Sensor Concept Research Articles

QD/SnO2 Photoactivated Chemoresistive Sensor for Selective Detection of Primary Alcohols at Room Temperature

Sensors based on nanocomposites of quantum dots (QDs) and wide-gap metal oxides are of exceptional interest for photoactivated detection of toxic and pollutant gases without thermal heating. However, the class of detecting gases has been limited almost exclusively to oxidizing gases like NO2. Here, we designed a photoactivated sensor for the selective detection of primary alcohols at room temperature using CdSe quantum dots coupled to a wide-gap SnO2 semiconductor matrix. Our concept of the sensor operations is based on the photochemical reaction of primary alcohols via photoactivated QD-SnO2 charge transfer and does not involve chemisorbed oxygen, which is traditional for the operation of metal oxide sensors. We demonstrated an efficient sensor response to C1–C4 primary alcohols of ppm concentration under photoexcitation with a yellow LED in the absence of a signal from other volatile organic compounds (VOCs). We believe that proposed sensor concept opens up new ways to design photoactivated sensors without heating for the detection of VOCs.

Sensors on Flapping Wings (SOFWs) Using Complementary Metal–Oxide–Semiconductor (CMOS) MEMS Technology

This article presents a framework of using MEMS sensors to investigate unsteady flow speeds of a flapping wing or the new concept of sensors on flapping wings (SOFWs). Based on the implemented self-heating flow sensor using U18 complementary metal–oxide–semiconductor (CMOS) MEMS foundry provided by the Taiwan Semiconductor Research Institute (TSRI), the compact sensing region of the flow sensor was incorporated for in situ diagnostics of biomimetic flapping issues. The sensitivity of the CMOS MEMS flow sensor, packaged with a parylene coating of 10 μm thick to prolong the lifetime, was observed as −3.24 mV/V/(m/s), which was below the flow speed of 6 m/s. A comprehensive investigation was conducted on integrating CMOS MEMS flow sensors on the leading edge of the mean aerodynamic chord (m.a.c.) of the flexible 70-cm-span flapping wings. The interpreted flow speed signals were checked and demonstrated similar behavior with the (net) thrust force exerted on the flapping wing, as measured in the wind tunnel experiments using the force gauge. The experimental results confirm that the in situ measurements using the concept of SOFWs can be useful for measuring the aerodynamic forces of flapping wings effectively, and it can also serve for future potential applications.

Sub-100 Hz intrinsic linewidth 852 nm silicon nitride external cavity laser.

We demonstrate an external cavity laser with intrinsic linewidth below 100 Hz around an operating wavelength of 852 nm, selected for its relevance to laser cooling and manipulation of cesium atoms. This system achieves a maximum CW output power of 24 mW, a wavelength tunability over 10 nm, and a side-mode suppression ratio exceeding 50 dB. This performance level is facilitated by careful design of a low-loss integrated silicon nitride photonic circuit serving as the external cavity combined with commercially available semiconductor gain chips. This approach demonstrates the feasibility of compact integrated lasers with sub-kHz linewidth centering on the needs of emerging sensor concepts based on ultracold atoms and can be further extended to shorter wavelengths via selection of suitable semiconductor gain media.

Improving Freight Train Wheel Monitoring with Smart Sensors

Onboard monitoring of freight car axleboxes enhances safety, reduces maintenance costs, and improves track conditions by preventing secondary damage. Installing wireless sensors on freight cars without a nearby power source should be cost-effective, given the large quantities involved. To address this, a new wireless smart sensor node has been deployed. The sensor automatically recognizes stable operating conditions, detects wheel rotational speed from vibrations, performs real-time condition monitoring, and transmits the results to the cloud. This study outlines the smart sensor concept and the pilot field test conducted with real freight cars. The results demonstrate the ability to estimate wheel rotational speed from vibrations and the potential for detecting wheel out-of-roundness (OOR) using a newly developed condition indicator for low-power real-time operations.

Sensor concepts for gear damage detection in wind power drives

Abstract Gearbox failure in offshore wind turbines can be caused by fatigue damages like tooth fank fracture (TFF) or pitting damage. For damage detection in current applications, simple threshold values for vibration amplitudes are used to initiate a shutdown of the wind turbine. At this time significant damage to the gear is already present and an external maintenance is inevitable. The objective of this experimental study is to determine a better sensor concept for detection of gear damage in wind power drives during operation. The damage needs to be detected at a very early stage before the health condition of the gears becomes critical. Evaluation criteria for comparison of sensor concepts include the earliest possible detection of damage, robust and reliable detection under varying operating conditions, as well as practicality and cost-effectiveness. The test results show, vibration measurement in radial direction and in high frequency ranges is most suitable and most robust for pitting detection during operation. With the best sensor concept in place, it is possible to detect gear damage like pitting at a very early stage of 0.3 % pitting size.

Fabrication and characterization of inkjet-printed interdigitated electrodes for non-faradaic electrochemical detection of uromodulin in urine

This work describes the simulation, design, and fabrication of an inkjet-printed impedance biosensor for uromodulin which is a urine biomarker for kidney disease. The interdigitated electrodes (IDE) were fabricated through inkjet printing of silver ink on polyethylene terephthalate (PET) substrate and subsequently passivated with SU-8 for non-faradaic measurements. We functionalized the passivated IDE with biotin-streptavidin complex and uromodulin antibody to detect uromodulin (6.75 μg/ml-100 μg/ml). The measured impedance due to uromodulin immobilized from artificial urine samples showed a 27.4 % change in impedance magnitude per μg/ml of uromodulin at 150 Hz (log scale). The limit of detection achieved is 25 ng/ml. The sensor demonstrated specificity to uromodulin when measured against albumin, which is another protein biomarker present in the urine. These results highlight the possibility of implementing non-faradaic impedance measurements using a passivated IDE as the sensor element. It also promotes the sensor fabrication method of inkjet printing. The sensor concept shows the implementation potential of IDE as a single-use-sensor for point-of-care applications.

Design & development of adulteration detection system by fumigation method & machine learning techniques

A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40–50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food.

On the Origin of Signal and Bandwidth of Converse Magnetoelectric Magnetic Field Sensors

AbstractConverse magnetoelectric sensors enable the detection of low‐frequency and low‐amplitude magnetic fields over a bandwidth of several kilohertz by combining the electrical excitation of a magnetoelectric resonator via a piezoelectric layer with an inductive readout. Here, a comprehensive sensor model is presented to further foster the development of this promising sensor concept. The model relates the output signal to the device characteristics, taking into account the magnetoelastic and electromechanical properties, the resonator geometry, and operating conditions. The sensor system is thoroughly experimentally analyzed to validate the model. Based on the analysis, the sensor concept is explained in detail, including the origin of its loss and bandwidth and their connection with the magneto‐mechanical loss in the magnetostrictive layer. Significant advances have been made in the comprehensive understanding of converse magnetoelectric sensors, providing a solid basis for future improvements in magnetoelectric sensor systems.

Optical Fiber‐Based Wearable Sensors for Remote Health Monitoring [Invited

AbstractThe wearable optical fiber sensors have demonstrated significant promise in the realm of health monitoring in recent times. These sensors utilize the flexibility and exceptional sensitivity of optical fibers to precisely measure many physiological aspects of the human body, including heart rate, breathing rate, mobility status, and body temperature. Optical fiber sensors usually have good biocompatibility and anti‐interference capabilities, can be integrated into flexible materials, and are suitable for long‐term wear. It can be integrated into clothing, patches, or accessories to provide continuous, real‐time health data monitoring, providing important support for personalized medicine and remote health management. This paper primarily presents the fundamental operating concept of wearable optical fiber sensors and their use in monitoring physiological signals across multiple domains. In conclusion, this paper provides a summary of the limitations and future prospects of wearable fiber sensors using optical fiber technology.

3D Light-Direction Sensor Based on Segmented Concentric Nanorings Combined with Deep Learning.

High-precision, ultra-thin angular detectable imaging upon a single pixel holds significant promise for light-field detection and reconstruction, thereby catalyzing advancements in machine vision and interaction technology. Traditional light-direction angle sensors relying on optical components like gratings and lenses face inherent constraints from diffraction limits in achieving device miniaturization. Recently, angle sensors via coupled double nanowires have demonstrated prowess in attaining high-precision angle perception of incident light at sub-wavelength device scales, which may herald a novel design paradigm for ultra-compact angle sensors. However, the current approach to measuring the three-dimensional (3D) incident light direction is unstable. In this paper, we propose a sensor concept capable of discerning the 3D light-direction based on a segmented concentric nanoring structure that is sensitive to both elevation angle (θ) and azimuth angle (ϕ) at a micrometer device scale and is validated through simulations. Through deep learning (DL) analysis and prediction, our simulations reveal that for angle scanning with a step size of 1°, the device can still achieve a detection range of 0∼360° for ϕ and 45°∼90° for θ, with an average accuracy of 0.19°, and DL can further solve some data aliasing problems to expand the sensing range. Our design broadens the angle sensing dimension based on mutual resonance coupling among nanoring segments, and through waveguide implementation or sensor array arrangements, the detection range can be flexibly adjusted to accommodate diverse application scenarios.

High‐Performance Multipedal Shape Strain Sensors for Human Motion and Electrophysiological Signal Monitoring

AbstractStrain sensors from conducting polymer hydrogel have been widely employed in various wearable devices, electronic skins, and biomedical applications. These sensors provide outstanding flexibility and high sensitivity by integrating conducting polymer with hydrogels, making them particularly suitable for monitoring human motion and physiological signals like heart rate or muscle activity. Despite their extensive application potential, conducting polymer hydrogel face several technical challenges in practical use, including poor mechanical properties, lack of long‐term stability, and difficulty in customizable design. This work introduces a method for fabricating a multipedal strain sensor using poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS)/polyvinyl alcohol (PVA) dimethyl sulfoxide (DMSO)hydrogels through screen printing and demonstrates its application in human motion monitoring. The multipedal strain sensor demonstrates a low Young's modulus (200 kPa), high stretchability (400%), and excellent mechanical cyclic stability (3000 cycles). Furthermore, this strain sensor is further applied to detect human movements such as chewing, smiling, fist clenching, arm bending, and carotid pulse monitoring. Comparative analysis between the multipedal‐designed sensor and the non‐designed sensor highlights the enhanced sensing capabilities of the multipedal sensor. The design of this multipedal sensor holds the potential to broaden the design concepts for strain sensors and offers new insights for wearable devices and electronic skins.

Optimization design and key-term separation identification of parallel six-dimensional pose sensor with high sensitivity and high precision

The integrated bearing-positioning parallel manipulator is an important mean for ground testing of space optical telescope before being launched into space. To ensure positioning accuracy, the manipulator requires a pose sensor for the end-effector measurement with six degrees of freedom (DOF), real-time, high-precision, and operation in a vacuum environment. However, it is challenging for the current pose sensor to simultaneously meet all these requirements. In this paper, a novel concept of six-dimensional pose sensor based on parallel mechanism is proposed. A new mechanism design approach is presented to achieve the large measurement range, real-time measurement performance, and high accuracy measurement based on performance atlas. First, the GF sets synthesis is developed to optimize the configuration of pose sensor. Next, two new design indicators are proposed to evaluate the real-time and high accuracy performance. Mechanism parameters are optimized by combining with mechanism singularity analysis. To guarantee high measurement accuracy, a key-term separation identification method with double neural networks is presented. Experiments on 6-UPS pose sensor demonstrate the effectiveness of the proposed mechanism design and key-term identification approach.

Effect of oxygen content in CuO x nanofilms on chloride ion detection for ion sensor applications

Sensors for detecting chloride ions have been required for routine monitoring of industry and human health. This study proposes a concept of an ion sensor based on CuO x nanofilms with different oxygen contents. The CuO x -based sensors exhibited an increase in DC current for those with low oxygen content and a decrease for those with high oxygen content following exposure to a chloride ion solution. AC impedance analysis suggested differential reactions of chloride ions in the bulk and surface regions of CuO x , dependent on the oxygen content. For the CuO x -based sensors with a ratio of 0.78 oxygen to copper atoms at chloride ion concentrations of 10−1000 ppm, the sensitivity in the bulk region calculated from AC impedance was 61−2926, which was higher than the sensitivity of 1.3−2.6 calculated from DC impedance. Finally, CuO x -based sensors demonstrated identifiability for chloride ions compared to sodium and calcium ions.

Wavefront sensing with a gradient phase filter

Wavefront sensors have now become core components in the fields of metrology of optical systems, biomedical optics, and adaptive optics systems for astronomy. However, none of the designs used or proposed so far achieve simultaneously a high spatial resolution at the pupil of the tested optics and absolute measurement accuracy comparable to those of modern laser-interferometers. This paper presents an improved wavefront sensor concept that reaches both previous goals. This device named Crossed-sine phase sensor (CSPS) is based on a fully transparent gradient phase filter (GPF) placed at an intermediate location between the virtual pupil and image planes of the tested optics. The theoretical principle of the sensor is described in Fourier optics formalism. Numerical simulations confirm that a measurement accuracy of λ/100 RMS is achievable. The CSPS also offers the advantages of being quasi-achromatic and working on spatially or spectrally extended, natural or artificial light sources.

High responsive Pd decorated low temperature α-MoO3 hydrogen gas sensor

The H2 gas adsorption properties, which are the most crucial concept for a gas sensor, of the 2-dimensional (2D) MoS2 are limited compared to its oxide form of 2D α-MoO3. On the other hand, it is straightforward to form high surface-to-volume ratio nanostructures with MoS2. In order to get the benefits of the large surface and better H2 adsorption properties for the H2 gas sensor, MoS2 film grown by the chemical vapor deposition (CVD) method was converted into MoO3 with a thermal oxidation process at 380 °C under oxygen-ambient conditions. The grown MoS2 film has indicated that it is formed from the coalesced MoS2 flakes. An ultra-high response of 3 × 106 at a relatively low temperature of 100 °C was achieved for as low as 800 ppm hydrogen concentration. The hydrogen gas sensing mechanism has been discussed in depth using the double injection model, similar to the electrochromic mechanism.

Simulation-based data reduction and data processing for sheet metal forming in the hybrid twin framework

In sheet metal forming, the interaction between virtual models and the real world remains challenging. Process simulations can exhibit significant errors, and reliable measurements are often scarce during early production stages. This study presents a hybrid twin framework that systematically unifies computer-aided design, simulation, and measurement data in an adaptive manner. Central to this framework is a reverse engineering algorithm that reconstructs and transforms the geometry of deep-drawn components from optical scan data into B-spline surfaces. The algorithm demonstrated high precision, indicating its suitability for process control and geometric analysis. The hybrid twin framework integrates virtual data from simulations and real-world data, as evidenced by a sensor concept for inline surface measurement. The framework ensures robust and redundant measurement concepts by estimating complete geometries from a few systematically preselected measuring points. This adaptive approach permits continuous updates and extensions to the database, accommodating both sparse inline signals and offline inspection data. This framework provides a conceptual model for integrating direct feedback interactions between virtual and physical environments, thereby enhancing the precision of analytical and predictive models in sheet metal forming processes.

Implementation of an intelligent process monitoring system for screw presses using the CRISP-DM standard

AbstractIncreasing the service life and process reliability of systems plays an important role in terms of sustainable and economical production. Especially in the field of energy-intensive bulk forming, low scrap rates and long tool lifetimes are business critical. This article describes a modular method for AI-supported process monitoring during hot forming within a screw press. With this method, the following deviations can be detected in an integrated process: the height of the semi-finished product, the positions of the die and the position of the semi-finished product. The method was developed using the CRISP-DM standard. A modular sensor concept was developed that can be used for different screw presses and dies. Subsequently a hot forming-optimized test plan was developed to examine individual and overlapping process deviations. By applying various methods of artificial intelligence, a method for process-integrated detection of process deviations was developed. The results of the investigation show the potential of the developed method and offer starting points for the investigation of further process parameters.

Proof of concept for a new sensor to monitor marine litter from space

Worldwide, governments are implementing strategies to combat marine litter. However, their effectiveness is largely unknown because we lack tools to systematically monitor marine litter over broad spatio-temporal scales. Metre-sized aggregations of floating debris generated by sea-surface convergence lines have been reported as a reliable target for detection from satellites. Yet, the usefulness of such ephemeral, scattered aggregations as proxy for sustained, large-scale monitoring of marine litter remains an open question for a dedicated Earth-Observation mission. Here, we track this proxy over a series of 300,000 satellite images of the entire Mediterranean Sea. The proxy is mainly related to recent inputs from land-based litter sources. Despite the limitations of in-orbit technology, satellite detections are sufficient to map hot-spots and capture trends, providing an unprecedented source-to-sink view of the marine litter phenomenon. Torrential rains largely control marine litter inputs, while coastal boundary currents and wind-driven surface sweep arise as key drivers for its distribution over the ocean. Satellite-based monitoring proves to be a real game changer for marine litter research and management. Furthermore, the development of an ad-hoc sensor can lower the minimum detectable concentration by one order of magnitude, ensuring operational monitoring, at least for seasonal-to-interannual variability in the mesoscale.

Ultrastretchable Segmented Sensors for Functional Human-Machine Interfaces.

The key feature that enables soft sensors to shorten the performance gap between robots and biological structures is their deformability, coupled with their capability to measure physical changes. Robots equipped with these sensors can interact safely and proprioceptively with their environments. This has sparked interest in developing novel sensors with high stretchability for application in human-robot interactions. This study presents a novel ultrasoft optoelectronic segmented sensor design capable of measuring strains exceeding 500%. The sensor features an ultrastretchable segment physically joined with an asymmetrically configured soft proprioceptive segment. This configuration enables it to measure high strain and to detect both the magnitude and direction of bending. Although the sensor cannot decouple these types of deformations, it can sense prescribed motions that combine stretching and bending. The proposed sensor was applied to a highly deformable scissor mechanism and a human-robot interface (HRI) device for a robotic arm, capable of quantifying parameters in complex interactions. The results from the experiments also demonstrate the potential of the proposed segmented sensor concept when used in tandem with machine learning, affording new dimensions of proprioception to robots during multilayered interactions with humans.

Sensitivity enhancement in erbium-doped fiber laser intra-cavity SPR sensor

To enhance sensitivity of fiber-based surface plasmon resonance (SPR) sensor for refractive index detection, we propose a concept of fiber laser intra-cavity SPR sensor that is constructed by placing a hollow fiber-based SPR sensor in an erbium-doped fiber laser cavity. The hollow fiber-based SPR sensor is firstly designed and optimized to achieve maximum amplitude sensitivity of 65.576 RIU−1 in a detection range of 1.6–1.61 at the operation wavelength of 1530 nm, and then placed in the erbium-doped fiber laser cavity to form the fiber laser intra-cavity SPR sensing system. The relationships between the detection sensitivity, pump power, and SPR sensor length of the laser intra-cavity SPR sensor are investigated in detail by using three-level rate equations, and the numerical results demonstrate that the sensitivity is enhanced 82 times, achieves 5375.627 RIU−1 when the fiber-based SPR sensor is placed in the laser cavity, and can be further enhanced by tuning the pump power to close to threshold value. The work described here demonstrates a possible solution to enhance the sensitivity by combining the fiber-based SPR sensor and the fiber laser.