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
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
Position sensitive device(PSD)sensor is a vital optical element that is mainly used in tracking systems for visible light communication(VLC).Recently,a new reconfigurable PSD architecture emerged.The proposed architec...Position sensitive device(PSD)sensor is a vital optical element that is mainly used in tracking systems for visible light communication(VLC).Recently,a new reconfigurable PSD architecture emerged.The proposed architecture makes the PSD perform more functions by modifying its architecture.As the PSD is mainly formed of an array of photodiodes.The primary concept involves employing transistors to alternate between the operating modes of the photodiodes(photoconductive and photovoltaic).Additionally,alternating among output pins can be done based on the required function.This paper presents the mathematical modeling and simulation of a reconfigurable-multifunctional optical sensor which can perform energy harvesting and data acquisition,as well as positioning,which is not available in the traditional PSDs.Simulation using the MATLAB software tool was achieved to demonstrate the modeling.The simulation results confirmed the validity of the mathematical modeling and proved that the modified sensor architecture,as depicted by the equations,accurately describes its behavior.The proposed sensor is expected to extend the battery's lifecycle,reduce its physical size,and increase the integration and functionality of the system.The presented sensor might be used in free space optical(FSO)communication like cube satellites or even in underwater wireless optical communication(UWOC).展开更多
This study aims to explore the feasibility of conducting supervised classification of road barriers in a practical context through the utilization of diverse data collection methods.These methods encompass acceleromet...This study aims to explore the feasibility of conducting supervised classification of road barriers in a practical context through the utilization of diverse data collection methods.These methods encompass accelerometer,ultrasonic,GPS,and real-time clock sensors,which collectively contribute to a comprehensive analysis of the subject matter.This study primarily focuses on the highways of India,along with urban and semi-urban areas,as its central subject of investigation.In order to facilitate the collection of data from these sensors,a mobile application referred to as DC has been meticulously developed.In this study,the data collected from the sensors undergo a transformation process to create a fuzzy dataset.This is achieved through the application of min-max normalization followed by fuzzification techniques.A variety of methodologies for measuring distance have been established,each aimed at achieving optimal classification outcomes.One of the primary objectives is to establish comprehensive standards for assessing the condition of roadways,considering a multitude of factors,including the overall length of the road and the extent of any damage present.This study conducts a comprehensive comparative analysis of all distance metrics employed for the classification of road impediments.The findings reveal promising results regarding accuracy,demonstrating an approximate range between 98%and 99%.Furthermore,to facilitate the observation of outcomes in real time,a visualization tool is currently under development.This tool aims to display road obstructions on maps,enhancing the user's ability to navigate and understand the current traffic conditions effectively.展开更多
To facilitate real-time monitoring and recording of humidity in the environment and to satisfy the requirement for strain performance in certain applications(such as wearable devices),this paper proposes an in-situ me...To facilitate real-time monitoring and recording of humidity in the environment and to satisfy the requirement for strain performance in certain applications(such as wearable devices),this paper proposes an in-situ method for synthesising Au nanoparticles on ZIF-67.In this study,an Au@ZIF-67 composite humidity-sensitive material was combined with flexible polyethylene terephthalate interdigitated electrodes to create an Au@ZIF-67 flexible humidity sensor.The prepared samples were characterised using X-ray diffraction,X-ray photoelectron spectroscopy,and transmission electron microscopy.The humidity-sensitive properties of the sensor were investigated,and its monitoring capabilities in applications involving respiration,gestures,skin,and baby diapers were tested.The experimental results indicate that compared with a pure ZIF-67 humidity sensor,the Au@ZIF-67(0.1Au@Z)flexible humidity sensor exhibits a 158.07%decrease in baseline resistance and a 51.66%increase in sensitivity to 95%relative humidity,and the hysteresis,response time,and recovery time are significantly reduced.Furthermore,the sensor exhibits excellent characteristics such as high resolution,repeatability,and stability.The obtained results regarding the material properties,humidity sensitivity,and practical application of non-contact humidity monitoring demonstrate that the prepared sensors exhibit excellent and comprehensive performance,indicating their broad prospects in wearable medical devices,wireless Internet of Things,humidity detection in complex environments,and intelligent integrated systems.展开更多
Over recent decades,carbon-based chemical sensor technologies have advanced significantly.Nevertheless,significant opportunities persist for enhancing analyte recognition capabilities,particularly in complex environme...Over recent decades,carbon-based chemical sensor technologies have advanced significantly.Nevertheless,significant opportunities persist for enhancing analyte recognition capabilities,particularly in complex environments.Conventional monovariable sensors exhibit inherent limitations,such as susceptibility to interference from coexisting analytes,which results in response overlap.Although sensor arrays,through modification of multiple sensing materials,offer a potential solution for analyte recognition,their practical applications are constrained by intricate material modification processes.In this context,multivariable chemical sensors have emerged as a promising alternative,enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition,while utilizing a single sensing material.Among various carbon-based materials,carbon nanotubes(CNTs)and graphene have emerged as ideal candidates for constructing high-performance chemical sensors,owing to their well-established batch fabrication processes,superior electrical properties,and outstanding sensing capabilities.This review examines the progress of carbon-based multivariable chemical sensors,focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition.The discussion encompasses fundamental aspects of these sensors,including sensing materials,sensor architectures,performance metrics,pattern recognition algorithms,and multivariable sensing mechanism.Furthermore,the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.展开更多
Rapid and robust identification of bacteria is crucial for environmental monitoring and clinical diagnosis.Herein,a bioinspired interface-mediated multichannel sensor array was developed based on three-coloremitting a...Rapid and robust identification of bacteria is crucial for environmental monitoring and clinical diagnosis.Herein,a bioinspired interface-mediated multichannel sensor array was developed based on three-coloremitting antimicrobial functional carbon dots(FCDs)and concanavalin A doped polydopamine nanoparticles(Con A-PDA)for identification of bacteria.In this sensor,the fluorescence intensity of the three FCDs was quenched by the Con A-PDA.Upon addition different types of bacteria,the fluorescence intensity of the three FCDs was restored or further quenched.Recur to statistical analysis methods,it is employed to accurately discriminate 10 types of bacteria(including three probiotics and seven pathogenic bacteria)in natural water samples and human urine samples.The discrimination ability of the sensor array was highly enhanced via different competing binding of the FCDs and the bacteria toward Con A-PDA.The proposed array-based method offers a rapid,high-throughput,and reliable sensing platform for pathogen diagnosis in the field of environmental monitoring and clinical diagnosis.展开更多
This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognit...This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognition methods that struggle to adapt in dynamic environments,as they typically rely on post-response mechanisms that fail to detect scene changes before users experience latency.The proposed framework leverages data from multiple smartphone sensors,including acceleration sensors,gyroscopes,magnetic field sensors,and orientation sensors,to identify different communication scenes,such as walking,running,cycling,and various modes of transportation.Extensive experimental comparative analysis with existing methods on the open-source SHL-2018 dataset confirmed the superior performance of our approach in terms of F1 score and processing speed.Additionally,tests using a Microsoft Surface Pro tablet and a self-collected Beijing-2023 dataset have validated the framework's efficiency and generalization capability.The results show that our framework achieved an F1 score of 95.15%on SHL-2018and 94.6%on Beijing-2023,highlighting its robustness across different datasets and conditions.Furthermore,the levels of computational complexity and power consumption associated with the algorithm are moderate,making it suitable for deployment on mobile devices.展开更多
Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commo...Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commonly used in various applications such as environmental monitoring,surveillance,healthcare,agriculture,and industrial automation.Despite the benefits of WSN,energy efficiency remains a challenging problem that needs to be addressed.Clustering and routing can be considered effective solutions to accomplish energy efficiency in WSNs.Recent studies have reported that metaheuristic algorithms can be applied to optimize cluster formation and routing decisions.This study introduces a new Northern Goshawk Optimization with boosted coati optimization algorithm for cluster-based routing(NGOBCO-CBR)method for WSN.The proposed NGOBCO-CBR method resolves the hot spot problem,uneven load balancing,and energy consumption in WSN.The NGOBCO-CBR technique comprises two major processes such as NGO based clustering and BCO-based routing.In the initial phase,the NGObased clustering method is designed for cluster head(CH)selection and cluster construction using five input variables such as residual energy(RE),node proximity,load balancing,network average energy,and distance to BS(DBS).Besides,the NGOBCO-CBR technique applies the BCO algorithm for the optimum selection of routes to BS.The experimental results of the NGOBCOCBR technique are studied under different scenarios,and the obtained results showcased the improved efficiency of the NGOBCO-CBR technique over recent approaches in terms of different measures.展开更多
Figure 6(a)in the paper[Chin.Phys.B 33074203(2024)]was incorrect due to editorial oversight.The correct figure is provided.This modification does not affect the result presented in the paper.
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE...Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.展开更多
Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectu...Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,etc.This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest path.Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives.To address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective framework.The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives.The search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing methods.The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness.The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions.These adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network conditions.The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation.The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques.The proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended network lifetime.Furthermore,analysis using p-values obtained from multiple performance measures(p-values<0.05)showed that the proposed approach outperforms with a high level of confidence.The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs.It allows stable performance in networks with 100 to 300 nodes,under varying node densities,and across different base station placements.Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use.展开更多
This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer mult...This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.展开更多
Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic ...Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.展开更多
Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this uniq...Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.展开更多
Chemical sensor arrays can obtain more comprehensive analyte information through high-dimensional data.It is of great significance in the analysis of multi-component complex samples.This review summarizes the developm...Chemical sensor arrays can obtain more comprehensive analyte information through high-dimensional data.It is of great significance in the analysis of multi-component complex samples.This review summarizes the development and status of chemical sensor arrays.We focused on the design of chemical sensor arrays based on various sensing materials.In addition,several pattern recognition methods in chemometrics are introduced.And applications of chemical sensor arrays in food monitoring,medical diagnosis,and environmental monitoring are illustrated.Based on the analysis of the limitations of current sensor array technology,the direction of the array is also predicted.This review aims to help the broad readership understand the research state of chemical sensor arrays and their development prospects.展开更多
Flexible and wearable pressure sensors hold immense promise for health monitoring,covering disease detection and postoperative rehabilitation.Developing pressure sensors with high sensitivity,wide detection range,and ...Flexible and wearable pressure sensors hold immense promise for health monitoring,covering disease detection and postoperative rehabilitation.Developing pressure sensors with high sensitivity,wide detection range,and cost-effectiveness is paramount.By leveraging paper for its sustainability,biocompatibility,and inherent porous structure,herein,a solution-processed all-paper resistive pressure sensor is designed with outstanding performance.A ternary composite paste,comprising a compressible 3D carbon skeleton,conductive polymer poly(3,4-ethylene dioxythiophene):poly(styrenesulfonate),and cohesive carbon nanotubes,is blade-coated on paper and naturally dried to form the porous composite electrode with hierachical micro-and nano-structured surface.Combined with screen-printed Cu electrodes in submillimeter finger widths on rough paper,this creates a multiscale hierarchical contact interface between electrodes,significantly enhancing sensitivity(1014 kPa-1)and expanding the detection range(up to 300 kPa)of as-resulted all-paper pressure sensor with low detection limit and power consumption.Its versatility ranges from subtle wrist pulses,robust finger taps,to large-area spatial force detection,highlighting its intricate submillimetermicrometer-nanometer hierarchical interface and nanometer porosity in the composite electrode.Ultimately,this all-paper resistive pressure sensor,with its superior sensing capabilities,large-scale fabrication potential,and cost-effectiveness,paves the way for next-generation wearable electronics,ushering in an era of advanced,sustainable technological solutions.展开更多
Photonic spin Hall effect(PSHE), as a novel physical effect in light–matter interaction, provides an effective metrological method for characterizing the tiny variation in refractive index(RI). In this work, we propo...Photonic spin Hall effect(PSHE), as a novel physical effect in light–matter interaction, provides an effective metrological method for characterizing the tiny variation in refractive index(RI). In this work, we propose a multi-functional PSHE sensor based on VO_(2), a material that can reveal the phase transition behavior. By applying thermal control, the mutual transformation into different phase states of VO_(2) can be realized, which contributes to the flexible switching between multiple RI sensing tasks. When VO_(2) is insulating, the ultrasensitive detection of glucose concentrations in human blood is achieved. When VO_(2) is in a mixed phase, the structure can be designed to distinguish between the normal cells and cancer cells through no-label and real-time monitoring. When VO_(2) is metallic, the proposed PSHE sensor can act as an RI indicator for gas analytes. Compared with other multi-functional sensing devices with the complex structures, our design consists of only one analyte and two VO_(2) layers, which is very simple and elegant. Therefore, the proposed VO_(2)-based PSHE sensor has outstanding advantages such as small size, high sensitivity, no-label, and real-time detection, providing a new approach for investigating tunable multi-functional sensors.展开更多
Conformal thin-film sensors enable precise monitoring of the operating conditions of components in extreme environments.However,the development of these sensors encounters major challenges,especially in uniformly appl...Conformal thin-film sensors enable precise monitoring of the operating conditions of components in extreme environments.However,the development of these sensors encounters major challenges,especially in uniformly applying multiple film layers on complex metallic surfaces and accurately capturing diverse operational parameters.This work reports a multi-sensor design and multi-layer additive manufacturing process targeting spherical metallic substrates.The proposed high-temperature dip-coating and self-leveling fabrication process achieves high-temperature thin-film coatings with excellent uniformity,high-temperature electrical insulation,and adhesion properties.The fabricated Ag/Pt thin film thermocouple arrays and a heat flux sensor exhibit a maximum temperature resistance of up to 960℃,with thermoelectric potential outputs and hightemperature resistance closely mirroring those of wire-based Ag/Pt thermocouples.Harsh environmental testing was conducted using high-power lasers and a flame gun.The results show that the array of thin-film conformal thermocouples more accurately reflected temperature changes at different points on a spherical surface.The heat flux sensors achieve responses within 95 ms and with-stand environments with heat fluxes over 1.2 MW/m^(2).The proposed multi-sensor design and fabrication method offers promising monitoring applications in harsh environments,including aerospace and nuclear power.展开更多
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.
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.
文摘Position sensitive device(PSD)sensor is a vital optical element that is mainly used in tracking systems for visible light communication(VLC).Recently,a new reconfigurable PSD architecture emerged.The proposed architecture makes the PSD perform more functions by modifying its architecture.As the PSD is mainly formed of an array of photodiodes.The primary concept involves employing transistors to alternate between the operating modes of the photodiodes(photoconductive and photovoltaic).Additionally,alternating among output pins can be done based on the required function.This paper presents the mathematical modeling and simulation of a reconfigurable-multifunctional optical sensor which can perform energy harvesting and data acquisition,as well as positioning,which is not available in the traditional PSDs.Simulation using the MATLAB software tool was achieved to demonstrate the modeling.The simulation results confirmed the validity of the mathematical modeling and proved that the modified sensor architecture,as depicted by the equations,accurately describes its behavior.The proposed sensor is expected to extend the battery's lifecycle,reduce its physical size,and increase the integration and functionality of the system.The presented sensor might be used in free space optical(FSO)communication like cube satellites or even in underwater wireless optical communication(UWOC).
文摘This study aims to explore the feasibility of conducting supervised classification of road barriers in a practical context through the utilization of diverse data collection methods.These methods encompass accelerometer,ultrasonic,GPS,and real-time clock sensors,which collectively contribute to a comprehensive analysis of the subject matter.This study primarily focuses on the highways of India,along with urban and semi-urban areas,as its central subject of investigation.In order to facilitate the collection of data from these sensors,a mobile application referred to as DC has been meticulously developed.In this study,the data collected from the sensors undergo a transformation process to create a fuzzy dataset.This is achieved through the application of min-max normalization followed by fuzzification techniques.A variety of methodologies for measuring distance have been established,each aimed at achieving optimal classification outcomes.One of the primary objectives is to establish comprehensive standards for assessing the condition of roadways,considering a multitude of factors,including the overall length of the road and the extent of any damage present.This study conducts a comprehensive comparative analysis of all distance metrics employed for the classification of road impediments.The findings reveal promising results regarding accuracy,demonstrating an approximate range between 98%and 99%.Furthermore,to facilitate the observation of outcomes in real time,a visualization tool is currently under development.This tool aims to display road obstructions on maps,enhancing the user's ability to navigate and understand the current traffic conditions effectively.
基金supported by the Natural Science Project of Zhengzhou Science and Technology Bureau(No.21ZZXTCX12)the Key Research and Development Program of Henan Province(No.221111220300)+1 种基金the Key Program of the National Natural Science Foundation of China(No.62333013)the Youth Backbone Teacher Training Program of Henan University of Technology(No.21420154).
文摘To facilitate real-time monitoring and recording of humidity in the environment and to satisfy the requirement for strain performance in certain applications(such as wearable devices),this paper proposes an in-situ method for synthesising Au nanoparticles on ZIF-67.In this study,an Au@ZIF-67 composite humidity-sensitive material was combined with flexible polyethylene terephthalate interdigitated electrodes to create an Au@ZIF-67 flexible humidity sensor.The prepared samples were characterised using X-ray diffraction,X-ray photoelectron spectroscopy,and transmission electron microscopy.The humidity-sensitive properties of the sensor were investigated,and its monitoring capabilities in applications involving respiration,gestures,skin,and baby diapers were tested.The experimental results indicate that compared with a pure ZIF-67 humidity sensor,the Au@ZIF-67(0.1Au@Z)flexible humidity sensor exhibits a 158.07%decrease in baseline resistance and a 51.66%increase in sensitivity to 95%relative humidity,and the hysteresis,response time,and recovery time are significantly reduced.Furthermore,the sensor exhibits excellent characteristics such as high resolution,repeatability,and stability.The obtained results regarding the material properties,humidity sensitivity,and practical application of non-contact humidity monitoring demonstrate that the prepared sensors exhibit excellent and comprehensive performance,indicating their broad prospects in wearable medical devices,wireless Internet of Things,humidity detection in complex environments,and intelligent integrated systems.
基金supported by National Natural Science Foundation of China(92263109,52305607 and 61904188)the Shanghai Rising-Star Program(22QA1410400)+1 种基金the Natural Science Foundation of Shanghai(23ZR1472200)the Medical Innovation Research Program of Shanghai Science and Technology Innovation Action Plan(Grant No.24DX2800100)。
文摘Over recent decades,carbon-based chemical sensor technologies have advanced significantly.Nevertheless,significant opportunities persist for enhancing analyte recognition capabilities,particularly in complex environments.Conventional monovariable sensors exhibit inherent limitations,such as susceptibility to interference from coexisting analytes,which results in response overlap.Although sensor arrays,through modification of multiple sensing materials,offer a potential solution for analyte recognition,their practical applications are constrained by intricate material modification processes.In this context,multivariable chemical sensors have emerged as a promising alternative,enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition,while utilizing a single sensing material.Among various carbon-based materials,carbon nanotubes(CNTs)and graphene have emerged as ideal candidates for constructing high-performance chemical sensors,owing to their well-established batch fabrication processes,superior electrical properties,and outstanding sensing capabilities.This review examines the progress of carbon-based multivariable chemical sensors,focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition.The discussion encompasses fundamental aspects of these sensors,including sensing materials,sensor architectures,performance metrics,pattern recognition algorithms,and multivariable sensing mechanism.Furthermore,the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.
基金supported by National Natural Science Foundation of China(Nos.22376057,22174048,22274048,22274045,22274047,and 21904039)the Foundation of the Science&Technology Department of Hunan Province(Nos.2023JJ30394 and2023ZJ1123)。
文摘Rapid and robust identification of bacteria is crucial for environmental monitoring and clinical diagnosis.Herein,a bioinspired interface-mediated multichannel sensor array was developed based on three-coloremitting antimicrobial functional carbon dots(FCDs)and concanavalin A doped polydopamine nanoparticles(Con A-PDA)for identification of bacteria.In this sensor,the fluorescence intensity of the three FCDs was quenched by the Con A-PDA.Upon addition different types of bacteria,the fluorescence intensity of the three FCDs was restored or further quenched.Recur to statistical analysis methods,it is employed to accurately discriminate 10 types of bacteria(including three probiotics and seven pathogenic bacteria)in natural water samples and human urine samples.The discrimination ability of the sensor array was highly enhanced via different competing binding of the FCDs and the bacteria toward Con A-PDA.The proposed array-based method offers a rapid,high-throughput,and reliable sensing platform for pathogen diagnosis in the field of environmental monitoring and clinical diagnosis.
基金supported by National 2011 Collaborative Innovation Center of Wireless Communication Technologies under Grant 2242022k60006。
文摘This paper presents a comprehensive framework that enables communication scene recognition through deep learning and multi-sensor fusion.This study aims to address the challenge of current communication scene recognition methods that struggle to adapt in dynamic environments,as they typically rely on post-response mechanisms that fail to detect scene changes before users experience latency.The proposed framework leverages data from multiple smartphone sensors,including acceleration sensors,gyroscopes,magnetic field sensors,and orientation sensors,to identify different communication scenes,such as walking,running,cycling,and various modes of transportation.Extensive experimental comparative analysis with existing methods on the open-source SHL-2018 dataset confirmed the superior performance of our approach in terms of F1 score and processing speed.Additionally,tests using a Microsoft Surface Pro tablet and a self-collected Beijing-2023 dataset have validated the framework's efficiency and generalization capability.The results show that our framework achieved an F1 score of 95.15%on SHL-2018and 94.6%on Beijing-2023,highlighting its robustness across different datasets and conditions.Furthermore,the levels of computational complexity and power consumption associated with the algorithm are moderate,making it suitable for deployment on mobile devices.
文摘Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commonly used in various applications such as environmental monitoring,surveillance,healthcare,agriculture,and industrial automation.Despite the benefits of WSN,energy efficiency remains a challenging problem that needs to be addressed.Clustering and routing can be considered effective solutions to accomplish energy efficiency in WSNs.Recent studies have reported that metaheuristic algorithms can be applied to optimize cluster formation and routing decisions.This study introduces a new Northern Goshawk Optimization with boosted coati optimization algorithm for cluster-based routing(NGOBCO-CBR)method for WSN.The proposed NGOBCO-CBR method resolves the hot spot problem,uneven load balancing,and energy consumption in WSN.The NGOBCO-CBR technique comprises two major processes such as NGO based clustering and BCO-based routing.In the initial phase,the NGObased clustering method is designed for cluster head(CH)selection and cluster construction using five input variables such as residual energy(RE),node proximity,load balancing,network average energy,and distance to BS(DBS).Besides,the NGOBCO-CBR technique applies the BCO algorithm for the optimum selection of routes to BS.The experimental results of the NGOBCOCBR technique are studied under different scenarios,and the obtained results showcased the improved efficiency of the NGOBCO-CBR technique over recent approaches in terms of different measures.
文摘Figure 6(a)in the paper[Chin.Phys.B 33074203(2024)]was incorrect due to editorial oversight.The correct figure is provided.This modification does not affect the result presented in the paper.
基金supported by the National Key Research and Development Program of China(2023YFB3307800)National Natural Science Foundation of China(62394343,62373155)+2 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26)Fundamental Research Funds for the Central Universities.
文摘Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2023-2-02038).
文摘Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,etc.This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest path.Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives.To address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective framework.The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives.The search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing methods.The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness.The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions.These adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network conditions.The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation.The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques.The proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended network lifetime.Furthermore,analysis using p-values obtained from multiple performance measures(p-values<0.05)showed that the proposed approach outperforms with a high level of confidence.The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs.It allows stable performance in networks with 100 to 300 nodes,under varying node densities,and across different base station placements.Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use.
基金supported by the National Natural Science Foundation of China (Nos. 62276204, 62203343)。
文摘This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.
基金supported by the National Natural Science Foundation of China under Grant Nos.U21A20464,62066005Innovation Project of Guangxi Graduate Education under Grant No.YCSW2024313.
文摘Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.
基金the National Natural Science Foundation of China(Grant No.52072041)the Beijing Natural Science Foundation(Grant No.JQ21007)+2 种基金the University of Chinese Academy of Sciences(Grant No.Y8540XX2D2)the Robotics Rhino-Bird Focused Research Project(No.2020-01-002)the Tencent Robotics X Laboratory.
文摘Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.
基金funded by Natural Science Foundation of Heilongjiang Province(No.LH2022B004)Fundamental Research Funds for the Central Universities(No.2572022DJ01)+1 种基金111 Project(No.B20088)Heilongjiang Touyan Innovation Team Program(Tree Genetics and Breeding Innovation Team)。
文摘Chemical sensor arrays can obtain more comprehensive analyte information through high-dimensional data.It is of great significance in the analysis of multi-component complex samples.This review summarizes the development and status of chemical sensor arrays.We focused on the design of chemical sensor arrays based on various sensing materials.In addition,several pattern recognition methods in chemometrics are introduced.And applications of chemical sensor arrays in food monitoring,medical diagnosis,and environmental monitoring are illustrated.Based on the analysis of the limitations of current sensor array technology,the direction of the array is also predicted.This review aims to help the broad readership understand the research state of chemical sensor arrays and their development prospects.
基金support by the Science Fund of Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai(AMGM2021A03)the"Special Lubrication and Sealing for Aerospace"Shaanxi Provincial Science and Technology Innovation Team(2024RS-CXTD-63)+1 种基金the Xianyang2023 Key Research and Development Plan(L2023-ZDYF-QYCX-009)the World First Class University and First Class Academic Discipline Construction Funding 2023(0604024GH0201332,0604024SH0201332).
文摘Flexible and wearable pressure sensors hold immense promise for health monitoring,covering disease detection and postoperative rehabilitation.Developing pressure sensors with high sensitivity,wide detection range,and cost-effectiveness is paramount.By leveraging paper for its sustainability,biocompatibility,and inherent porous structure,herein,a solution-processed all-paper resistive pressure sensor is designed with outstanding performance.A ternary composite paste,comprising a compressible 3D carbon skeleton,conductive polymer poly(3,4-ethylene dioxythiophene):poly(styrenesulfonate),and cohesive carbon nanotubes,is blade-coated on paper and naturally dried to form the porous composite electrode with hierachical micro-and nano-structured surface.Combined with screen-printed Cu electrodes in submillimeter finger widths on rough paper,this creates a multiscale hierarchical contact interface between electrodes,significantly enhancing sensitivity(1014 kPa-1)and expanding the detection range(up to 300 kPa)of as-resulted all-paper pressure sensor with low detection limit and power consumption.Its versatility ranges from subtle wrist pulses,robust finger taps,to large-area spatial force detection,highlighting its intricate submillimetermicrometer-nanometer hierarchical interface and nanometer porosity in the composite electrode.Ultimately,this all-paper resistive pressure sensor,with its superior sensing capabilities,large-scale fabrication potential,and cost-effectiveness,paves the way for next-generation wearable electronics,ushering in an era of advanced,sustainable technological solutions.
基金Project supported by the National Natural Science Foundation of China(Grant No.NSFC 12175107)the Natural Science Foundation of Nanjing Vocational University of Industry Technology,China(Grant No.YK22-02-08)+3 种基金the Qing Lan Project of Jiangsu Province,Chinathe Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX23_0964)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20230347)the Fund from the Research Center of Industrial Perception and Intelligent Manufacturing Equipment Engineering of Jiangsu Province,China(Grant No.ZK21-05-09)。
文摘Photonic spin Hall effect(PSHE), as a novel physical effect in light–matter interaction, provides an effective metrological method for characterizing the tiny variation in refractive index(RI). In this work, we propose a multi-functional PSHE sensor based on VO_(2), a material that can reveal the phase transition behavior. By applying thermal control, the mutual transformation into different phase states of VO_(2) can be realized, which contributes to the flexible switching between multiple RI sensing tasks. When VO_(2) is insulating, the ultrasensitive detection of glucose concentrations in human blood is achieved. When VO_(2) is in a mixed phase, the structure can be designed to distinguish between the normal cells and cancer cells through no-label and real-time monitoring. When VO_(2) is metallic, the proposed PSHE sensor can act as an RI indicator for gas analytes. Compared with other multi-functional sensing devices with the complex structures, our design consists of only one analyte and two VO_(2) layers, which is very simple and elegant. Therefore, the proposed VO_(2)-based PSHE sensor has outstanding advantages such as small size, high sensitivity, no-label, and real-time detection, providing a new approach for investigating tunable multi-functional sensors.
基金supported by the National Key Research and Development Program of China(No.2022YFB3203900)。
文摘Conformal thin-film sensors enable precise monitoring of the operating conditions of components in extreme environments.However,the development of these sensors encounters major challenges,especially in uniformly applying multiple film layers on complex metallic surfaces and accurately capturing diverse operational parameters.This work reports a multi-sensor design and multi-layer additive manufacturing process targeting spherical metallic substrates.The proposed high-temperature dip-coating and self-leveling fabrication process achieves high-temperature thin-film coatings with excellent uniformity,high-temperature electrical insulation,and adhesion properties.The fabricated Ag/Pt thin film thermocouple arrays and a heat flux sensor exhibit a maximum temperature resistance of up to 960℃,with thermoelectric potential outputs and hightemperature resistance closely mirroring those of wire-based Ag/Pt thermocouples.Harsh environmental testing was conducted using high-power lasers and a flame gun.The results show that the array of thin-film conformal thermocouples more accurately reflected temperature changes at different points on a spherical surface.The heat flux sensors achieve responses within 95 ms and with-stand environments with heat fluxes over 1.2 MW/m^(2).The proposed multi-sensor design and fabrication method offers promising monitoring applications in harsh environments,including aerospace and nuclear power.
