Sensor Components Research Articles

Geometrical and electrostatic densities in a highly sparse as-electrospun polystyrene microfiber mat

Electromechanically active nano/microfibers are promising components of sensors and actuators; however, piezoelectric polymers are normally expensive. To address this issue, this study examined the geometrical and electrostatic densities of an inexpensive highly sparse as-electrospun atactic polystyrene microfiber mat. The densities of porous individual fibers and the highly sparse fiber mat were experimentally determined to be 0.88 and 0.051 g cm−3, respectively, with corresponding material filling ratios of 80 % and 4.7 %, respectively. A high theoretical surface charge density of approximately 1.4 × 10−3 C m−2 was determined for the fiber mat after excluding air spaces in both individual fibers and the mat. These findings provide a pathway to outstanding electrets that are ultra-lightweight and have high charge densities.

Influence of Temperature on 17-4PH Characteristics of Torque Sensor Elastomer

The elastomer is the core component of the torque sensor, and its performance directly affects the accuracy of the torque sensor. The elastic modulus, as a key parameter of elastomers, can affect the output performance of sensors at extreme temperatures due to the temperature response. In this paper, the mathematical model of the torque sensor is analyzed, and the theoretical model of the elastic modulus of the material under the action of temperature is constructed. Then, the temperature performance test and the torque calibration test under the changing temperature environment are carried out for the elastomer made of 17-4PH material. The test results show that the relationship between the elastic modulus of the elastomer and the temperature is nonlinear, and the higher the temperature, the faster the elastic modulus decreases. The change law of the elastic modulus model considering the temperature effect is consistent with the change of the sensor output value, which provides a reliable basis for the precise design of the torque sensor.

Athermalization of Optical System with Large Field of View for Image Navigation

Image navigation sensor is one of the most important sensing means for human to explore the extraterrestrial space environment, and optical system is a key component of image navigation sensor, directly determines the amount of information perceived. The optical system requirements of image navigation sensors are gradually developing towards large field of view and miniaturization. The fisheye lens is the first choice for large field of view imaging by selecting the appropriate optical structure, without adding other additional components, and has the advantages of small size and strong perception energy. However, as the image surface illuminance of fisheye lens will decrease sharply with the increase of the field of view, the change of temperature will also degrade the imaging quality of the system, and strong stray light sources such as the sun will easily enter the field of view, which will greatly affect the perception ability of the system. Because of this, effective design of these systems is quite difficult. In this paper, a reverse-telephoto structure was used for large-field imaging, and the degradation of imaging quality caused by temperature changes was eliminated by selecting appropriate optical and mechanical materials. Designed with a focal length of 6.7mm, an F number of 3.6, a viewing angle of 130°, a working band of 0.45μm to 0.65μm, an after work distance of 7mm, a total system length≤60mm, we tested our system in a -40°C to +60°C temperature range and measured the image quality. Through simulation analysis, the system in the whole field of view within our spot diagrams was≤5.5μm, optical transfer function was greater than 0.2 at 90lp/mm, and no vignetting occurred. Furthermore, the imaging illumination of the edge field of view was 0.72 times that of the center field of view, the f – tan (θ) relative distortion value of the center field of view was≤2.3%, and the f – θ relative distortion value of the edge field of view was≤2.7%. Compared to normal temperatures, the optical transfer function changes at -20°C and +40°C were 0.02 and 0.01, respectively. Thus, the system displayed the characteristics of excellent image quality, small size, high energy utilization rate and good non-thermal performance.

Influence of polyaniline and cellulose nanocrystals on starch biopolymer film for intelligent food packaging

The development of bioplastics is important to reduce the environmental impact of petroleum-based plastic, while intelligent food packaging is essential in reducing food wastage through the accurate indication of food spoilage. In this research, a biopolymer film that is able to detect food spoilage has been fabricated. Starch is the main matrix of the polymer film, while polyaniline (PANI) acts as the sensor component. The biopolymer film is reinforced with cellulose nanocrystal (CNC) extracted from oil palm empty fruit bunches (OPEFB) to enhance the sensor performance. CNC was successfully extracted from OPEFB with a yield and crystallinity index as high as 41% and 83%, respectively. Various characterization methods were employed to characterize the samples, including FT-IR and UV–vis, while TEM was used to scrutinize the morphology of the CNC. The thermal stability of the bio-nanocomposite films was studied through TGA and DSC. The XRD analysis indicates that CNC improves the crystallinity of the film from 18% to 41%. The performance of the bio-nanocomposite films as an ammonia sensor was studied through an ammonia sensitivity test, where the optimum starch/PANI/CNC 6% film has a limit of detection (LOD) and limit of quantification (LOQ) of 52 ppm and 156 ppm, respectively (R2 = 0.9909, RSD = 3.98%). Also, it shows selectivity to ammonia gas only. Therefore, the starch/PANI/CNC biopolymer film has great potential for its application as intelligent food packaging.

A strategy for human safety monitoring in high-temperature environments by 3D-printed heat-resistant TENG sensors

Flexible wearable devices based on triboelectric nanogenerators (TENGs) can be used to track many parameters around the human body, including signals from motions, gestures, and contacts, to perform a variety of tasks. However, it is still a challenge to achieve wide working temperature and multi-parameter body motion sensing with TENG sensors. To address that, this work reports a facile 3D printing-based strategy for preparing functionalized heat-resistant TENG sensors. In detail, a printable heat-resistant ink and multiple 3D printing-based assembly methods are proposed. The as-fabricated ink, which could maintain up to 200 ℃, then serves as multiple components in the TENG sensors through patterning. Functionalized TENG sensors and human–machine interfaces are printed and subsequently networked on a human body to sense comprehensive motion information. The results indicate six typical motions can be recognized with 94.85% accuracy based on neural network algorithms. A simple digital twin for motion capture and trace tracking in virtual space is furthermore implemented. Combining the proposed TENG sensors with virtual reality (VR) platforms opens up a new road for human safety monitoring in high-temperature environments.

Recent Advances in Sensor-Actuator Hybrid Soft Systems: Core Advantages, Intelligent Applications, and Future Perspectives.

The growing demand for soft intelligent systems, which have the potential to be used in a variety of fields such as wearable technology and human-robot interaction systems, has spurred the development of advanced soft transducers. Among soft systems, sensor-actuator hybrid systems are considered the most promising due to their effective and efficient performance, resulting from the synergistic and complementary interaction between their sensor and actuator components. Recent research on integrated sensor and actuator systems has resulted in a range of conceptual and practical soft systems. This review article provides a comprehensive analysis of recent advances in sensor and actuator integrated systems, which are grouped into three categories based on their primary functions: i) actuator-assisted sensors for intelligent detection, ii) sensor-assisted actuators for intelligent movement, and iii) sensor-actuator interactive devices for a hybrid of intelligent detection and movement. In addition, several bottlenecks in current studies are discussed, and prospective outlooks, including potential applications, are presented. This categorization and analysis will pave the way for the advancement and commercialization of sensor and actuator-integrated systems.

Unraveling the Possibilities: Recent Progress in DNA Biosensing.

Due to the advantages of its numerous modification sites, predictable structure, high thermal stability, and excellent biocompatibility, DNA is the ideal choice as a key component of biosensors. DNA biosensors offer significant advantages over existing bioanalytical techniques, addressing limitations in sensitivity, selectivity, and limit of detection. Consequently, they have attracted significant attention from researchers worldwide. Here, we exemplify four foundational categories of functional nucleic acids: aptamers, DNAzymes, i-motifs, and G-quadruplexes, from the perspective of the structure-driven functionality in constructing DNA biosensors. Furthermore, we provide a concise overview of the design and detection mechanisms employed in these DNA biosensors. Noteworthy advantages of DNA as a sensor component, including its programmable structure, reaction predictility, exceptional specificity, excellent sensitivity, and thermal stability, are highlighted. These characteristics contribute to the efficacy and reliability of DNA biosensors. Despite their great potential, challenges remain for the successful application of DNA biosensors, spanning storage and detection conditions, as well as associated costs. To overcome these limitations, we propose potential strategies that can be implemented to solve these issues. By offering these insights, we aim to inspire subsequent researchers in related fields.

Design parameters of free-form color splitters for subwavelength pixelated image sensors

Metasurface-based color splitters are emerging as next-generation optical components for image sensors, replacing classical color filters and microlens arrays. In this work, we report how the design parameters such as the device dimensions and refractive indices of the dielectrics affect the optical efficiency of the color splitters. Also, we report how the design grid resolution parameters affect the optical efficiency and discover that the fabrication of a color splitter is possible even in legacy fabrication facilities with low structure resolutions.

High-concentration F-incorporated ZnO thin films doped via femtosecond-laser hyperdoping

ZnO is a transparent conductive oxide that has received great attention over the past few decades, as it is a key component in light-emitting diodes, gas sensors, and solar cells. However, the electrical conductivity of intrinsic ZnO is not ideal, and thus, ZnO is often doped with appropriate elements such as fluorine (F). Unfortunately, the lack of adequate precursors in traditional doping methods may increase the difficulty of ZnO doping with F. In this work, we realized, for the first time, high-concentration F-doped ZnO thin films with a high Hall mobility of 20.7 cm2/V/s via femtosecond-laser hyperdoping. The highest atomic percentage of F was determined to be 8.3 at.%, which was over three times larger than that of previously reported F-doped ZnO films. Compared with pristine ZnO, the highest carrier concentration of the F-doped ZnO could be increased by four orders of magnitude, and the resistivity could be reduced by three orders of magnitude. Our results demonstrated that the femtosecond-laser hyperdoping technology could achieve efficient non-metallic hyperdoping of ZnO with high transparency and Hall mobility, thereby reducing the power dissipation and improving the current carrying capacity of ZnO-based devices.

Benzylfentanyl as a Surrogate Template for Fentanyl-Selective Imprinted Polymers.

The illicit use of fentanyl has led to hundreds of thousands of opioid-related deaths worldwide. Therefore, the detection of fentanyl by law enforcement and recreational users is of utmost importance. However, current detection methods are expensive, time-consuming, require special storage conditions, and necessitate complex instrumentation that is generally unportable and requires skilled personnel to operate. An alternative approach would be using molecularly imprinted polymers (MIPs) as the recognition component of a handheld sensor, testing strip, or color-based assay. In this work, a molecularly imprinted polymer was constructed using the functional monomer methacrylic acid (MAA) and the cross-linking monomer ethyleneglycol dimethacrylate (EGDMA), with benzylfentanyl (Bfen) as the template. The use of benzylfentanyl is advantageous because it closely mimics fentanyl's structure but does not cause any physiological narcotic effects. Important studies herein determined the optimum ratio of the template/functional monomer, with subsequent evaluations of selectivity of the MIP for the template and fentanyl versus the commonly encountered narcotics such as methamphetamine, cocaine, and heroin. The data obtained from the HPLC analysis showed that the Bfen-MIP was successful in selectively binding the template and actual fentanyl, better than other common narcotics.

Velocity range-based reward shaping technique for effective map-less navigation with LiDAR sensor and deep reinforcement learning.

In recent years, sensor components similar to human sensory functions have been rapidly developed in the hardware field, enabling the acquisition of information at a level beyond that of humans, and in the software field, artificial intelligence technology has been utilized to enable cognitive abilities and decision-making such as prediction, analysis, and judgment. These changes are being utilized in various industries and fields. In particular, new hardware and software technologies are being rapidly applied to robotics products, showing a level of performance and completeness that was previously unimaginable. In this paper, we researched the topic of establishing an optimal path plan for autonomous driving using LiDAR sensors and deep reinforcement learning in a workplace without map and grid coordinates for mobile robots, which are widely used in logistics and manufacturing sites. For this purpose, we reviewed the hardware configuration of mobile robots capable of autonomous driving, checked the characteristics of the main core sensors, and investigated the core technologies of autonomous driving. In addition, we reviewed the appropriate deep reinforcement learning algorithm to realize the autonomous driving of mobile robots, defined a deep neural network for autonomous driving data conversion, and defined a reward function for path planning. The contents investigated in this paper were built into a simulation environment to verify the autonomous path planning through experiment, and an additional reward technique "Velocity Range-based Evaluation Method" was proposed for further improvement of performance indicators required in the real field, and the effectiveness was verified. The simulation environment and detailed results of experiments are described in this paper, and it is expected as guidance and reference research for applying these technologies in the field.

Intelligent Grapevine Disease Detection Using IoT Sensor Network.

The Internet of Things (IoT) has gained significance in agriculture, using remote sensing and machine learning to help farmers make high-precision management decisions. This technology can be applied in viticulture, making it possible to monitor disease occurrence and prevent them automatically. The study aims to achieve an intelligent grapevine disease detection method, using an IoT sensor network that collects environmental and plant-related data. The focus of this study is the identification of the main parameters which provide early information regarding the grapevine's health. An overview of the sensor network, architecture, and components is provided in this paper. The IoT sensors system is deployed in the experimental plots located within the plantations of the Research Station for Viticulture and Enology (SDV) in Murfatlar, Romania. Classical methods for disease identification are applied in the field as well, in order to compare them with the sensor data, thus improving the algorithm for grapevine disease identification. The data from the sensors are analyzed using Machine Learning (ML) algorithms and correlated with the results obtained using classical methods in order to identify and predict grapevine diseases. The results of the disease occurrence are presented along with the corresponding environmental parameters. The error of the classification system, which uses a feedforward neural network, is 0.05. This study will be continued with the results obtained from the IoT sensors tested in vineyards located in other regions.

Photoinduced Processes in Molecular and Supramolecular Arrays Based on Porphyrins and Acridinium Units

Multichromophoric arrays based on porphyrins are of high interest for their mimicry of naturally occurring photoinduced energy and electron transfer processes leading to energy collection and conversion. [1] Moreover, the rich photophysics and electrochemistry of porphyrins make them suitable components of molecular wires, switches or chemical sensors. [2] In this perspective, it is interesting to pursue the combination of porphyrins with units that can act as multiresponsive components by modifying their chemical and electronic structure upon chemical or redox stimuli. N-acridinium ions have been recently explored as a new class of switches that respond to various orthogonal stimulations (pH or electrochemical/photochemical inputs) that trigger their reversible conversion into the non-aromatic acridane form. [3] Few examples reporting the association of acridinium units and porphyrins indicate that these systems are characterized by fast photoinduced electron transfer reactions between the porphyrin donor and the acridinium acceptor. [4,5]We recently investigated the photophysical properties of molecular and supramolecular arrays based on porphyrins and acridinium units, where the type of porphyrin (free-base or metallated) and/or the form of the switchable component (acridinium or acridane) has been varied (Figure 1). We found that the association of porphyrins and acridinium ions always leads to ultrafast electron transfer processes, and that these reactions can be altered by changing the temperature or by converting the acridinium electron acceptor into an acridane energy donor. Moreover, the combination of two Zn-porphyrins with two acridinium/acridane units results in switchable molecular receptors able to coordinate suitable hosts. The photophysics of the arrays and of the complexes, analyzed by means of steady-state and time-resolved luminescence techniques and ultrafast transient absorption spectroscopy, will be herein presented and discussed.The work is supported by the H2020-MSCA-ITN-2017-765297 project “NOAH” and by the H2020-LC-SC3-2020-RES-RIA-101006839 project “CONDOR”.[1] M. Beyler, L. Flamigni, V. Heitz, J.-P. Sauvage, B. Ventura, Photochem. Photobiol. 2014, 90, 275-286.[2] Beletskaya I, Tyurin VS, Tsivadze AY, Guilard R, Stern C. Chem. Rev. 2009, 109, 1659-1713.[3] H.-P. J. de Rouville, J. Hu, V. Heitz, ChemPlusChem, 2021, 86, 110-129.[4] A. Edo-Osagie, D. Sánchez-Resa, D. Serillon, E. Bandini, C. Gourlaouen, H.-P. Jacquot de Rouville, B. Ventura, V. Heitz, C. R. Chim. 2021, 24, 1-9.[5] H. Kotani, K. Ohkubo, M. J. Crossley, S. Fukuzumi, J. Am. Chem. Soc., 2011, 133, 11092-11095.Figure 1. Structures of the studied arrays and schemes of the occurring photoinduced processes: a) bisacridinium-Zn(II) porphyrin conjugate, b) bisacridinium-diphenylporphyrin conjugate, c) bis(acridinium/acridane)-Zn(II) porphyrin receptors. Figure 1

Engineering Plasmonic Hybridization toward Advanced Optical Sensors

AbstractThe requirements for an optical sensor are high: the sensor should be reliable, quick, and simple, and all these features must be provided at ultralow fabrication costs. The requirements go even further, as the sensor may operate without an energy source, and nowadays, the sensor components should be recyclable. Today existing plasmonic concepts and advances in nanofabrication can meet these requirements. This review article describes the available nanoscale components and discusses their reasonable layout to obtain the sensing requirements. It is shown that combining photonic and plasmonic properties helps to get high sensitivity at low losses, where these hybrid modes with simple optical concepts are introduced. The current state of the art in developing lithographic nanostructuring and directed self‐assembly to pave the way for future refractive index sensors is also used. By providing a guideline for advanced sensors, how the individual optical components can be used to create hybrid properties that can be realized cost‐effectively are shown. The review article ends with a discussion on the perspectivs for plasmonic hybridization sensors.

Capacitive humidity sensor based on zirconium phosphate nanoplates film with wide sensing range and high response

Humidity sensing material is a key component of humidity sensor. In this short communication, we synthesized zirconium phosphate (ZrP) nanoplates and investigated their capacitive humidity sensing performance. The results show that the ZrP humidity sensor exhibits a wide response range (10.9–91.5 % relative humidity (RH)), high response (2409 at 91.5 % RH), short recovery time (9 s), and good repeatability at the optimal working frequency of 100 Hz and room temperature of 25 °C. In addition, the humidity sensing mechanism of the ZrP humidity sensor is analyzed by combining the characterization, dielectric constant and impedance testing results.

Application of MXene in the Electrochemical Detection of Neurotransmitters: A Review

Neurotransmitters are small chemical signaling molecules crucial for the proper function of the nervous system. The dysregulation of neurotransmitters results in several mental disorders like Parkinson’s and Alzheimer’s diseases, schizophrenia, and conditions such as depression and addiction. These signaling molecules are present at low concentrations, and obtaining information about these molecules’ levels is vital. Moreover, neurotransmitter monitoring in the nervous system remains challenging due to its low concentrations and rapid response. Electrochemical detection continues to garner significant attention as an attractive technique due to its facile nature, high sensitivity, and cost-effectiveness. The electroactive materials of electrochemical sensors are at the heart of this sensing technology. Although multiple nanomaterials have been explored as active components in electrochemical sensors for detecting neurotransmitters, MXenes are gaining attention in the electrochemical sensing of neurotransmitters. This review aims to discuss the use of MXenes and their composites for the electrochemical detection of neurotransmitters, describe the various MXene composites based on the nature of the composite viz pristine and chemical functionalized, carbon nanomaterial, polymer, metal nanoparticle, and transition metal dichalcogenide composites, and define the future directions in leveraging the properties of MXene composites for early-stage electrochemical detection of neurological diseases originating from an imbalance in neurotransmitters.

Multifaceted contributions of Dicer2 to arbovirus transmission by Aedes aegypti

Arthropod-borne viruses (arboviruses) transmitted by Aedes aegypti mosquitoes are an increasing threat to global health. The small interfering RNA (siRNA) pathway is considered the main antiviral immune pathway of insects, but its effective impact on arbovirus transmission is surprisingly poorly understood. Here, we use CRISPR-Cas9-mediated gene editing invivo to mutate Dicer2, a gene encoding the RNA sensor and key component of the siRNA pathway. The loss of Dicer2 enhances early viral replication and systemic viral dissemination of four medically significant arboviruses (chikungunya, Mayaro, dengue, and Zika viruses) representing two viral families. However, Dicer2 mutants and wild-type mosquitoes display overall similar levels of vector competence. In addition, Dicer2 mutants undergo significant virus-induced mortality during infection with chikungunya virus. Together, our results define a multifaceted role for Dicer2 in the transmission of arboviruses by Ae. aegypti mosquitoes and pave the way for further mechanistic investigations.

Condition-Based Maintenance for Performance Degradation Under Nonperiodic Unreliable Inspections

With the development of monitoring technology, increasing attention has been paid to condition-based maintenance (CBM). Also, if condition information for maintenance can be obtained with fewer inspections, the cost of the entire maintenance process could be reduced. However, in application, the monitoring equipment cannot maintain always a reliable operating state, for example, due to various uncertain factors, such as sensor errors, component tolerances, and environment disturbances, the inspections from the sensors are often unreliable. Motivated by these simple observations, we propose a non-periodic condition-based maintenance policy under unreliable inspections. The time interval of the non-periodic inspections is obtained via an inspection scheduling function. The unknown parameters of the component degradation process are updated by gradient descent, while simultaneously the maintenance decision variables are adjusted. A catastrophe strategy-based particle swarm optimization is used to set the optimal decision variables by minimizing the long-run cost rate. Application to laser degradation data illustrates the effectiveness of the proposed method.

Sparse attack reconstruction for cyber–physical systems via descriptor reduced-order observer

Cyber attacks seriously threaten the security of Cyber–Physical Systems (CPSs) which are composed of cyber and physical realms. From the perspectives of control and system engineering, attacks will causally sabotage the performances of controlled (physical) plants with substantial damage. This paper focuses on the issue of secure estimation for CPSs under malicious false data injection (FDI) attacks implemented on sensor and actuator components synchronously. Initially, by establishing an adaptive counteraction searching strategy, the combinational entry mode of FDI attacks can be well located. Followed by which, a descriptor reduced-order switched observer from the lens of decoupling technique is crafted for reconstructing system states and sparse FDI attacks. The resulting secure estimation scheme can potentially avoid the sliding surface switching downside compared to the existing ones. Then, the sufficient conditions on ensuring the convergence of estimated errors and determining the adaptive switching counteraction logic are meticulously derived. Eventually, both the applicability and the superiority of our theoretical derivation are verified by the case study.

Overview of Food Preservation and Traceability Technology in the Smart Cold Chain System.

According to estimates by the Food and Agriculture Organization of the United Nations (FAO), about a third of all food produced for human consumption in the world is lost or wasted-approximately 1.3 billion tons. Among this, the amount lost during the storage stage is about 15-20% for vegetables and 10-15% for fruits. It is 5-10% for vegetables and fruits during the distribution stage, resulting in a large amount of resource waste and economic losses. At the same time, the global population affected by hunger has reached 828 million, exceeding one-tenth of the total global population. The improvement of the cold chain system will effectively reduce the amount of waste and loss of food during the storage and transportation stages. Firstly, this paper summarizes the concept and development status of traditional preservation technology; environmental parameter sensor components related to fruit and vegetable spoilage in the intelligent cold chain system; the data transmission and processing technology of the intelligent cold chain system, including wireless network communication technology (WI-FI) and cellular mobile communication; short-range communication technology, and the low-power, wide-area network (LPWAN). The smart cold chain system is regulated and optimized through the Internet of Things, blockchain, and digital twin technology to achieve the sustainable development of smart agriculture. The deep integration of artificial intelligence and traditional preservation technology provides new ideas and solutions for the problem of food waste in the world. However, the lack of general standards and the high cost of the intelligent cold chain system are obstacles to the development of the intelligent cold chain system. Governments and researchers at all levels should strive to highly integrate cold chain systems with artificial intelligence technology, establish relevant regulations and standards for cold chain technology, and actively promote development toward intelligence, standardization, and technology.