Range Of Sensors Research Articles

Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle-Decorated Carbon Nanotube-Based Field-Effect Transistor Sensors.

The opioid overdose crisis is a global health challenge. Fentanyl, an exceedingly potent synthetic opioid, has emerged as a leading contributor to the surge in opioid-related overdose deaths. The surge in overdose fatalities, particularly due to illicitly manufactured fentanyl and its contamination of street drugs, emphasizes the urgency for drug-testing technologies that can quickly and accurately identify fentanyl from other drugs and quantify trace amounts of fentanyl. In this paper, gold nanoparticle (AuNP)-decorated single-walled carbon nanotube (SWCNT)-based field-effect transistors (FETs) are utilized for machine learning-assisted identification of fentanyl from codeine, hydrocodone, and morphine. The unique sensing performance of fentanyl led to use machine learning approaches for accurate identification of fentanyl. Employing linear discriminant analysis (LDA) with a leave-one-out cross-validation approach, a validation accuracy of 91.2% is achieved. Meanwhile, density functional theory (DFT) calculations reveal the factors that contributed to the enhanced sensitivity of the Au-SWCNT FET sensor toward fentanyl as well as the underlying sensing mechanism. Finally, fentanyl antibodies are introduced to the Au-SWCNT FET sensor as specific receptors, expanding the linear range of the sensor in the lower concentration range, and enabling ultrasensitive detection of fentanyl with a limit of detection at 10.8fgmL-1.

A Robust Methodology for Dynamic Proximity Sensing of Vehicles Overtaking Micromobility Devices in a Noisy Environment

The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance laws, where motorists are required to maintain a minimum distance of 1.5 m when passing a cyclist, are difficult to enforce due to the difficulty in determining the exact distance between a moving vehicle and a cyclist. Existing systems reported in the literature are invariably used for research and require manual intervention to record passing vehicles. Further, due to the dynamic and noisy environment on the road, the collected data also need to be manually post-processed to remove errors and false positives, thus making such systems impractical for use by cyclists. This study aims to address these two concerns by providing an automated and robust framework, integrating a low-cost, small single-board computer with a range sensor and a camera, to measure and analyze vehicle–cyclist passing distance and speed. Preliminary deployments in Singapore have demonstrated the system’s efficacy in capturing high-resolution data under varied traffic conditions. Our setup, using a Raspberry Pi 4, LiDAR distance sensor, a small camera, and an automated data clustering technique, had a high success rate for correctly identifying the number of close vehicle passes for distances between 1 and 1.5 m. The insights garnered from this integrated setup promise not only a deeper understanding of interactions between motor vehicles and micromobility devices, but also a roadmap for data-driven urban safety interventions.

Designing a highly near infrared-reflective black nanoparticles for autonomous driving based on the refractive index and principle

HypothesisThe development of highly NIR reflective black single-shell hollow nanoparticles (BSS-HNPs) can overcome the Light Detection and Ranging (LiDAR) sensor limitations of dark-tone materials. The crystalline phase of TiO2 and the refractive index can be controlled by calcination temperature. The formation of hollow structure and the refractive index is expected to simultaneously increase the light reflection and LiDAR detectability. ExperimentsThe BSS-HNPs are synthesized using the sol–gel method, calcination, NaBH4 reduction, and etching to form a hollow structure with true blackness. The computational bandgap calculation is conducted to determine the bandgap energy (Eg) of the white and black TiO2 with different crystalline structures. The blackness of the as-synthesized materials is determined by the Commission on Illumination (CIE) L*a*b* color system. FindingsThe hydrophilic nature of BSS-HNPs enables the formulation of hydrophilic paints, allowing the mono-layer coating. With the synergistic effects of hollow structure and the refractive index, BSS-HNPs manifested superb NIR reflectance at LiDAR detection wavelengths. The high detectability, blackness, and hollow structure of BSS-HNPs can expand the variety of LiDAR-detectable dark-tone materials.

Large detection range and high strain sensitivity fiber SPR sensor based on wave structure

To achieve a fiber strain sensor with a large detection range and high sensitivity, this paper proposes a wave structured fiber SPR strain sensor. When subjected to axial strain, the wave structured fiber is stretched axially, increasing the stretchability of the sensor and achieving a large detection range strain sensing. Meanwhile, axial strain reduces the longitudinal amplitude of the fiber wave structure, effectively changing the total reflection angle of the transmitted beam at the peak and valley (SPR incidence angle) to achieve high sensitivity SPR strain sensing. The experiment indicates that the strain detection range of the sensor can reach 0-1800µε, with a maximum strain sensitivity of 36.25pm/µε. The wave structured fiber SPR strain sensor designed in this article provides a new approach to improve the range and sensitivity of strain detection.

In Situ-Derived N-Doped ZnO from ZIF-8 for Enhanced Ethanol Sensing in ZnO/MEMS Devices.

Microelectromechanical systems (MEMS) gas sensors have numerous advantages such as compact size, low power consumption, ease of integration, etc., while encountering challenges in sensitivity and high resistance because of their low sintering temperature. This work utilizes the in situ growth of Zeolitic Imidazolate Framework-8 (ZIF-8) followed by its conversion to N-doped ZnO. The results obtained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM) indicate that the in situ derivation of ZIF-8 facilitates the adhesion of ZnO particles, forming an island-like structure and significantly reducing the interfaces between these particles. Furthermore, powder X-ray diffraction (XRD) analysis, elemental mapping, and X-ray photoelectron spectroscopy (XPS) analysis confirm the conversion of ZIF-8 to ZnO, the successful incorporation of N atoms into the ZnO lattice, and the creation of more oxygen vacancies. The ZIF-8-derived N-doped ZnO/MEMS sensor (ZIF (3)-ZnO/MEMS) exhibits remarkable gas sensitivity for ethanol detection. At an operating temperature of 290 °C, it delivers a substantial response value of 80 towards 25 ppm ethanol, a 13-fold enhancement compared with pristine ZnO/MEMS sensors. The sensor also exhibits an ultra-low theoretical detection limit of 11.5 ppb to ethanol, showcasing its excellent selectivity. The enhanced performance is attributed to the incorporation of N-doped ZnO, which generates abundant oxygen vacancies on the sensor's surface, leading to enhanced interaction with ethanol molecules. Additionally, a substantial two-order-of-magnitude decrease in the resistance of the gas-sensitive film is observed. Overall, this study provides valuable insights into the design and fabrication strategies applicable to high-performance MEMS gas sensors in a broader range of gas sensing.

Comprehensive Investigation of the Temperature-Dependent Electromechanical Behaviors of Carbon Nanotube/Polymer Composites.

The performances of flexible piezoresistive sensors based on polymer nanocomposites are significantly affected by the environmental temperature; therefore, comprehensively investigating the temperature-dependent electromechanical response behaviors of conductive polymer nanocomposites is crucial for developing high-precision flexible piezoresistive sensors in a wide-temperature range. Herein, carbon nanotube (CNT)/polydimethylsiloxane (PDMS) composites widely used for flexible piezoresistive sensors were prepared, and then the temperature-dependent electrical, mechanical, and electromechanical properties of the optimized CNT/PDMS composite in the temperature range from -150 to 150 °C were systematically investigated. At a low temperature of -150 °C, the CNT/PDMS composite becomes brittle with a compressive modulus of ∼1.2 MPa and loses its elasticity and reversible sensing capability. At a high temperature (above 90 °C), the CNT/PDMS composite softens, shows a fluid-like mechanical property, and loses its reversible sensing capability. In the temperature range from -60 to 90 °C, the CNT/PDMS composite exhibits good elasticity and reversible sensing behaviors and its modulus, resistivity, and sensing sensitivity decrease with an increasing temperature. At room temperature (30 °C), the CNT/PDMS composite exhibits better mechanical and piezoresistive stability than those at low and high temperatures. Given that environmental temperature changes have significant effects on the sensing performances of conductive polymer composites, the effect of ambient temperature changes must be considered when flexible piezoresistive sensors are designed and fabricated.

Flying Projectile Attitude Determination from Ground-Based Monocular Imagery with a Priori Knowledge

This paper discusses using ground-based imagery to determine the attitude of a flying projectile assuming prior knowledge of its external geometry. It presents a segmentation-based approach to follow the object and evaluates it quantitatively with simulated data and qualitatively with both simulated and real data. Two experimental cases are considered: One assumes reliable target distance measurement from an auxiliary range sensor, while the other assumes no range information. The results show that in the case of an unknown projectile–camera distance, with projectile dimensions of 1.378 m and 0.08 m in length and diameter, the estimated distance, in-plane location, and pitch angle accuracies are about 50 m, 0.15 m, and 6 degrees, respectively. Yaw angle estimation is ambiguous. In the second case, assuming that the projectile–camera distance is known resolves the ambiguity of yaw estimation, resulting in accuracies of about 0.15 m, 3 degrees, and 20 degrees for in-plane location, pitch, and yaw angles, respectively. These accuracies were normalized to a 1-km projectile–camera distance.

Ultrawide Sensing Range and High-Sensitivity Capacitive Pressure Sensor Based on Skeleton Dilution Strategies for Human Motion and Correction of Poor Body Posture

Ultrawide Sensing Range and High-Sensitivity Capacitive Pressure Sensor Based on Skeleton Dilution Strategies for Human Motion and Correction of Poor Body Posture

Acoustic field visualisation using local absorption of ultrasound and thermochromic liquid crystals

Acoustic field and vibration visualisation is important in a wide range of applications. Laser vibrometry is often used for such visualisation, however, the equipment has a high cost and requires significant user expertise, and the method can be slow, as it requires scanning point by point. Here we suggest a different approach to visualisation of acoustic fields in the kHz – MHz range, using paint-on or removable film sensors, which produce a direct visual map of ultrasound displacement. The sensors are based on a film containing thermochromic liquid crystals (TLC), along with a backing/underlay layer which improves absorption of ultrasound. The absorption generates heat, which can be seen by a change in colour of the TLC film. A removable sensor is used to visualise the resonant modes of an air-coupled flexural transducer operated from 410 kHz to 7.23 MHz, and to visualise 40 kHz standing waves in a Perspex plate. The thermal basis of the visualisation is confirmed using thermal imaging. The speed and cost of visualisation makes the new sensor attractive for use in condition monitoring, for fast assessment of transducer quality, or for analysis of acoustic field distribution in power ultrasonic systems.

Ultrasensitive solid-state electrochemiluminescence sensor based on lotus root shaped carbon fiber, CdSe QDs and Fe3O4 synergically amplify Ru(bpy)32+ luminophore signal for detection of cyfluthrin.

An efficient and innovative electrochemiluminescence (ECL) sensor was developed for trace detection of cyfluthrin.The sensor utilized materials such as lotus root shaped carbon fiber (Co CNFs), cadmium selenide quantum dots (CdSe QDs), and Fe3O4 to amplify Ru(bpy)32+ signals. Co CNFs, with its large specific surface area and porosity, served the purpose of not only enhancing the stability of the sensor by fixing CdSe QDs and Ru(bpy)32+ on the Co CNFs/GCE, but also facilitating electron transfer. CdSe QDs was involved in the luminescence reaction and collaborated with Ru(bpy)32+ to enhance the sensor's sensitivity, while Fe3O4 promoted electron transfer in the system due to its large surface area. The solid-state ECL sensor achieved satisfactory signal under the synergistic action of these components. The ECL signal of the sensor was quenched by cyfluthrin, and a favorable linear relationship was observed between the sensor and cyfluthrin in the concentration range 1 × 10-12 to 1 × 10-6 M. The detection limit of the sensor was 3.3 × 10-13 M (S/N = 3). The utilization of lotus root shaped carbon fiber, CdSe QDs, and Fe3O4 in the Ru(bpy)32+ system demonstrated a synergistic effect for cyfluthrin detection, presenting a new approach for the rapid determinationanalysis of pesticide residues in foods.

During an 18-month course of automated insulin delivery treatment, children aged 2 to 6 years achieve and maintain a higher time in tight range.

To investigate whether the positive effects on glycaemic outcomes of 3-month automated insulin delivery (AID) achieved in 2- to 6-year-old children endure over an extended duration and how AID treatment affects time in tight range (TITR), defined as 3.9-7.8 mmol/L. We analysed 18 months of follow-up data from a non-randomized, prospective, single-arm clinical trial (n = 35) conducted between 2021 and 2023. The main outcome measures were changes in time in range (TIR), glycated haemoglobin (HbA1c), time above range (TAR), TITR, and mean sensor glucose (SG) value during follow-up visits (at 0, 6, 12 and 18 months). The MiniMed 780G AID system in SmartGuard Mode was used for 18 months. Parental diabetes distress was evaluated at 3 and 18 months with the validated Problem Areas in Diabetes-Parent, revised (PAID-PR) survey. Between 0 and 6 months, TIR and TITR increased, and HbA1c, mean SG value and TAR decreased significantly (p < 0.001); the favourable effect persisted through 18 months of follow-up. Between 3 and 18 months, PAID-PR score declined significantly (0 months: mean score 37.5; 3 months: mean score 28.6 [p = 0.06]; 18 months: mean score 24.6 [p < 0.001]). Treatment with AID significantly increased TITR and TIR in young children. The positive effect of AID on glycaemic control observed after 6 months persisted throughout the 18 months of follow-up. Similarly, parental diabetes distress remained reduced during 18 months follow-up. These findings are reassuring and suggest that AID treatment improves glycaemic control and reduces parental diabetes distress in young children over an extended 18-month follow-up.

Rhodamine B-Containing Chitosan-Based Films: Preparation, Luminescent, Antibacterial, and Antioxidant Properties.

In this study, Rhodamine B-containing chitosan-based films were prepared and characterized using their mechanical, photophysical, and antibacterial properties. The films were synthesized using the casting method and their mechanical properties, such as tensile strength and elongation at break, were found to be dependent on the chemical composition and drying process. Infrared spectroscopy and X-ray diffraction analysis were used to examine the chemical structure and degree of structural perfection of the films. The photophysical properties of the films, including absorption spectra, fluorescence detection, emission quantum yields, and lifetimes of excited states, were studied in detail. Rhodamine B-containing films exhibited higher temperature sensitivity and showed potential as fluorescent temperature sensors in the physiological range. The antibacterial activity of the films was tested against Gram-positive bacteria S. aureus and Gram-negative bacteria E. coli, with Rhodamine B-containing films demonstrating more pronounced antibacterial activity compared to blank films. The findings suggest that the elaborated chitosan-based films, particularly those containing Rhodamine B can be of interest for further research regarding their application in various fields such as clinical practice, the food industry, and agriculture due to their mechanical, photophysical, and antibacterial properties.

A self-powered flexible tactile sensor utilizing chemical battery reactions to detect static and dynamic stimuli

Flexible and wearable tactile sensors have become increasingly important in various fields, including healthcare monitoring and human-computer interaction. However, traditional resistive or capacitive sensors require an external power supply to continuously consume energy, while piezoelectric and triboelectric sensors respond only to dynamic mechanical stimuli. The development of tactile sensors that do not require an external power source and are capable of monitoring dynamic/static mechanical stimuli could complement the shortcomings of existing sensing devices. In this paper, we propose a simple, convenient, and environmentally friendly paper-based sandwich solid Zn-MnO2 battery, which can be used as a self-powered flexible tactile sensor through simple structural design. As a battery, it exhibited an open circuit voltage of 1.29 V, the highest short circuit current density of 2.8 mA/cm2, and a capacity of 2.2 mAh/cm2 at a current density of 0.1 mA/cm2, which is sufficient to power small electrical appliances. As a self-powered pressure sensor, it exhibits an average sensitivity of 1.27 μA/kPa in the 0–25 kPa and 0.14 μA/kPa within the 25–500 kPa pressure range, a response time of 42 ms and a recovery time of 41 ms, a minimum pressure resolution of tens of Pascals, and long-period repeatability and stability. This work will significantly contribute to the further advancement of intelligent, self-powered tactile sensors for a wide range of applications in fields such as human healthcare and human-machine interfaces for detecting static and dynamic stimuli.

Non-destructive methodology for crack detection using machine learning-assisted resonant sensor

Condition monitoring of structures built using metallic supports is a significant issue in structural engineering. Through this work, design, fabrication, and optimization of a dual-scale resonance-based microwave sensor is discussed for monitoring cracks developed over metallic surfaces. These sensors were initially simulated and later fabricated in micro-wave (4.59 GHz) and radio-wave region (418 MHz) for which the power attenuation and frequency shifts were observed. With the structure having crack depth of 2 mm, microwave sensor showed the power shift ∼24 %, while the radio-wave range sensors showed ∼9 %. The corresponding frequency shifts were 2.4 % and 0.85 %, respectively. The microwave sensor (power shift data) showed ∼280 % more sensitivity than the radio-wave sensor. By using dual scale, comprising of both frequency and power shifts, the microwave sensor classification was further improved to 99.72 %, as compared to their individual counterparts. A non-destructive crack detection system in microwave regime is hence demonstrated, which can be further explored for range of crack studies in metallic structures.

Prototype of Check the Class Individually by Making Our Rounds in the Classroom Trajectory Measurement System Using Laser Range Sensor

Prototype of Check the Class Individually by Making Our Rounds in the Classroom Trajectory Measurement System Using Laser Range Sensor

MnO2-based dual channel surface plasmon resonance fiber sensor for trace glutathione and refractive index detection.

Glutathione (GSH) plays vital role in human biological systems, so its rapid and sensitive detection is necessary for health condition monitoring. In this work, a simple structure for dual channel GSH and refractive index (RI) detection is proposed. By introducing Au-MnO2 thin film coating on the fiber surface for the first time, GSH solution would lead to the dissolution of MnO2, the change in GSH levels could be monitored over a short period in channel 2. For channel 1, ITO-Ag thin film is applied for RI change detection. After optimization, the GSH detection sensitivity reached about -2.361 nm/mM in the range of 0.005-50 mM, and the RI sensitivity reached 1704.252 nm/RIU in the range of 1.331-1.3895 RIU. Channel 1 could also put into GSH detection in the high concentration scale to enlarge the sensor's range and 0.095 nm/mM of sensitivity is acquired within the range of 50-600 mM. With the presence of MnO2 film, the detection sensitivity increased 25.663 times. Neither channel interferes with the operation of the other. Proposed sensor provides stability, high selectivity and elevation in GSH detection sensitivity, which shows great potential for environmental and biological detection field and their applications.

A Data-Driven Model for Range Sensors

In this paper, an analysis of the precision of range sensors is presented. Light Detection and Ranging (LIDAR) data from three different sensors (HLS-LFCD-LDS, SICK TIM561, and Kinect V2), stereo data from the Realsense D435, and structured light data from both Kinect V1 for Xbox and the Xtion PRO Live were analyzed regarding the influence of range, incident angle to the surface, and surface material. In addition, a comparison with standard deviation models based on vendor-provided specifications was performed. We found that the vendor specifications are in general not specific enough to facilitate accurate sensor simulations. Therefore, we developed a data-driven model for range precision. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision.

Investigation of sound pressure leakage effect for primary calibration down to 10−2 Hz using a laser pistonphone system

Recently, the demand for sensor calibration in the infrasonic range has increased to obtain accurate information when monitoring infrasound generated by large-scale natural disasters. The laser pistonphone method is a primary calibration method to evaluate infrasound sensors in the low-frequency range. In this method, sound pressure is generated in a fixed sealed volume of air, by a volume change via an attached piston movement. By measuring the piston displacement using laser interferometer, the generated sound pressure can be calculated by multiplying the velocity of volume change by acoustic transfer impedance of a pistonphone. The main concern with this method is that sound pressure leakage from a gap adjacent to piston significantly changes the acoustic transfer impedance at lower frequencies. To apply the laser pistonphone method in the low-frequency range down to 10−2 Hz, it is essential to accurately evaluate and compensate for the leakage effect, i.e. the gap’s acoustic transfer impedance. In this study, we propose a technique for experimentally evaluating the gap impedance. The main idea is to determine the total acoustic transfer impedance by dividing the sound pressure by the volume velocity and then deducing the gap impedance by subtracting the chamber impedance from the obtained total acoustic transfer impedance. A digital pressure sensor was used to precisely measure sound pressure below 100 Hz because pressure sensors are suitable for accurate measurement of pressure fluctuations at low frequencies. We validated the proposed approach by calibrating an analog pressure sensor that outputs an analog voltage proportional to the absolute pressure. As a result, the sensitivity calibrated by the laser pistonphone method at 0.02 Hz agreed with the static sensitivity provided by the manufacturer within 0.02 dB.

Optoelectronic system for measuring the sugar content in citrus fruits

The color of oranges originates from carotenoids, which are natural pigments ranging from yellow to red color and is indicative of the sugar level and degree of ripeness. During the degreening process, the fruit quality can deteriorate, which is manifested as insufficient ripeness and sugar (sucrose) content. Therefore, in this study, we have designed a low-cost, easy-to-use optoelectronic system to measure the sugar content in orange and lemon juice. The system consists of a U-shaped plastic optical fiber (POF) and a visible multispectral sensor. The device identifies variation in the absorption of the evanescent field of the POF when it comes in contact with orange juice. The response of the multispectral sensor in the wavelength range of 450–650 nm is digitally captured, improving the efficiency and accuracy of the measurements. To validate the multispectral sensor measurements, the sugar contents are compared with those obtained using a visible spectrum analyzer and refractometer.

A Novel Approach to Light Detection and Ranging Sensor Placement for Autonomous Driving Vehicles Using Deep Deterministic Policy Gradient Algorithm

&lt;div&gt;This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms. By leveraging machine learning techniques, this research streamlines the sensor placement optimization process, enhancing the perception capabilities of autonomous driving vehicles. The optimized sensor configurations obtained from the DDPG algorithm lead to safer and more reliable autonomous driving systems, contributing to the advancement and widespread adoption of autonomous driving technology.&lt;/div&gt;