This research presents a capacitive displacement sensor concept, designed for integration into a pin array gripper. The design is optimised for additive manufacturing, offering new design possibilities and enabling the creation of fully additive manufactured pin grippers with integrated displacement sensors. A prototype sensor was fabricated using additive manufacturing, and experimental results confirm the simulations and the functionality of the fabricated sensor for different pin materials.

This study demonstrates how integrating silicon mechanical sensors into composites enables the detection of internal structural variations and how this can find applications in process monitoring or structural health monitoring (SHM). It introduces a novel, minimally intrusive process for embedding sensors within composites by means of a substrate-free transfer technique. Temperature and strain effects are studied. The sensor exhibits high sensitivity and is able to detect a creep effect at the core of the composite.

The concept of an innovative energy harvester that could combine piezoelectric and reverse electrowetting-on-dielectric (REWOD) techniques is presented. By harnessing biomechanical vibrations from the cardiovascular system with piezoelectricity and a REWOD unit, the overall power output of the harvester was enhanced. This contributes to the advancement of self-powered, sustainable, wearable biosensors, enabling seamless and continuous data acquisition without relying on external batteries.

While electronic noses are designed to detect volatile organic compounds (VOCs) in complex odors, materials within sampling chambers and lines can emit their own gases, altering sensor responses. Our study confirmed that silicone and 3D-printed resin release compounds that poison sensors and reduce life span. In contrast, polyetheretherketone (PEEK) is a stable material that does not cause poisoning. Understanding these effects is crucial for designing reliable gas sensing platforms, ensuring accurate gas detection in different applications.

Bistability is a mechanical phenomenon resulting in two stable, switchable states similar to a light switch. Micromachined piezoelectric bistable loudspeakers can utilize this effect to generate ultrasonic pulses with substantial loudness for applications such as distance measurements. In this work, we study different aspects of such a micromachined ultrasonic loudspeaker and demonstrate that such a device is feasible for ranging applications.

This research improves the performance of microbridge resonators for various applications, including gas sensing. By strategically designing the bottom electrode size relative to the microbridge length while keeping all other design parameters constant, the required operating voltage is reduced without compromising performance. Advanced modelling and experimental results demonstrate a 16 % reduction in operating voltage. These findings enhance resonator efficiency across diverse applications.

The paper outlines a do-it-yourself optical sensor system for measuring water colour that can be used in citizen science projects to engage laypersons in science. The automated optical sensor system uses a camera with a Raspberry Pi computer to determine water colour according to the Forel–Ule colour scale. The collected data can be shared to the EyeOnWater database and used to provide insight and knowledge for further research in the fields of limnology and oceanology.

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.

Globally, water has been the cause of the rise of civilization and also a cause of conflict. In this changing climate, managing water, the most precious commodity, has become a necessity. If we want to manage a resource properly, we need technology and data. With the innovative WAMO 300, a decentralised solution for this problem has been provided. WAMO 300, short for Water Monitor 300, acts as an IoT (Internet of Things) system that brings data from the physical world to the digital world.

Robust and interpretable machine learning algorithms, which have been proven in various applications, often lack efficient implementation on limited hardware. The novel approach is to convert the inference of the interpretable ML into a deep neural network, which is efficiently executable on edge hardware. This approach was validated in terms of runtime efficiency, memory requirements, and accuracy and resulted in a significant improvement in terms of runtime and memory requirements.