Sensor Concept Research Articles

Presenting the project SekQuaSens 3 : combining a networked sensor concept with model-based decisions for optimized energy flow in a district

Presenting the project SekQuaSens ³ : combining a networked sensor concept with model-based decisions for optimized energy flow in a district

Systematic design of 2D and 3D vibroacoustic fluidic cavity sensors using topology optimization

In this research we investigate a novel sensor concept, which utilizes a standing acoustic wave inside a liquid-filled cavity to probe volumetric properties of a fluid analyte. However, realizing a high-Q cavity resonator is a challenge because of low acoustic impedance contrast between liquids and solids. In our previous studies, we surround the cavity resonator with phononic crystal layers that provide a strong cavity resonance within the phononic band gap. The quality factor drastically depends on the geometry of the metamaterial lattice. Therefore, the main aim of this study is to find an optimal material layout of the solid domain around the cavity by employing topology optimization. We formulate the optimization problem as maximization of the Q-factor of the cavity resonance. We consider the sensor as a fully coupled vibroacoustic system, taking into account material losses and frequency-dependent material properties. Since resonance problems are very sensitive to minor variations in geometry, we apply a gray scale suppression constraint to minimize the influence of geometrical uncertainties and make the optimized designs suitable for additive manufacturing

SENSING MECHANISM AND APPLICATION OF MECHANICAL STRAIN SENSOR: A MINI-REVIEW

This study reviews the potential of flexible strain sensors based on nanomaterials such as carbon nanotubes (CNTs), graphene, and metal nanowires (NWs). These nanomaterials have excellent flexibility, conductivity, and mechanical properties, which enable them to be integrated into clothing or attached to the skin for the real-time monitoring of various activities. However, the main challenge is balancing high stretchability and sensitivity. This paper explains the basic concept of strain sensors that can convert mechanical deformation into electrical signals. Moreover, this paper focuses on simple, flexible, and stretchable resistive and capacitive sensors. It also discusses the important factors in choosing materials and fabrication methods, emphasizing the crucial role of suitable polymers in high-performance strain sensing. This study reviews the fabrication processes, mechanisms, performance, and applications of stretchable strain sensors in detail. It analyzes key aspects, such as sensitivity, stretchability, linearity, response time, and durability. This review provides useful insights into the current status and prospects of stretchable strain sensors in wearable technology and human–machine interfaces.

A Line Length Independent, Pseudo-Transmission Permittivity Sensor Basing on Dielectric Waveguides

This contribution introduces a novel, dielectric waveguide based, permittivity sensor. Next to the fundamental hybrid mode theory, which predicts exceptional wave propagation behavior, a design concept is presented that realizes a pseudo-transmission measurement approach for attenuating feed-side reflections. Furthermore, a transmission line length independent signal processing is introduced, which fosters the robustness and applicability of the sensor concept. Simulation and measurement results that prove the sensor concept and validate the high measurement accuracy, are presented and discussed in detail.

Faraday cage-type self-powered immunosensor based on hybrid enzymatic biofuel cell.

Self-powered immunosensors (SPIs) based on enzymatic biofuel cell (EBFC) have low sensitivity and poor stability due to the high impedance of the immune sandwich and the vulnerability of enzymes to environmental factors. Here, we applied the Faraday cage-type sensing mode on a hybrid biofuel cell (HBFC)-based SPI for the first time, which exhibited high sensitivity and stability. Cytokeratin 19 fragment (CYFRA 21-1) was used as a model analyte. Au nanoparticle-reduced graphene oxide (Au-rGO) composite was used as the supporting matrix for immunoprobe immobilized with detection antibody and glucose dehydrogenase (GDH), also the builder for Faraday cage structure on the bioanode in the presence of antigen. After the combination of immunoprobe, antigen, and the antibody on the bioanode, the Faraday cage was constructed in case the AuNP-rGO was applied as a conductive cage for electron transfer from GDH to the bioanode without passing through the poorly conductive protein. With the assistance of the Faraday cage structure, the impedance of the bioanode decreased significantly from 4000 to 300 Ω, representing a decline of over 90%. The sensitivity of the SPI, defined as the changes of open circuit voltage (OCV) per unit concentration of the CYFRA 21-1, was 68 mV [log (ng mL-1)]-1. In addition, Fe-N-C was used as an inorganic cathode material to replace enzyme for oxygen reduction reaction (ORR), which endowed the sensor with 4-week long-term stability. This work demonstrates a novel sensing platform with high sensitivity and stability, bringing the concept of hybrid biofuel cell-based self-powered sensor.

Data-driven soft sensors in refining processes – Pulp property estimation using ARX – models

This paper focuses on estimation of shives(wide) and fiber length in RGP82CD-refiners using an AutoRegressive eXogenous (ARX) structure in a data-driven soft sensor concept. Both external and internal variables are considered as model inputs. The pulp properties were sampled every 15 min from an on-line device positioned after the latency chest, whereas other process data were sampled every 6 seconds. Notably, despite the high data sampling rate, the development of robust models necessitated a dataset spanning over two months of process information. The external variables studied in this paper were specific energy, the sawmill chip content, plate gaps, and dilution water feed rates to each refining zone. Additional internal variables, such as the inlet flat zone temperature, the maximum temperature, and the periphery temperature in the conical zone, were also used as model inputs. It was concluded that both shives(wide) and fiber length can be estimated with relatively good accuracy although large uncertainties exist in the measured properties. Finally, it was shown that fast pulp property dynamics in the blow-line can be followed, which outperforms current practices of using pulp measurement devices positioned after the latency chest. This offers implementation of more advanced future pulp property control concepts.

Brillouin lasers in a graphene microresonator for multispecies and individual gas molecule detection

Optical microcavities offer a promising platform for highly efficient light–matter interactions. Recently, the combination of microresonators and 2D materials in nanoscale has further enriched the optoelectronics of the microcavity geometries, spurring broad advances ranging from lasers, nonlinear converters, modulators to sensors. Here, we report the concept of a Brillouin laser sensor, by depositing graphene on an over-modal microsphere. Driven by a single continuous-wave pump at 1550 nm, multiple Brillouin lasers from distinct mode families are co-generated in a single device. The Brillouin lasers excited in the high Q cavity produce heterodyne beating notes with phase noise down to −161 dBc/Hz at 1 MHz offset, not only enabling label-freely identifiable detection of multispecies gas molecules adsorbed on the graphene in situ but also rendering it possible to trace individual molecules. Such a combination of graphene optoelectronics and Brillouin lasers in microcavities demonstrates a novel physical paradigm and offers insights into powerful tools for fast and precise optical sensing.

Universal protocol for the wafer-scale manufacturing of 2D carbon-based transducer layers for versatile biosensor applications

In this manuscript, we present a comprehensive fabrication protocol for high-performance graphene oxide (GO) sensor concepts. It is suitable for a variety of biosensing applications and contains the essential process steps, starting with vapor phase evaporation for siloxane monolayers, followed by spin-coating of GO as a nanometer-thin transducer with exceptional homogeneity and micromechanical surface methods which enable seamless transformation of GO transducers to be desired micro and nano dimensions.In addition to linking basic research and innovative sensor concepts with an outlook for commercial applications of point-of-care systems for early-stage diagnostics, the authors consider it necessary to take a closer look at the manufacturing processes to create more transparency and clarity, to manufacture such specific sensor concepts systematically. The detailed manufacturing approaches are intended to motivate practitioner to explore and improve this GO-based key technology.This process development is illustrated below using the manufacturing methods for three types of sensors, namely sensors based on i) surface plasmon resonance spectroscopy (SPR), ii) impedance spectroscopy and iii) bio-field effect transistors (ISFETs).The obtained results in this work prove successful GO sensor productions by achieving:•Uniform and stable immobilization of GO thin films,•High yield of sensor units on a wafer scale, here up to 96 %,•Promising integration potential for various biomedical sensor concepts to early-stage diagnostic.

Location and Tracking Mode of Sports Rehabilitation Training With Self-Powered Sensors Based on Particle Swarm Optimization Algorithm

With the growth of the country’s economic strength, people’s living standards have steadily improved. Sports have become the main means to change physical fitness and maintain the per capita health index. The state also puts forward higher requirements for the security work of athletes, elderly groups and rehabilitation patients. Traditional rehabilitation training mainly relies on various sensor devices, such as acceleration sensing, pressure sensing and so on. These devices need a lot of energy to supply power for the equipment in the process of use, which is no longer suitable for today’s modern development environment. Based on the concept of self-powered sensors, this paper proposes a sports rehabilitation training model updated by particle swarm optimization algorithm. First of all, the performance and indicators of self-powered sensor devices are analyzed, and the self-powered electrical components are optimized using friction nano new energy technology. Starting from the basic characteristics of wearable models, we can ensure the consistency of posture in sports rehabilitation. During training, the wearing process will stretch 10% of the length in advance to ensure comfort and sensitivity. According to the results of the test record, the pulse index is clear and obvious, consistent with the actual number of minutes per minute. Finally, the sensor array model and function model are improved under the particle swarm optimization algorithm to realize the positioning and tracking function in sports rehabilitation training. The results show that the wearable training equipment optimized by particle swarm optimization can better locate and track the movement track than the traditional sports rehabilitation equipment. It can adjust the movement posture and protect the safety of trainers. At the same time, it is more prominent in performance and power supply duration.

Learning structural stress virtual sensors from on-board instrumentation of a commercial aircraft

This work aims to predict the mechanical stress on the structure of a business jet in service phase from flight instrument only. A significant database obtained from test flights using aircraft instrumented with strain gauges has been provided as part of an Artificial Intelligence challenge organized by the French Ile-de-France region and the aircraft manufacturer Dassault Aviation. Learning techniques are considered to train a prediction model of the aircraft structural stress. The proposed baseline includes a clustering step for phase identification in time series and an ensemble model with two stacked regressors. The model is trained on a dataset of 117 flights and its overall performance is evaluated on a validation set of flight sequences from 186 flights. The main advantages of the proposed learning approach are its prediction accuracy, its training frugality and its interpretability. This paper presents a global data science workflow applied to a problem of structural stress prediction. Despite a development in a constrained time period with no direct access to the data, the proposed approach demonstrates the feasibility of the concept of learned virtual sensor of aircraft structural stress and paves the way for applications to other structures.

High-stretchability and low-hysteresis strain sensors using origami-inspired 3D mesostructures.

Stretchable strain sensors are essential for various applications such as wearable electronics, prosthetics, and soft robotics. Strain sensors with high strain range, minimal hysteresis, and fast response speed are highly desirable for accurate measurements of large and dynamic deformations of soft bodies. Current stretchable strain sensors mostly rely on deformable conducting materials, which often have difficulties in achieving these properties simultaneously. In this study, we introduce capacitive strain sensor concepts based on origami-inspired three-dimensional mesoscale electrodes formed by a mechanically guided assembly process. These sensors exhibit up to 200% stretchability with 1.2% degree of hysteresis, <22 ms response time, small sensing area (~5 mm2), and directional strain responses. To showcase potential applications, we demonstrate the use of distributed strain sensors for measuring multimodal deformations of a soft continuum arm.

Trace identification of cysteine enantiomers based on an electrochemical sensor assembled from CuxS@SOD zeolite

The nonchiral sensor concept based on a sodalite (SOD) zeolite loaded CuxS (CuxS@SOD) catalyst is proposed as a sensing platform for chiral cysteine (Cys) determination. Chiral Cys is analyzed by the difference of binding capacity between CuxS catalysts. The observed current in amperometric i-t curve (A i-t C) is always positive for the L-cysteine (L-Cys), while it is negative for the D-cysteine (D-Cys). Under differential pulse voltammetry (DPV) method, the characteristic current peak for the CuxS@SOD moves to right (positive potential position) with the addition of L-Cys while it moves to left (negative potential direction) with the addition of D-Cys, respectively. Cyclic voltammetry (CV) is consistent with DPV and discusses the diffusion control mechanism. In this work, the ultra-trace determination of cysteine enantiomers reaches the limit of detection (LOD) of 0.70 fM and 0.60 fM by the highly efficient CuxS catalyst restrained in the nanosized SOD zeolite cages of the opening window pores, respectively. The sensor opens up a novel potential prospect for achiral composite in the field of chiral recognition through electrochemical methods with extra-low concentration.

Triboelectric Film Sensor for Integrity Monitoring of Bolted Joints

In this study, a concept and design of a self-powered sensor that utilizes a triboelectric effect to evaluate the condition of tensile bolted joints was proposed. Based on the fact that the triboelectric charge yields electrostatic voltage induced by the separation of the contacting rough surfaces, the proposed sensor is a film-shaped triboelectric sensor made of inexpensive materials being installed between the objects to be fastened. The principle of the sensor is that it detects microscale relative motions between the contacting surfaces against an external vibratory load when the integrity of the fastened joint is compromised due to a decrease in the bolt’s fastening force. In this study, we designed and fabricated triboelectric sensor and tested it on a tensile bolted joint specimen subjected to inertial vibratory loading, and it was experimentally shown that the output voltage amplitude of the sensor increased as the bolt’s fastening force decreased. In addition, a modeling study was performed to explain the unexpected decrease in voltage amplitude observed at medium preloads, by combining the triboelectric and mechanical models with the experimental results of two different external circuit configurations. Estimation of the triboelectric charge density at the contacting surfaces was performed, which was found to be consistent with the contact mechanics model assumed. Finally, the calculation of the sensor output voltage based on the presented mechanical/triboelectric model was provided, confirming the validity of the modeling study.

On-orbit optical detection of lethal non-trackable debris

Resident space objects in the size range of 0.1 mm–3 cm are not currently trackable but have enough kinetic energy to have lethal consequences for spacecraft. The assessment of small orbital debris, potentially posing a risk to most space missions, requires the combination of a large sensor area and large time coverage. For example, a sensor with a time•area product of 3 m2-years is considered sufficient to make a significant contribution to our understanding of the near-Earth small debris population. Deploying large sensors, however, is generally resource intensive, due to their size and weight. The lightsheet sensor concept, allows the creation of a “virtual witness plate”, which is created without any supporting physical structure and therefore presents an attractive opportunity for the detection small debris anywhere between low Earth orbit and interplanetary space. This sensor project is called Lightsheet Anomaly Resolution And Debris Observation (LARADO). Recent technology maturation efforts in the laboratory have successfully detected small debris (1.6 mm diameter) moving at 6.38 km/s. NRL is building the NASA funded the LARADO instrument as a technology maturation effort for a flight demonstration on STPSat-7 in 2025. In this presentation, we will describe the LARADO instrument, present the laboratory data and analysis, and describe the instrument details for the STPSat-7 spacecraft slated for launch via the DoD Space Test Program in CY2025.

Long-term performance validation of NH3 concentration prediction model for virtual sensor application in livestock facility

Livestock facilities commonly generate NH3, a hazardous substance that may also harm livestock. Therefore, monitoring of NH3 concentrations in livestock facilities is necessary to ensure proper control. However, NH3 is alkaline and toxic, causing corrosion inside detection sensors and making monitoring difficult. This study proposes a virtual sensor concept to complement the durability of NH3 physical sensors. The study also conducts a long-term performance validation of a data-driven NH3 concentration prediction model. Results indicate that the model's prediction performance declines sharply when the data generation pattern inside the livestock facility changes due to changes in outdoor conditions and facility operation. Furthermore, the prediction performance of the model differed depending on the training data period settings when updating the model. Hence, the model needs versioning and update management to respond to the data generation pattern in the livestock facility when operating the NH3 concentration virtual sensor. The virtual sensor is expected to enhance monitoring and reduce sensor management costs in livestock facilities.

Dynamic Mixed Modeling in Large Eddy Simulation Using the Concept of a Subgrid Activity Sensor

Following the relative success of mixed models in the Large Eddy Simulation of complex turbulent flow configurations, an alternative formulation is suggested here which incorporates the concept of a local subgrid activity sensor. The general idea of mixed models is to combine the advantages of structural models (superior alignment properties), usually of the scale similarity type, and functional models (superior stability), usually of the eddy viscosity type, while avoiding their disadvantages. However, the key question is the mathematical realization of this combination, and the formulation in this work accounts for the local level of underresolution of the flow. The justification and evaluation of the newly proposed mixed model is based on a priori and a posteriori analysis of homogeneous isotropic turbulence and laminar–turbulent transition in the Taylor–Green vortex, respectively. The suggested model shows a robust and accurate behavior for the cases investigated. In particular, it outperforms the separate structural and functional base models as well as the simulation without an explicit subgrid-scale model.

System for automated monitoring of local soil removal during cleaning in closed food processing lines with a quartz crystal sensor

Using quartz crystal microbalances (QCM) to monitor cleaning processes creates new opportunities towards efficient and need-based cleaning processes. A first study with starch as food model soiling showed the suitability of a QCM-based sensor concept to detect swellable substances and monitor the cleaning process [1]. The results of a follow-up study will be presented in this manuscript. Cleaning tests were conducted at 40 and 55 °C while monitoring a range of characteristics (peak height, attenuation, integral, tangent incline, turning point) in the sensor signal course. An optical sensor, monitoring the cleaning process was used as a reference. Based on this, the most promising parameters (peak height, attenuation, tangent incline) were selected to develop an evaluation algorithm counteracting the sensor cross-sensitivity towards process parameters such as temperature and influences of the cleaning fluid. The algorithm can determine whether the sensor surface is clean or soiled. Cleaning tests with tomato paste and milk proved the sensor’s ability to detect industrial food products with an average deviation of 68,0 s (tomato paste) and 43,8 s (milk) from the reference sensor.

Wireless, Material-Integrated Sensors for Strain and Temperature Measurement in Glass Fibre Reinforced Composites.

Fiber reinforced plastics (FRP) offer huge potentials for energy efficient applications. Special care must be taken during both FRP fabrication and usage to ensure intended material properties and behavior. This paper presents a novel approach for the monitoring of the strain and temperature of glass fibre reinforced polymer (GFRP) materials in the context of both production process monitoring and structural health monitoring (SHM) applications. The sensor is designed to be integrated into GFRPs during the production process, and the sensor concept includes possibilities of automated placement during textile layup. To minimize sensor impact on GFRP integrity and to simplify vacuum setup and part handling, the sensor operates without the need for either wires or a battery. In the first sections of this work, sensor concept, design and prototype fabrication are presented. Subsequently, it is shown how the sensors can be used for flow front monitoring and cure estimation during GFRP production by measuring local resin temperature. The resulting specimens are then characterized regarding strain measurement capabilities, mechanical influence on the host component and overall system limitations. Average strain sensor accuracy is found to be ≤0.06 mm/m, while a maximum operation temperature of 126.9 °C and a maximum reading distance of 38 mm are measured. Based on a limited number of bending tests, no negative influence of sensor presence on breaking strength could be found. Possible applications include structural components, e.g., wind turbine blades or boat hulls.

Opto-mechanical fiber sensing with optical and acoustic cladding modes

Optical fibers are an excellent sensor platform. However, the detection and analysis of media outside the cladding and coating of standard fibers represent a long-standing challenge: light that is guided in the single optical core mode does not reach these media. Cladding modes help work around this difficulty, as their transverse profiles span the entire cross-section of the fiber cladding and reach its outer boundary. In this tutorial, we introduce and discuss in detail two recent advances in optical fiber sensors that make use of cladding modes. Both concepts share optomechanics as a common underlying theme. First, we describe a spatially continuous distributed analysis using the optical cladding modes of the fiber. Light is coupled to these modes using Brillouin dynamic gratings, which are index perturbations associated with acoustic waves in the core that are stimulated by light. Unlike permanent gratings, which are routinely used to couple light with cladding modes, Brillouin dynamic gratings may be switched on and off at will and can be confined to short fiber sections at arbitrary locations in a random-access manner. Second, we present the extension of the cladding mode sensor concept to include acoustic rather than optical modes. The acoustic cladding modes may be stimulated and monitored by guided light in the single core mode, and their linewidths are modified by the elastic properties of surrounding media. The principles and analyses of both concepts are provided in detail, alongside examples of experimental setups and results.

Catadioptric sensor concept with interlaced beam paths for imaging and pinpoint spectroscopy.

This paper presents the concept, optical design, and implementation of a catadioptric sensor for simultaneous imaging of a scene and pinpoint spectroscopy of a selected position, with object distances ranging from tens of centimeters to infinity and from narrow to wide adjustable viewing angles. The use of reflective imaging elements allows the implementation of folded and interlaced beam paths for spectroscopy and image acquisition, which enables a compact setup with a footprint of approximately 90m m×80m m. Although the wavelength range addressed extends far beyond the visible spectrum and reaches into the near infrared (∼400n m to 1000 nm), only three spherical surfaces are needed to project the intermediate image onto the image detector. The anamorphic imaging introduced by the folded beam path with different magnification factors in the horizontal and vertical directions as well as distortion can be compensated by software-based image processing. The area of the scene to be spectrally analyzed is imaged onto the input of an integrated miniature spectrometer. The imaging properties and spectroscopic characteristics are demonstrated in scenarios close to potential applications such as product sorting and fruit quality control.