Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO₂ readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment.

This work describes the architecture and the experimental results from the characterization of a 32-channels mixed-signal Application-Specific Integrated Circuit (ASIC) developed for the readout of the lithium-drifted silicon, Si(Li), detectors of the General AntiParticle Spectrometer (GAPS) experiment dedicated to searching for dark matter. The instrument is designed for the identification of antiprotons, antideuterons and antihelium nuclei from cosmic rays during an Antarctic balloon mission scheduled for late 2024. A full custom integrated circuit, named SLIDER32 (32-channels Si-LI DEtector Readout) ASIC, has been produced in a commercial 180 nm CMOS technology. The ASIC is comprised of 32 low-noise analog readout channels featuring dynamic signal compression to comply with the wide input range, an 11-bit SAR ADC and a digital back-end section which is responsible for channel setting and for sending digital information to the data acquisition system (DAQ). The circuit design criteria and the experimental results are discussed in the paper.

Electroconductive textiles (e-Textiles) are vital in developing wearable sensors that preserve the comfort and characteristics of textiles. Among two-dimensional (2D) transition metal dichalcogenides (TMDs), considered a promising option for sensor applications, tungsten di-selenide (WSe₂) homostructures have been used as humidity- and temperature-sensing materials for developing e-textiles, as mentioned in a first-of-its-kind report. Exfoliated chemical vapor deposition (CVD)-grown 2H-WSe₂ nanosheets were dispersed in hydroalcoholic solutions using an amino-functionalized silane to improve dispersion. Acrylic thickener was added to create 2H-WSe₂-based pastes, which were applied onto cotton using the knife-over-roll technique to obtain thin, flexible electroconductive coatings on textiles. Various characterization techniques confirmed the even distribution of 2D-WSe₂-based coatings on fabrics and the maintenance of textile comfort and wearability. The conductivity of coated fabrics was measured at room temperature and ranged between 2.9 × 10⁸ and 1.6 × 10⁹ Ω sq⁻¹. The WSe₂-based textile sensors functioned well as resistance humidity detectors within 30–90% relative humidity (RH), revealing good repeatability and sensitivity after multiple exposure cycles. To a lesser extent, WSe₂-based textile sensors act as temperature detectors within 20–60 °C with limited repeatability. The 2D-based textiles exhibited a quadratic dependence of resistance on temperature and a characteristic thermal hysteresis. This proposed strategy marks a significant milestone in developing scalable and flexible 2D TMD-based detectors with great potential for wearable sensing devices.

Diffraction-limited storage rings are posing huge challenges on the detectors required in new generation x-ray experiments, especially those based on continuous-wave operation. A modern mixed-signal readout channel demands strict requirements on noise, power consumption, and frame rate, asking for advancements in both analog and digital design. The proposed readout channel, pFREYA, is designed for pixellated detectors to be employed in continuous-wave x-ray science applications (e.g., ptychography) with a frame rate of 1 MHz. It is developed in a commercial 65nm CMOS technology and features single photon resolution at 5 keV, 9 keV, 18 keV, and 25 keV photon energies, a power consumption of 220 μW, and a pixel area of 150μm× 150 μm. It also offers a 2-bit switchable peaking time selection for signal-to-noise ratio optimisation, signal-over-threshold identification for zero-suppression, and 10-bit analogto- digital conversion per channel. In the present work an overview on the readout channel architecture will be given and results from the characterisation of the CSA and the shaper employed in the architecture will be provided, in addition to the noise performance of the pFREYA channel.

Active mobility and light vehicles, such as e-bikes, are gaining increasing attention as sustainable transportation alternatives to internal combustion solutions. In this context, collecting comprehensive data on environmental conditions, vehicle performance, and user interaction is crucial for improving system efficiency and user experience. This paper presents a data acquisition system designed to collect data from multiple sensor platforms. The architecture is optimized to maintain low power consumption and operate within limited computational resources, making it suitable for real-time data acquisition on light vehicles. To achieve this, a data acquisition module was developed using a single-board computer integrated with a custom shield, which also captures data related to the assistance of an e-bike motor through a wireless interface. The paper provides an in-depth discussion of the architecture and software development, along with a detailed overview of the sensors used. A demonstrator was created to verify the system architecture idea and prove the potentialities of the system overall. The demonstrator has been qualified by professional and semi-professional riders in the framework of the Giro-E, a cyclist event which took place in May 2024, on the same roads of the Giro d’Italia. Finally, some preliminary analyses on the data acquired are provided to show the performance of the system, particularly in reconstructing the user behavior, the environmental parameters, and the type of road.