membrane, is made, we matched it better with the materials used to capture target chemicals. This makes the sensor easier to build and more reliable. Our work shows how choosing the right membrane material can greatly improve performance, opening the way for broader applications in detecting pollutants.
The recent bridged EGFET (extended-gate field effect transistor) sensor design is the most user-friendly potentiometric transducer concept to date. Manufacturing of the sensor, the introduction of a sensitised phase transfer membrane by in situ casting, and transduction of the electric potential resulting from analyte–sensitiser binding are remarkably simple. However, so far, the immobilisation of the sensitiser has only been demonstrated within an agar hydrogel phase transfer membrane, the same material used for the so-called bridge that defines the concept. Here, we demonstrate in situ casting of a plasticised polyvinyl chloride (PVC) membrane onto the agar bridge as an alternative. We compare the performance of different types of sensitisers – an organic dye and an ion-exchanging clay – for the same target analyte, Cr(VI) oxyanions, when the sensitiser is immobilised in either an agar hydrogel or a plasticised PVC membrane. We find superior performance, as quantified by sensor response at the Cr(VI) maximum contaminant limit, when the membrane and processing solvent match the solubility of the sensitiser. We unite the simplicity of the bridged EGFET, the convenience of in situ membrane casting, and the performance advantage of organic-solvent-processed phase transfer membranes for organic sensitisers.
The first choice in science and industry to image surfaces down to the sub-nm range are scanning electron microscopy (SEM) and atomic force microscopy (AFM). Both techniques have specific disadvantages which can be compensated by the other method. Therefore, the implementation of AFM inside an SEM vacuum chamber provides the user with the best of both worlds. When operated under vacuum, AFM cantilevers have larger Q factors than in air and thus a lower scanning speed. In this work, an electrical circuit and a piezoelectrically driven micro-electromechanical system (MEMS) cantilever is developed to tune the Q factor of the cantilever using a feedback system, with the goal of replacing air damping. In doing so, it is demonstrated that AFM measurements in vacuum with scanning speeds as under ambient air pressure are feasible. The cantilever features an electrically driven integrated piezoelectric transducer, which is used to excite the oscillation while the piezoelectric current serves as a feedback signal for a closed-loop feedback approach. In vacuum, the Q factor is reduced by a factor of 4. Hence, the cantilever oscillation and step response show a damping behaviour equivalent to an operation in air.
An algorithm for adaptive accuracy enhancement of lateral position sensing based on quadrature spatio-temporal modulation is presented, and its application in a prototype micropower optical position sensor with simultaneously firing infrared emitters is reported. Substantial (4×) improvement in measurement accuracy over a basic detection method has been observed using an automated test stand where partial incapacity on one of the emitter channels has been simulated.
This article investigates methods for sampling volatiles on a compounding extruder to enable the development of a quasi-continuous automated sampling and measurement system for odorous contaminants. A first prototype of a bespoke extraction system was presented in earlier work, comprising four sequential cooling traps and one subsequent sorption trap. This setup allows us to obtain samples from the vacuum degassing port of a Coperion ZSK extruder. Preliminary results indicated that samples contain a plethora of odor-active compounds. This study assesses samples from the processing of post-consumer recycled polypropylene by means of gas chromatography–mass spectrometry/olfactory detection (GC–MS/O), focusing on comparing the composition of condensed and adsorbed samples. The results give an overview of the degassing atmosphere, listing more than 108 volatile compounds including odorants. Qualitative comparison of the sampling techniques indicates significant fractioning between condensates and adsorbates, which is illustrated on an orientation plot for water solubility versus volatility. Based on the results, guidelines for the design of sampling units for automated use in the aspired online monitoring application are proposed to transfer a broad spectrum of relevant contaminants to an attached measurement system.
Wildlife-related traffic accidents represent a persistent hazard on rural roads in Germany and beyond. Current electronic wildlife warning systems typically monitor only very short distances and therefore cannot provide large-area coverage. This paper presents a novel multisensor approach that integrates radar and infrared (IR) technology into existing roadside delineators. Due to regulatory requirements, delineators are placed at intervals of 50 m on German country roads. Integrating sensors into these delineators thus provides a uniform infrastructure that can be utilised. The radial extension of the sensor range allows a monitoring zone to be formed along the road. We evaluate thermal infrared arrays and high-resolution 60 GHz radar sensors for range, resolution and robustness under varying environmental conditions. Field measurements in wildlife parks demonstrate that the system can reliably detect deer at distances of up to 30 m and evaluate their moving speed as well. Challenges such as ambient temperature effects, optical dispersion in IR detection and resolution limits are discussed. The results highlight the potential of multisensor systems to reduce wildlife accidents and improve road safety.
In times of digital transformation, machines are expected to operate exclusively using the International System of Units (SI), something that humans have not yet fully achieved. The same should include sensor systems feeding data to those machines. This requires an internationally authoritative database for converting the non-SI units that humans enter to SI units at the level of the human–machine interface or human–sensor interface.
Power transformers are an integral part of our electric power system. Parasitic direct currents in the alternating-current grid cause inefficient transformer operation and demand mitigation measures to protect the grid’s constituents. In particular, geomagnetically induced currents (GICs) arising from solar activity prove to be an unpredictable risk for grid operators. Within this paper, we present an interferometric fiber-optic current sensor system designed for long-term monitoring of GICs, allowing fully remote sensor control and data access. The developed sensor possesses a noise-limited threshold sensitivity of 2.64 mA 
The fabrication of microelectromechanical systems (MEMS) devices comprises many steps, each of which adds to the tolerance, resulting in device performances that may fall outside the defined limits in the design process. Hence, it is important to know local thin film properties most accurately, directly affecting the performance of the MEMS device. Furthermore, the capability of monitoring and mapping the thin film thickness and stress across a wafer enables device statistics and the strengthening of scientific statements. Within this study, we used standard MEMS structures consisting of a cantilever and a step profile to perform automated and contactless characterization of the local thin film thickness and stress across six 4-inch (100 mm) wafers. For this purpose, we constructed a measurement setup combining white light interferometry (WLI) to measure the static deflection of the cantilevered beams and plates and the thickness of the thin film through a step profile etched into the thin film. Even more, an XYZ-stage positions hundreds of devices below the objective lens of the WLI. This leads to precise maps of the local thin film thickness and to the extraction of a mean stress and a gradient stress from the static deflection of slender beams. The beams are oriented parallel and perpendicular to the wafer flat so that the measurement of orientation-dependent stress values is possible.
In an era of rapid technological advancement, object detection has become essential for enhancing efficiency and safety in various fields. Although significant progress has been made in air-based applications, underwater object detection remains relatively unexplored, especially in shallow aquatic environments such as coastal zones, harbors, and aquaculture facilities, due to its unique challenges. Ultrasonic technology, with its ability to travel long distances underwater and perform well in turbid and low-light conditions, stands out as a promising solution. Recent advances in micro-electromechanical system (MEMS) technology, particularly capacitive micromachined ultrasonic transducers (CMUTs), offer new opportunities for underwater detection. CMUTs are compact and highly sensitive and operate with a wide bandwidth, making them ideal for underwater applications. This research explores the use of CMUTs, fabricated by Fraunhofer ENAS with a resonant frequency of 1.5 MHz, for underwater object detection. Initial experiments confirmed their feasibility for detecting submerged objects of various sizes, shapes, and materials. Further investigations assessed the resolution of CMUTs in detecting minimum object sizes using an automated XYZ stage. Finally, the technology was used to map the topography of test objects, including intricate 3D structures and alphabet shapes, demonstrating its potential for high-resolution mapping. These results highlight the promising capabilities of CMUTs for underwater sensing applications, with substantial potential for further development.
A novel concept of an acoustic flowmeter, based on single-mode waveguides, is proposed, implemented, and analysed in this work. Instead of transmitting a pulse diagonally across the duct's cross-section, this device operates with two ducts that operate simultaneously as pipes and as waveguides. Below the frequency threshold for single-mode propagation, acoustic waves are forced to traverse the waveguides with a plane front, precluding the possibility of beam drifting, inner reflections, and spreading losses. This enables the designer to flexibly increase the sound path and perform a highly sensitive measurement of the flow velocity and speed of sound, even if the excitation frequency is required to be kept below a relatively low value. A device based on this principle was constructed and tested for flow measurements in air. It consists of two waveguides of a circular cross-section (5 mmkHz). Usage of a wideband signal was possible due to the combined frequency response of a special kind of micromachined ultrasound transducer (MUT) and a commercial micro-electromechanical system (MEMS) microphone. The constructed flowmeter was capable of measuring flow velocities up until the transition to turbulent flow at 16 L min−1L min−1, and it also detected changes of less than 0.2 m s−1
This research presents a capacitive displacement sensor concept, designed for integration into a pin array gripper. The sensor employs a plate capacitor structure to measure the displacement of individual pins, with each pin positioned to move between the electrodes. The sensor is designed with sensing, guiding and shielding electrodes to maintain a homogeneous electric field between the capacitor plates and a linear capacitance response. We implemented a shielding strategy with the objective of minimising external interference and reducing mutual interference between individual displacement sensors. This ensures stable operation and reliable measurements, which are crucial for the reliable functioning in dynamic environments. The design is optimised for additive manufacturing, offering advantages in customisation, adaptability to various pin gripping systems and a compact form factor. It also opens up new possibilities for integrating sensing elements directly into the structure of the gripper. A prototype sensor was fabricated using additive manufacturing and tested in an experimental setup to validate its functionality and to enable a comparison of its performance against the results of numerical simulations.
This study demonstrates how integrating silicon mechanical sensors into composite structures enables the detection of internal structural variations and can find applications in process monitoring or structural health monitoring (SHM). It introduces a novel, minimally intrusive process for embedding sensors within composites (substrate-free transfer-printed sensor). Both temperature and strain effects are studied and presented. The silicon strain sensor exhibits high sensitivity due to the piezoresistive effect. The combination of high temperature and strain produces a plastic degradation of the material, linked to the creep phenomenon of the composite's epoxy resin binder. This internal structure modification in the composite is directly detected with the strain gauges in real time.
Wearable biosensors play a crucial role in modern healthcare, providing continuous monitoring of various physiological parameters. However, the reliance on batteries that require replacement introduces interruptions in the data acquisition process and patient discomfort, and for this reason, energy harvesting methods that convert human body energy into electricity have attracted considerable research interest. In this paper, the concept of an innovative hybrid energy harvester that combines piezoelectric and reverse electrowetting-on-dielectric (REWOD) techniques is introduced. The key working principle revolved around the electrical double layer present in the REWOD component and coupling it with a piezoelectric generator via an electret. By harnessing biomechanical vibrations with a piezoelectric material and the REWOD unit, the overall power output of the harvester was enhanced. The proposed design was evaluated through numerical simulations and a series of experimental tests. In the present work, experimental results on the influence of various design parameters on the amount of generated power through the REWOD process are presented, thus contributing to the advancement of self-powered, sustainable, wearable biosensors, enabling seamless and continuous data acquisition without relying on external batteries.
Electronic nose (E-nose) technology relies on partially specific electronic chemical sensor arrays with an appropriate pattern recognition system housed in dedicated chambers and coupled with sampling systems to analyze simple or complex odors. An optimized design and dimensioning of the sensor chamber and sampling system can significantly improve sensor responses. In this context, the design of E-nose sampling systems has recently benefited from emerging technologies such as additive manufacturing (i.e., 3D printing) and innovative materials. While new materials can enable new functionalities in sensor housing construction, their potential gaseous emission can compromise sensor performance over time. More broadly, materials used in E-nose components and in the sampling environment can release volatile organic compounds (VOCs) that can irreversibly adsorb onto sensor surfaces, interfering with sensor functionality, also known as “poisoning”. This study aims to develop an initial experimental methodology and a dedicated setup to assess the potential poisoning effect of materials commonly used in E-nose components or typically found in the sampling environment – PEEK (polyetheretherketone), biocompatible resin and silicone – on gas sensor performance. For this purpose, two widely used commercial metal oxide semiconductor (SMOX) sensors (TGS2610 and TGS2611) were exposed to these materials in an accelerated poisoning test over 2 weeks. The results indicated that silicone and biocompatible 3D-printed resin, even after thermal pre-treatment, significantly altered sensor responses, whereas PEEK did not show any effect on sensor sensitivity over the test duration.
This work presents both the directivity pattern and distance dependence of the pressure field of a piezoelectric micromachined ultrasonic transducer (PMUT). The PMUT comprises a silicon membrane and an integrated piezoelectric transducer based on aluminum nitride (AlN) and exploits bistability to achieve large displacements during snap-through between the two ground states. Snap-through is initiated by a short rectangular pulse train burst, and the resulting sound pressure pulse is recorded. The impact of the number of excitation signal pulses as well as the number of averaged measurement events on the sound pulse shape is studied to find an optimal balance between short sound pulse duration and high maximum sound pressure. Sound reflectance measurements finally demonstrate the feasibility of bistable PMUT devices for future ultrasonic ranging applications.
This study presents a novel design methodology for microbridge resonators aimed at reducing pull-in voltage while maintaining resonant frequency. Previous studies have primarily focused on adjusting resonator plate geometry, modifying anchor conditions, or altering material properties to control pull-in voltage. In contrast, this work introduces the ratio of the bottom electrode length to the microbridge length as a critical and tunable design parameter, which has remained unexplored in previous studies. An analytical model is developed to capture the effects of this ratio, and its predictions are validated through finite element analysis. To demonstrate the concept, two microbridges are designed and fabricated with bottom electrode lengths of 42 and 82 µm, corresponding to 35 % and 68 % of the microbridge length, respectively. All other design parameters, such as plate thickness, material properties and cavity height, are kept constant to enable a fair comparison. Electrical characterizations confirm that increasing the bottom electrode-to-microbridge length ratio effectively lowers the pull-in voltage without degrading resonator performance. Results show a 16 % reduction in pull-in voltage when the bottom electrode length is 68 % of the microbridge length, demonstrating the feasibility and advantages of the proposed methodology over existing techniques.
The EyeOnWater Raspberry Pi (EOW–RPI) is a do-it-yourself maker project for citizen science to measure and classify the natural colour of water. It describes the systematic development (from concept to the prototype and application) and evaluation of a replicable optical sensor system that enables the automatic determination of water's colour according to the Forel–Ule colour scale using a Raspberry Pi and associated camera. Within the framework of image data processing, the system was automated in accordance with the criteria of scientific methods such that application errors were minimised from the initial image acquisition to the colour analysis. The overarching purpose of this project was to promote the independence of scientifically interested and technically skilled laypersons in building their own research device and collecting and providing their own data through a community website. Furthermore, the project can serve as inspiration for the maker community with respect to the development of enhanced extensions. For this purpose, it is crucial that the documentation of software and hardware is made available as open-source information. In this project, we follow a systemic methodology that extends from the requirement analysis and conceptualisation phase through to the system architecture, with the aim of developing suitable hardware and software. To this end, the sensor system was implemented as a prototype and its technical feasibility; handling; and the scientific quality of data acquisition, processing, and Forel–Ule colour analysis were evaluated.
This project developed a low-cost, real-time portable device for measuring radiation. The device is based on a camera similar to those used in modern smartphones and includes the ability to connect to the internet, allowing users to access data through any web browser. The program files are available for download for educational purposes.
This work presents a portable gamma-ray dosimeter which uses as a detector a CMOS image sensor mounted on a compact low-cost Internet of Things (IoT)-compatible ESP32-CAM embedded board. The detector uses simple, physics-based processing algorithms to allow for particle detection using CMOS image sensors on boards with limited processing capabilities. The detector enables real-time acquisition and processing, achieving nearly 100 % live measurement time, and the measurement results can be accessed from any device connected to the Wi-Fi network. A dose–event relation of (80 ± 5) nSv per event, calibrated with a Cs-137 gamma source, is suitable for cost-effective environmental radiation monitoring, personal dosimetry, or education purposes. The firmware is released as open-source, making it accessible for public use.
Poor water management has led to conflicts worldwide and significant loss of life and property. According to the UN, by 2040, nearly one in four children will live in an area with limited water resources. Poor water management has also been a critical factor in accelerating climate change. Climate change, in turn, intensifies extreme weather events, leading to more frequent and severe floods. The traditional water level monitoring stations are outdated, invasive, and limited in telemetry capabilities, resulting in numerous fatalities due to the lack of an effective early warning system. WAMO 300 (Water Monitor 300) introduces an innovative and noninvasive method of measuring water levels using navigation satellites such as GPS or Galileo, providing real-time alerts to various stakeholders. This study evaluates the feasibility of the WAMO 300 system and proposes decentralised water management solutions, leveraging this system to promote sustainable water diplomacy.
With the rising number of machine learning and deep learning applications, the demand for implementation of those algorithms near the sensors has grown rapidly to allow efficient edge computing. Especially in sensor-based tasks like predictive maintenance and smart condition monitoring, the goal is to implement the algorithms near the data acquisition system to avoid unnecessary energy consumption caused by extensive transfer of raw data. Deep learning algorithms achieved good results in various fields of application and often allow the efficient implementation on dedicated hardware and common AI accelerators like graphic and neural processing units. However, they often need more interpretability to analyze upcoming results. For this purpose, this paper presents an approach to represent trained interpretable machine learning algorithms, consisting of a stack of feature extraction, feature selection, and classification/regression algorithms, as deep neural networks. This representation retains the interpretability but allows efficient implementation on hardware to process the acquired data directly on the sensor node. The representation is based on dissembling the inference of the trained interpretable algorithm into the basic mathematical operations to represent them with deep neural network layers. The technique to convert the trained interpretable machine learning algorithms is described in detail and applied to parts of an open-source machine learning toolbox. The accuracy, runtime, and memory requirements are investigated on four datasets, implemented on resource-limited edge hardware. The deep neural network representation reduced the runtime compared to a common Python implementation by up to 99.3 % while retaining the accuracy. Finally, a quantization method was successfully applied to interpretable machine learning algorithms, gained an additional reduction of 64.8 % in runtime, and reduced the memory requirement up to 75.6 % compared to the full precision implementation.