study provides farmers and the dairy industry with a practical,energy-efficient system for continuously monitoring cattle behavior to enhance herd productivity while reducing labor costs.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
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
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
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
Detection of target analytes at low concentrations is significant in various fields,including pharmaceuticals,healthcare,and environmental protection.Theophylline(TP),a natural alkaloid used as a bronchodilator to tre...Detection of target analytes at low concentrations is significant in various fields,including pharmaceuticals,healthcare,and environmental protection.Theophylline(TP),a natural alkaloid used as a bronchodilator to treat respiratory disorders such as asthma,bronchitis,and emphysema,has a narrow therapeutic window with a safe plasma concentration ranging from 55.5-111.0μmol·L^(-1)in adults.Accurate monitoring of TP levels is essential because too low or too high can cause se-rious side effects.In this regard,non-enzymatic electrochemical sensors offer a practical solution with rapidity,portability,and high sensitivity.This article aims to provide a comprehensive review of the recent developments of non-enzymatic electrochemical sensors for TP detection,highlighting the basic principles,electro-oxidation mechanisms,catalytic effects,and the role of modifying materials on electrode performance.Carbon-based electrodes such as glassy carbon electrodes(GCEs),carbon paste electrodes(CPEs),and carbon screen-printed electrodes(SPCEs)have become the primary choices for non-enzymatic sensors due to their chemical stability,low cost,and flexibility in modification.This article identifies the sig-nificant contribution of various modifying materials,including nanomaterials such as carbon nanotubes(CNTs),graphene,metal oxides,and multi-element nanocomposites.These modifications enhance sensors’electron transfer,sensitivity,and selectivity in detecting TP at low concentrations in complex media such as blood plasma and pharmaceutical samples.The electro-oxidation mechanism of TP is also discussed in depth,emphasizing the hydroxyl and carbonyl reaction pathways strongly influenced by pH and electrode materials.These mechanisms guide the selection of the appropriate electrode ma-terial for a particular application.The main contribution of this article is to identify superior modifying materials that can improve the performance of non-enzymatic electrochemical sensors.In a recent study,the combination of multi-element nanocomposites based on titanium dioxide(TiO_(2)),CNTs,and gold nanoparticles(AuNPs)resulted in the lowest detection limit of 3×10^(-5)μmol·L^(-1),reflecting the great potential of these materials for developing high-performance electrochemical sensors.The main conclusion of this article is the importance of a multidisciplinary approach in electrode material design to support the sensitivity and selectivity of TP detection.In addition,there is still a research gap in understanding TP’s more detailed oxidation mechanism,especially under pH variations and complex environments.Therefore,further research on electrode modification and analysis of the TP oxidation mechanism are urgently needed to improve the accuracy and sta-bility of the sensor while expanding its applications in pharmaceutical monitoring and medical diagnostics.By integrating various innovative materials and technical approaches,this review is expected to be an essential reference for developing efficient and affordable non-enzymatic electrochemical sensors.展开更多
Silicon resonant pressure sensors are widely used in aerospace and industry due to their high accuracy,good linearity,and long-term stability.Due to the stress and signal processing limitation,its accuracy is difficul...Silicon resonant pressure sensors are widely used in aerospace and industry due to their high accuracy,good linearity,and long-term stability.Due to the stress and signal processing limitation,its accuracy is difficult to meet the application requirements,and we can only search methods to approach this continuously.This research introduces a stress-isolated structure and a processing method of frequency signal to enhance the index.The stress-isolated structure separates the press-sensor from the printed circuit board by nested bonding for plates with silica gel.It minimizes the transmitted stress but no affect on the electrical connection.The analysis algorithm of frequency based on a given waveform number instead of a given time improves the analytical accuracy of frequency and pressure.By redesigning the interrupt timing control strategy in Microcontroller unit,it addresses sudden jumps of frequency signal.The proposed technique compared to similar schemes,tested on a resonant pressure sensor at a temperature condition of-45℃to 75℃and barometric condition of 3.5 kPa to 110 kPa,reduces the effect of stress effectively,and enhances to 0.01Hz in accuracy of frequency.More importantly there is no sudden jump in frequency signal.The pressure accuracy is increased by 3.5 times to 0.0145%after calibrating.Over-all,the advised method meets the requirement of high accuracy and promotes applications in the aerospace field.展开更多
基金funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under grant number:02/2022/TN.
文摘This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors.Data collection and behavioral analysis are achieved using machine learning(ML)algorithms through accelerometer sensors.However,behavioral analysis poses challenges due to the complexity of cow activities.The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints.Shorter windows may lack sufficient information,reducing algorithm performance.Additionally,the sensor’s position on the cowsmay shift during practical use,altering the collected accelerometer data.This study addresses these challenges by employing a 3-s data window to analyze cow behaviors,specifically Feeding,Lying,Standing,and Walking.Data synchronization between accelerometer sensors placed on the neck and leg compensates for the lack of information in short data windows.Features such as the Vector of Dynamic Body Acceleration(VeDBA),Mean,Variance,and Kurtosis are utilized alongside the Decision Tree(DT)algorithm to address energy efficiency and ensure computational effectiveness.This study also evaluates the impact of sensor misalignment on behavior classification.Simulated datasets with varying levels of sensor misalignment were created,and the system’s classification accuracy exceeded 0.95 for the four behaviors across all datasets(including original and simulated misalignment datasets).Sensitivity(Sen)and PPV for all datasets were above 0.9.The study provides farmers and the dairy industry with a practical,energy-efficient system for continuously monitoring cattle behavior to enhance herd productivity while reducing labor costs.
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.
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.
基金the funding from Lembaga Penelitian dan Pengabdian Masyarakat(LPPM)Universitas Indonesia,by Riset Kolaborasi Indonesia(RKI)-World Class University(WCU)Program with grant number NKB-1067/UN2-RST/HKP.05.00/2023 and NKB-781/UN2.RST/HKP.05.00/2024.
文摘Detection of target analytes at low concentrations is significant in various fields,including pharmaceuticals,healthcare,and environmental protection.Theophylline(TP),a natural alkaloid used as a bronchodilator to treat respiratory disorders such as asthma,bronchitis,and emphysema,has a narrow therapeutic window with a safe plasma concentration ranging from 55.5-111.0μmol·L^(-1)in adults.Accurate monitoring of TP levels is essential because too low or too high can cause se-rious side effects.In this regard,non-enzymatic electrochemical sensors offer a practical solution with rapidity,portability,and high sensitivity.This article aims to provide a comprehensive review of the recent developments of non-enzymatic electrochemical sensors for TP detection,highlighting the basic principles,electro-oxidation mechanisms,catalytic effects,and the role of modifying materials on electrode performance.Carbon-based electrodes such as glassy carbon electrodes(GCEs),carbon paste electrodes(CPEs),and carbon screen-printed electrodes(SPCEs)have become the primary choices for non-enzymatic sensors due to their chemical stability,low cost,and flexibility in modification.This article identifies the sig-nificant contribution of various modifying materials,including nanomaterials such as carbon nanotubes(CNTs),graphene,metal oxides,and multi-element nanocomposites.These modifications enhance sensors’electron transfer,sensitivity,and selectivity in detecting TP at low concentrations in complex media such as blood plasma and pharmaceutical samples.The electro-oxidation mechanism of TP is also discussed in depth,emphasizing the hydroxyl and carbonyl reaction pathways strongly influenced by pH and electrode materials.These mechanisms guide the selection of the appropriate electrode ma-terial for a particular application.The main contribution of this article is to identify superior modifying materials that can improve the performance of non-enzymatic electrochemical sensors.In a recent study,the combination of multi-element nanocomposites based on titanium dioxide(TiO_(2)),CNTs,and gold nanoparticles(AuNPs)resulted in the lowest detection limit of 3×10^(-5)μmol·L^(-1),reflecting the great potential of these materials for developing high-performance electrochemical sensors.The main conclusion of this article is the importance of a multidisciplinary approach in electrode material design to support the sensitivity and selectivity of TP detection.In addition,there is still a research gap in understanding TP’s more detailed oxidation mechanism,especially under pH variations and complex environments.Therefore,further research on electrode modification and analysis of the TP oxidation mechanism are urgently needed to improve the accuracy and sta-bility of the sensor while expanding its applications in pharmaceutical monitoring and medical diagnostics.By integrating various innovative materials and technical approaches,this review is expected to be an essential reference for developing efficient and affordable non-enzymatic electrochemical sensors.
文摘Silicon resonant pressure sensors are widely used in aerospace and industry due to their high accuracy,good linearity,and long-term stability.Due to the stress and signal processing limitation,its accuracy is difficult to meet the application requirements,and we can only search methods to approach this continuously.This research introduces a stress-isolated structure and a processing method of frequency signal to enhance the index.The stress-isolated structure separates the press-sensor from the printed circuit board by nested bonding for plates with silica gel.It minimizes the transmitted stress but no affect on the electrical connection.The analysis algorithm of frequency based on a given waveform number instead of a given time improves the analytical accuracy of frequency and pressure.By redesigning the interrupt timing control strategy in Microcontroller unit,it addresses sudden jumps of frequency signal.The proposed technique compared to similar schemes,tested on a resonant pressure sensor at a temperature condition of-45℃to 75℃and barometric condition of 3.5 kPa to 110 kPa,reduces the effect of stress effectively,and enhances to 0.01Hz in accuracy of frequency.More importantly there is no sudden jump in frequency signal.The pressure accuracy is increased by 3.5 times to 0.0145%after calibrating.Over-all,the advised method meets the requirement of high accuracy and promotes applications in the aerospace field.
