The rapid growth of wearable technology and artificial intelligence has driven a transition from traditional rigid electronic components to more comfortable and adaptable textile-based material integrations. Among tho...
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The rapid growth of wearable technology and artificial intelligence has driven a transition from traditional rigid electronic components to more comfortable and adaptable textile-based material integrations. Among those, flexible piezoresistive pressure sensors have garnered significant attention due to their simple structure, high sensitivity, ease of fabrication, and wide detection range. Textile-based interdigitated electrode (T-IDE) sensors, with their multi-scale material composition and multidimensional structural tunability, provide an ideal solution for achieving high sensitivity, excellent flexibility, and wearable piezoresistive sensorintegration. However, challenges remain in ensuring the stable adhesion of highly conductive electrodes on textile substrates while maintaining durability and repeatability in performance. This review systematically examines T-IDE piezoresistive sensors, covering electrode and substrate materials, fabrication techniques, multidimensional integration strategies, and their applications in smart wearables, health monitoring, electronic skin, and human-machine interaction. Finally, the review discusses fabrication techniques for optimizing electrode performance and manufacturing efficiency, as well as the future directions and prospects for sensor development. This comprehensive overview aims to provide an in-depth understanding of current innovations and challenges, fostering the advancement of flexible and intelligent wearable sensing technologies.
Biochar (BC), often obtained via thermochemical conversion methods of biomass, has emerged as a versatile material with significant potential in electrochemical sensing applications. This review critically examines th...
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Biochar (BC), often obtained via thermochemical conversion methods of biomass, has emerged as a versatile material with significant potential in electrochemical sensing applications. This review critically examines the recent advancements in the development of BC-based sensors for the electrochemical determination of pharmaceuticals, pesticides, heavy metals, phenolic compounds, and microplastics. BC-based electrochemical sensors have emerged as a promising alternative due to their sustainability, cost-effectiveness, and excellent electrochemical properties. The unique physicochemical properties of BC, including its high surface area, porosity, and functional groups, contribute to its effectiveness as a sensor material. The review begins with an overview of the synthesis methods for BC, highlighting the activation strategies on its structural and electrochemical properties. Next, the functionalization of BC and its integration into electrochemical sensor platforms are explored. The performance of BC-based sensors is evaluated using electrochemical focusing on their sensitivity, selectivity, detection limits, and stability. Future directions for research are proposed, emphasizing the need for further optimization, miniaturization, and integration of BC-based sensors into portable and on-site analytical devices.
Children with Autism Spectrum Disorder (ASD) often face unique risks during sports activities due to challenges such as motor coordination difficulties, sensory sensitivities, and communication impairments. This paper...
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Children with Autism Spectrum Disorder (ASD) often face unique risks during sports activities due to challenges such as motor coordination difficulties, sensory sensitivities, and communication impairments. This paper provides a comprehensive review of the use of wearable sensor technologies to enhance the safety and participation of children with ASD in sports. Utilizing a systematic approach, we analyze 144 papers identified through advanced search methodology. Our findings reveal that wearable sensors can monitor physiological signals like heart rate variability and electrodermal activity and biomechanical signals such as movement patterns to detect early signs of distress, anxiety, or potential injury. The integration of these technologies into sports settings for children with ASD presents significant potential for improving safety, reducing participation barriers, and enhancing overall well-being. Key findings indicate that while the application of wearable sensors in this context is still emerging, early results are promising. However, challenges remain regarding device usability, data privacy, and the need for further research to validate the effectiveness of these technologies in real-world sports environments. This review highlights the importance of interdisciplinary collaboration among researchers, technology developers, educators, and caregivers to develop and implement wearable sensor solutions that are tailored to the unique needs of children with ASD, thereby promoting safer and more inclusive sports participation.
Wearable self-healable strain sensors are gaining significant attention for applications in healthcare, robotics, and human-computer interaction. However, existing sensors face key challenges, including limited healin...
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Wearable self-healable strain sensors are gaining significant attention for applications in healthcare, robotics, and human-computer interaction. However, existing sensors face key challenges, including limited healing efficiency, low sensitivity to mechanical strain, and inadequate durability under repeated stress. Addressing these limitations, this study presents a novel strain sensor combining a polyurethane (PU) substrate with magnetic iron oxide nanoparticles (MIONPs) and silver flakes to enhance self-healing capabilities and strain sensitivity. The integration of MIONPs enables a self-healing efficiency of 96.6% within 24 h, a notable improvement over previous technologies that often require longer recovery times and achieve lower healing rates. Additionally, the sensor achieves a high gauge factor of 271.4 at 35% strain, representing a fourfold increase in sensitivity compared to traditional strain sensors. The sensor's responsiveness to external magnetic fields, with a magnetic sensitivity of 0.0049 T(-)1, further expands its application potential in areas like magnetically controlled devices and soft robotics. This work significantly advances multifunctional, self-healing strain sensors by addressing current limitations and offering improved performance for long-term, sustainable applications.
We present multicomponent knit resistive sensors for tracking tensile force. The knits were fabricated using a Twill structure, which is a simple pattern featuring anisotropic elastic behavior, providing high stabilit...
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We present multicomponent knit resistive sensors for tracking tensile force. The knits were fabricated using a Twill structure, which is a simple pattern featuring anisotropic elastic behavior, providing high stability along the course direction. Our sensors are made of two commercially available conductive yarn types, with highly different linear resistance. We present a variety of integration methods using the proposed Twill structure, all of which can be easily replicated on a two-bed weft-knitting machine. We evaluate the performance of the resulting sensor variations, with respect to consistency, hysteresis, short-term and long-term relaxation, and drift, among other metrics. We found that particulars of the knit's loop composition have a crucial effect on the consistency of the sensor readings. Furthermore, we show that knitting resistive yarn more tightly than the substrate material gives superior results and that improving elastic recoil by adding Lycra to the supporting substrate can considerably improve performance.
Integrating sensors within a complete readout system on a single die has become essential to the More-than-Moore philosophy. Mechanical stress, as one of the physical quantities of potential interest, provides various...
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Integrating sensors within a complete readout system on a single die has become essential to the More-than-Moore philosophy. Mechanical stress, as one of the physical quantities of potential interest, provides various information from simple static to dynamic load. integration of piezoresistive elements within a complete CMOS system has been achieved in many ways, and ground-laying effects have been studied and described in detail. To bring the mechanical and electrical domains closer together, a new concept is presented that allows an analytical and simulation-based approximation of the sensors' behavior due to applied mechanical stress as part of established concepts in electronics. It is evaluated based on measured state-of-the-art sensor implementations and used to bring up an alternative architecture with enhanced and on-the-fly adaptive sensitivity. Simulations are used to then further evaluate any model errors due to second-order effects that have been neglected within the design process.
Infrared optoelectronic sensing is the core of many critical applications such as night vision,health and medication,military,space exploration,*** including mechanical flexibility as a new dimension enables novel fea...
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Infrared optoelectronic sensing is the core of many critical applications such as night vision,health and medication,military,space exploration,*** including mechanical flexibility as a new dimension enables novel features of adaptability and conformability,promising for developing next-generation optoelectronic sensory applications toward reduced size,weight,price,power consumption,and enhanced performance(SWaP^(3)).However,in this emerging research frontier,challenges persist in simultaneously achieving high infrared response and good mechanical deformability in devices and integrated ***,we perform a comprehensive review of the design strategies and insights of flexible infrared optoelectronic sensors,including the fundamentals of infrared photodetectors,selection of materials and device architectures,fabrication techniques and design strategies,and the discussion of architectural and functional integration towards applications in wearable optoelectronics and advanced image ***,this article offers insights into future directions to practically realize the ultra-high performance and smart sensors enabled by infrared-sensitive materials,covering challenges in materials development and device micro-/*** for scaling these techniques across fabrication,performance,and integration are presented,alongside perspectives on potential applications in medication and health,biomimetic vision,and neuromorphic sensory systems,etc.
A series of sensors, designated S3R1-S3R4, were designed and synthesized for the detection of PO4 3- ions and toxic metals, specifically Hg2+ and Cu2+ ions. The colorimetric detection of PO4 3- ions using these sensor...
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A series of sensors, designated S3R1-S3R4, were designed and synthesized for the detection of PO4 3- ions and toxic metals, specifically Hg2+ and Cu2+ ions. The colorimetric detection of PO4 3- ions using these sensors exhibited a distinct visual color transition from yellow to purple in organo-aqueous media. The intrinsic cavitylike structure in the thiosemicarbazide-based derivative S3R4 significantly enhances the binding affinity for Hg2+ and Cu2+ ions in organic media. Utilizing UV-visible spectroscopic techniques and electrochemical investigations, the binding constants, stoichiometric ratios, limits of detection (LOD), and the electrochemical properties of the sensor-ion complexes were comprehensively characterized alongside their stability. Density Functional Theory (DFT) validation studies elucidated the binding mechanisms involved in the ion detection process. The LOD for PO4 3- with S3R1 was determined to be 0.28 ppm, while the LODs for Hg2+ and Cu2+ with S3R4 were found to be 0.15 ppm and 0.15 ppm, respectively. The significant binding constants and detection limits underscore the potential of S3R1-S3R4 as real-time sensors for detecting PO43- , Cu2+, and Hg2+ ions in environmental applications. Furthermore, the integration of molecular keypad locks and logic gate constructions highlights the applicability of these sensors in molecular communication systems.
With the profound changes in transportation and energy, the integration of new energy electric vehicles into the power grid will generate a large amount of data. sensors are deployed in the coupling environment of a t...
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With the profound changes in transportation and energy, the integration of new energy electric vehicles into the power grid will generate a large amount of data. sensors are deployed in the coupling environment of a transportation network and a power grid to transmit accurate monitoring data. Aiming at sensors that generate faults under the coupling interaction between a distribution network and a transportation network, in this paper, we propose a fault sensor node judgment method based on the spatial-temporal correlation fusion algorithm (FA). First, the cubic exponential smoothing (CES) algorithm of the time attribute and the piecewise least squares (PLSE) algorithm of the spatial properties are used to predict the temperature, humidity and voltage data monitored by the sensors. Then, according to the error size, the adaptive weight adjustment method is used to find the optimal weight value, and the FA model is obtained, so as to gain more accurate detection results. Finally, by comparing the predicted value with the set confidence interval, the identification of the fault sensor node is demonstrated. The results showed that the detection model proposed in this study has excellent fault sensor node detection performance. For the prediction results of the temperature data of the sensor, the fit accuracies of FA are 45.1 and 77.4% higher than those of ES and PLSE, respectively, which has certain practical significance.
Pervasive Wireless sensor Networks (PWSNs) are essential for collecting and transmitting real-time data from the physical world to various applications. Integrating blockchain technology with PWSNs enables decentraliz...
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Pervasive Wireless sensor Networks (PWSNs) are essential for collecting and transmitting real-time data from the physical world to various applications. Integrating blockchain technology with PWSNs enables decentralized data integration securely and immutably, although it requires energy-intensive computing processes. In this paper, the proposed model is designed to investigate the potential of blockchain technology to utilize acoustic signals in a pervasive acoustic wireless sensor network (PAWSN) to achieve energy efficiency. Considering the energy limitations of battery-operated sensor nodes, particularly acoustic sensors, and the energy consumption associated with blockchain technology, this study addresses these challenges by dividing the network into centralized and decentralized structures. In the proposed approach, acoustic sensor nodes operate within a centralized network structure, each assigned to a sensor zone with a central sink node. The sink nodes then participate in a decentralized network structure. To enhance energy efficiency, acoustic sensors in sensor zones are equipped with contextual information to minimize event listening. The primary objective of the proposed technique is to collect context-sensitive acoustic sensor data and integrate it into a decentralized blockchain while minimizing energy consumption at leaf nodes. The evaluation of the system architecture will employ blockchain technology, with a specific focus on leaf node energy efficiency (LNEN) as a primary performance metric. Analysis of experimental results reveals a substantial enhancement in energy efficiency, with LNEN approaching approximately 50% (LNEN approximate to 50%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$LNEN \approx 50\%$$\end{document}), attributed to the proposed model's effectiven
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