This paper presents a battery-less NFC sensor tag designed for contactless physiological monitoring, where extended read range and robust energy harvesting are critical to ensure reliable sensing. The sensor tag integrates three functional blocks: an electrochemical enzymatic sensor for analyte detection, the SIC4341 interface chip for data acquisition and wireless communication, and a dual-coil NFC antenna for efficient power transfer and signal exchange. The antenna is implemented on a thin, flexible polyimide substrate, with two in-phase coils printed on opposite sides and connected in series through a via hole. This configuration increases the overall inductance and magnetic flux, enhancing the tag communication range while maintaining a compact footprint. A full RLC circuit model is analyzed and tuned to operate at the standard NFC frequency of 13.56 MHz, with calibration to maintain resonance. The proposed device successfully demonstrates battery-less sensing of glucose levels under real operating conditions. Additionally, electromagnetic simulations and experimental validation confirm that the dual-coil design enables an almost 50% improvement in communication distance over a conventional single-coil layout, and is capable of extracting enough energy through inductive coupling over the large distance to drive the IC and sensing operation.

In near field communication (NFC), the operational range is typically constrained by the antenna dimensions and coil design. This paper introduces an innovative dual-coil NFC antenna system designed to increase this range to ensure more robust communication over increased distances and for wearable sensors applications. For the purpose, two in-phase circular coils are positioned on opposite sides of the same substrate, connected in series through appropriate via hole. This arrangement effectively increases both the coil length and turn count saving the substrate footprint. The outer diameter of each coil is 30mm and they are connected in series by a via hole in a 0.05mm thick commercial flexible substrate polyimide. The use of thin flexible substrate is aimed to ensure the availability of profile NFC antenna structure for various near field applications (cellular NFC-based communications and wearable real-time monitoring system of critical physiological parameters). Comprehensive design optimization has been conducted using ANSYS Electronics Desktop, accompanied by mutual positional (sliding and vertical) analyses to compare the field ranges achievable with dual-coil versus single-coil configurations.

Urban air pollution poses severe health risks, demanding accurate monitoring and exposure-reducing solutions. Low-Cost air quality sensors (LCS) provide high spatial resolution but suffer from accuracy limitations that hinder their reliability. This paper presents the AIQS project (AI-enhanced air quality sensor for optimizing green routes), an ongoing initiative that combines artificial intelligence, sensor hardware optimization, and pedestrian routing innovation to address these challenges. AIQS applies machine learning techniques, including Multilayer Perceptrons and fuzzy logic, to correct sensor readings. In parallel, hardware-level optimizations, such as fluid dynamics simulations and pre-treatment modules, are explored to enhance sensor performance. The corrected AQ data is then incorporated into a configurable routing tool capable of estimating pollutant exposure and computing low-exposure pedestrian paths in urban environments. First evaluations, shows that our correction models achieve up to 0.92 R2 against reference data across diverse urban environments. The corrected data drives a configurable routing tool that computes paths minimizing cumulative pollution exposure while balancing user preferences (e.g., proximity to green spaces). Preliminary validation in Modena, Italy demonstrates viable "green routes".

Antenna array is a key component in various communication systems these days. Rather than various linear and planar array structures, antenna elements are strictly conformed to the curved-shape surfaces they are mounted in conformal antenna arrays. In some specific designs, antenna arrays are required to be fixed with the curved surface like airborne systems, rockets, missiles and space shuttles. In this study, an array factor is derived for a spherical conformal antenna array in cylindrical coordinates as a group combination of various circular antenna arrays with different radius at uniform height steps. For the purpose, the array factors of circular (as a ring version of vertical linear array) and cylindrical (as a linear version of circular array) antenna arrays are derived in the first step. In the second step, a ring-based spherical structure is developed with the upgradation of cylindrical mathematical array factor derivation. In the third step, MATLAB codes are developed to numerically calculate and display the beam patterns of antenna arrays (circular, cylindrical and spherical) in cylindrical coordinate system. Finally, for the validation of proposed synthesis, the profile structures are simulated in HFSS to check if antenna arrays can work in the proposed structural configuration using 5G antenna elements.

Real-time monitoring of lube oil plays a crucial role in ensuring optimal machinery performance, preventing failures, and facilitating timely maintenance strategies. The approach proposed in this work, based on impedance spectroscopy and supervised machine learning (ML), addresses this need by a novel solution to a multiclass classification problem of cross-contaminations in aviation lubricant. Impedance measurements were performed at room temperature by immersing a microfabricated sensor in 16 aged oil samples containing increasing concentrations of water and aviation fuel. Two datasets were constructed: the first based on impedance components spectra and the second based on dissipation factor spectra. A data pre-processing and augmentation method was proposed for generating synthetic examples from the measured data. Both datasets were independently used to train three supervised classifiers, whose performance was evaluated based on three different approaches of dataset split ratio and k-fold cross-validation. The 1-nearest neighbors (NN) classifier proved to be the most effective in reducing false positives (FPs), false negatives (FNs), and computational running time. The best results were obtained by employing a split ratio of 60:40 and threefold cross-validation scheme on the impedance components-based dataset, yielding an accuracy of 99.8%.

Sputtered aluminum nitride (AlN) thin films were characterized by Piezoresponse Force Microscopy (PFM) technique using a methodology to decrease the contribution of the electrostatic forces to obtain a pure piezoelectric response. Our method is based on the sweeping of the DC voltage applied to the Atomic Force Microscope (AFM) tip under a fixed AC field to evaluate the contact surface potential difference (VCPD) between the tip and the sample used to measure the proper AlN piezoelectric coefficient (d33,eff), minimizing the electrostatic contribution. Kelvin probe Force Microscopy (KPFM) was employed as reference standard technique to measure the surface potential, confirming the reliability of the proposed experimental procedure on ceramic piezoelectric films, and simultaneously overcoming the disadvantages of the KPFM technique. The capability to tune surface potential of materials over a wide range of values opens new perspectives for the design of devices with changeable surface potential.

In this article, a system and a measurement approach to reduce the measurement time in the assessment of moisture contamination in lubricant oils is presented. The system’s sensing principle leverages the permittivity change of a miniaturized interdigital capacitor (IDC) while immersed in oil. The time-domain impedance concept, that is, the impulse response (IR), is exploited by using maximum length sequences (MLSs) as efficient broadband signals for the sensor’s excitation in a wide range of frequencies. Different from conventional impedance spectroscopy (IS), MLS-based measurements are performed with simpler hardware, higher computational efficiency, lower power consumption, and lower measurement time. As a novelty with respect to the state-of-the-art, this article introduces a linear model to relate a single measured quantity from the IR to water concentration in oil. This permits to reduce the digital processing operations, leading to low measurement time and, thus, to low energy-per-measurement parameters with respect to other works which rely on laboratory instrumentation. The validity of the linear model, for the detection of small concentrations of water in lubricant oil, has been verified through experimental measurements. Water–oil samples have been prepared with 0.2 vol%, 0.5 vol%, 1 vol%, 2 vol%, and 3 vol% water concentrations at room temperature, obtaining an estimated limit of detection (LOD) of 6.3 ppm. A low measurement time of 18 ms has been achieved which advances the state-of-the-art.

In this paper, a measurement system aimed to the fast classification of water contamination in oil samples will be presented. The transduction principle is based on the permittivity change of an interdigital capacitor which changes its capacitance value while immersed in oil samples with different water concentrations. Differently from other works, the presented system proposes a circuit and a measurement approach. It combines the broadband excitation property of MLS-based impulse response (IR) measurements with the support vector machine (SVM) machine-learning (ML) model. This approach allows to speed up the measurements, thus reducing the energy-per-measurement parameter in order to make the system suitable for battery-powered portable devices. The theoretical foundations, the circuit-level description of the analog front-end, and the used ML model will be presented in detail. The classification capability of the system will be proved by evaluating 40 IRs from 6 prepared oil samples at water concentrations of 0 vol%, 0.2 vol%, 0.5 vol%, 1 vol%, 2 vol%, and 3 vol%. The proposed system is able to measure a 1023-point IR in 700 ms, which is better than the state-of-the-art. Finally, an overall classification accuracy of 90% is obtained after the SVM training process with a 10 fold cross-validation.

The integration of sensing devices into cell culture systems is a topic of great interest in the study of pathologies and complex biological mechanisms in real-time. In particular, the fit-for-purpose microfluidic devices called organ-on-chip (OoC), which host living engineered organs that mimic in vivo conditions, benefit greatly from the integration of sensors, enabling the monitoring of specific chemical-physical parameters that can be correlated with biological processes. In this context, copper is an essential trace element whose total concentration may be associated with specific pathologies, and it is therefore important to develop reliable analytical techniques in cell systems. Copper can be determined by using the anodic stripping voltammetry (ASV) technique, but its applicability in cell culture media presents several challenges. Therefore, in this work, the performance of ASV in cell culture media was evaluated, and an acidification protocol was tested to improve the voltammetric signal intensity. A Transwell® culture model with Caco-2 cells was used to test the applicability of the developed acidification protocol by performing an off-line measurement. Finally, a microfluidic device was designed in order to perform the acidification of the cell culture medium in an automated manner and then integrated with a silicon microelectrode to perform in situ measurements. The resulting sensor-integrated microfluidic chip could be used to monitor the concentration of copper or other ions concentration in an organ-on-chip model; these functionalities represent a great opportunity for the non-destructive strategic experiments required on biological systems under conditions close to those in vivo.

The synergistic effect of silicon-based substrates on the physical and chemical properties of aluminum nitride (AlN) thin films was investigated. AlN thin films were deposited by RF magnetron sputtering on Low Resistivity (LR) Si, Silicon On Insulator (SOI) and Si/SiO2 substrates at room temperature. The morphological and structural properties were investigated by X-ray diffraction (XRD), Raman spectroscopy, Atomic Force microscopy (AFM). XRD analyses evidenced the co-presence of (002) and (101) orientations. The substrate influence on films morphology, crystalline order, intrinsic stress and grain size is well evidenced, as shown by Raman and AFM analyses. These surface characterization techniques represent a valid support to select the suitable Si-substrate/ piezoelectric thin film combination for the fabrication of a piezoelectric device. AlN sputtered on Si-LR substrate showed an enhancement of structural arrangement along (002) planes while the sample sputtered on Si/SiO2 resulted mainly oriented along (101) planes. For these reasons, further characterization was done: (002) -ori-ented AlN thin films were characterized in terms of piezoelectric response by piezometer and Piezoresponse Force Microscopy (PFM) measurements, while cytotoxicity and biocompatibility were investigated for (1 01) -oriented AlN thin films. This further investigation helped to assess the suitable film to integrate into piezoelectric devices operating in air or in liquid, respectively.

The presented work has been developed inside the PON project SIROBOTICS (SocIal ROBOTics for active and healthy ageing), CUP: B76C1800054000, founded by Apulia Region, Italy.

The present work discusses an experimental investigation of the flow into a silicon-based vaporizing liquid microthruster equipped with sensing capabilities and low-power on-channel secondary heaters. The sensing capabilities (resistance temperature detectors and capacitive void fraction sensors) are used to investigate the flow instability and to evaluate its performances. The device has a sandwich structure composed of a silicon substrate and a glass substrate. This last one allows optical access into the device. The main heating of the propellant is provided using a platinum resistive heater placed on the bottom of the silicon layer. By varying the electrical power supplied to the main heater at fixed mass flow rate, three flow regimes have been observed and investigated: fully liquid flow, two-phase flow, and fully vaporized flow. Their dynamics have been captured through high-speed micro-flow visualizations under rough vacuum conditions (about 29 kPa). Furthermore, the expansion of the exhaust vapor plume exiting from the micronozzle has been analyzed via Schlieren visualizations. Results highlighted the occurrence of a cyclic flow behavior during the two-phase flow regime. In contrast, the flow exhibits the presence of both liquid and vapor phases at the micronozzle exit. Once the fully vaporized flow regime is established, the two-phase flow dynamics into the inlet chamber become more stable with complete filling and more uniform distribution of the flow at the microchannels entrance. Schlieren imaging captured the increase of the exhaust plume spreading half-angle when moving from ambient condition towards rough vacuum.

Rapid advances in micro/nanotechnology have enabled to achieve high levels of miniaturization, promoting the development of low cost and highly efficient microsystems for specific applications. In the space sector, the miniaturization of satellites has led to a renewed interest in the research and development of advanced micropropulsion technologies able to generate small and accurate thrust forces and high specific impulse. This work presents the design and fabrication of a silicon-based water-propellant Vaporizing Liquid Microthurster (VLM) equipped with embedded microsensors for real-time monitoring of in-channel vapor/liquid fraction and fluid temperature during its operation. Anisotropic dry etching of silicon wafer and thermocompressive bonding were chosen as key fabrication steps: the former process was used to better control the surface roughness on microchannels inner walls, the latter was used to guarantee the fluidic tightness and complete the fabrication process of the device. Borofloat 33 glass, used to seal the micromachined silicon wafer, allows the optical inspection of the fluid flow and vaporization within the different chambers during the device operation. A platinum resistive heater placed on the bottom of the chip was exploited for the heating of the propellant. A set of Resistive Temperature Detectors (RTDs) and, for the first time, capacitive sensors were designed and integrated inside the microthruster chip to add distributed sensing capabilities for flow instability control. Further, a secondary low-power platinum thin film resistive heater was placed inside each of the eight channels, in order to allow for localized precision fluid heating and flow control. The operational feasibility of the fabricated microthruster was assessed by means of a preliminary characterization of the embedded sensors, based on experimental tests supported by numerical investigations; the results demonstrated that the designed microsensor devices are able to maximize the microthruster efficiency, in terms of microtexture-enhanced in-channel water vaporization and reduced power consumption.

INTRODUCTION:Small extracellular vesicles (sEVs), thanks to their cargo, are involved in cellular communication and play important roles in cell proliferation, growth, differentiation, apoptosis, stemness and embryo development. Their contribution to human pathology has been widely demonstrated and they are emerging as strategic biomarkers of cancer, neurodegenerative and cardiovascular diseases, and as potential targets for therapeutic intervention. However, the use of sEVs for medical applications is still limited due to the selectivity and sensitivity limits of the commonly applied approaches.METHODS:Novel sensing solutions based on nanomaterials are arising as strategic tools able to surpass traditional sensor limits. Among these, Si nanowires (Si NWs), realized with cost-effective industrially compatible metal-assisted chemical etching, are perfect candidates for sEV detection.RESULTS:In this paper, the realization of a selective sensor able to isolate, concentrate and quantify specific vesicle populations, from minimal volumes of biofluid, is presented. In particular, this Si NW platform has a detection limit of about 2×105 sEVs/mL and was tested with follicular fluid and blastocoel samples. Moreover, the possibility to detach the selectively isolated sEVs allowing further analyses with other approaches was demonstrated by SEM analysis and several PCRs performed on the RNA content of the detached sEVs.DISCUSSION:This platform overcomes the limit of detection of traditional methods and, most importantly, preserves the biological content of sEVs, opening the route toward a reliable liquid biopsy analysis.

This work is aimed at fabricating nanocomposites based on zinc oxide (ZnO) nanostructures and nanocellulose dispersed in a UV-cured acrylic matrix (EC) for application as functional coatings for self-powered applications. Morphological, thermal, and dynamic mechanical properties of the nanocomposites were characterized by X-Ray diffractometry (XRD), scanning electron microscopy, and differential scanning calorimetry. The piezoelectric behavior was evaluated in terms of root mean square (RMS) open circuit voltage, at different accelerations applied to cantilever beams. The generated voltage was correlated with ZnO nanostructures morphology, aluminum nitride film integration on the beam and proof mass insertion at the tip. Nitride layer increased the RMS voltage from 1 to 2.4 mV up to 3.9 mV (using ZnO nanoflowers). As confirmed by XRD analyses, the incorporation of ZnO nanostructures into the acrylic matrix favored an ordered structural arrangement of the deposited AlN layer, hence improving the piezoelectric response of the resulting nanocomposites. With proof mass insertion, the output voltage was further increased, reaching 4.5 mV for the AlN-coated system containing ZnO nanoflowers.

The monitoring of some parameters, such as pressure loads, temperature, and glucose level in sweat on the plantar surface, is one of the most promising approaches for evaluating the health state of the diabetic foot and for preventing the onset of inflammatory events later degenerating in ulcerative lesions. This work presents the results of sensors microfabrication, experimental characterization and FEA-based thermal analysis of a 3D foot-insole model, aimed to advance in the development of a fully custom smart multisensory hardware–software monitoring platform for the diabetic foot. In this system, the simultaneous detection of temperature-, pressure- and sweat-based glucose level by means of full custom microfabricated sensors distributed on eight reading points of a smart insole will be possible, and the unit for data acquisition and wireless transmission will be fully integrated into the platform. Finite element analysis simulations, based on an accurate bioheat transfer model of the metabolic response of the foot tissue, demonstrated that subcutaneous inflamed lesions located up to the muscle layer, and ischemic damage located not below the reticular/fat layer, can be successfully detected. The microfabrication processes and preliminary results of functional characterization of flexible piezoelectric pressure sensors and glucose sensors are presented. Full custom pressure sensors generate an electric charge in the range 0–20 pC, proportional to the applied load in the range 0–4 N, with a figure of merit of 4.7 ± 1 GPa. The disposable glucose sensors exhibit a 0–6 mM (0–108 mg/dL) glucose concentration optimized linear response (for sweat-sensing), with a LOD of 3.27 µM (0.058 mg/dL) and a sensitivity of 21 µA/mM cm2 in the PBS solution. The technical prerequisites and experimental sensing performances were assessed, as preliminary step before future integration into a second prototype, based on a full custom smart insole with enhanced sensing functionalities.

Synergic efforts in microfabrication processes, cells culture and tissue engineering promoted extraordinary progress in Organ-on-Chip (OoC) technology, leading to the development of in vitro microphysiological models able to recapitulate the microenvironment and key biochemical, functional, structural and mechanical features of specific tissues and living organs. In order to assess the functionality of these cell cultures with every increasing biological complexity, it is also important to equip OoCs with miniaturized sensing devices able to monitor key physical and chemical parameters related to the culture microenvironment and to pathophysiological cell-cell interactions. Gut is one of the most interesting and studied human organs: it performs multiple fundamental body functions, from transport, absorption and metabolism of nutrients and drugs, to the maturation of the immune system and host protection from pathogens and infections. In this Review, an overview of Gut-on-Chip (GoC) systems is provided, with a special attention focused on the most relevant sensing strategies integrated into GoC, aimed at monitoring in situ the microphysiological parameters related to intestine functionalities. Advantages and limitations associated with currently integrated physical, chemical, and biochemical sensors are discussed, together with the challenges that the technology still faces, and the possible adaptive solutions coming from other developed OoC models. Finally, we focus the attention on how gut microbiota connect to other organs of the human body and on the role of gut in the understanding of the progression of many diseases, such as the most recent pandemic infection caused by SARS-CoV-2 virus.

AbstractThis work is aimed at fabricating nanocomposites based on zinc oxide (ZnO) nanostructures and nanocellulose dispersed in a UV‐cured acrylic matrix (EC) for application as functional coatings for self‐powered applications. Morphological, thermal, and dynamic mechanical properties of the nanocomposites were characterized by X‐Ray diffractometry (XRD), scanning electron microscopy, and differential scanning calorimetry. The piezoelectric behavior was evaluated in terms of root mean square (RMS) open circuit voltage, at different accelerations applied to cantilever beams. The generated voltage was correlated with ZnO nanostructures morphology, aluminum nitride film integration on the beam and proof mass insertion at the tip. Nitride layer increased the RMS voltage from 1 to 2.4 mV up to 3.9 mV (using ZnO nanoflowers). As confirmed by XRD analyses, the incorporation of ZnO nanostructures into the acrylic matrix favored an ordered structural arrangement of the deposited AlN layer, hence improving the piezoelectric response of the resulting nanocomposites. With proof mass insertion, the output voltage was further increased, reaching 4.5 mV for the AlN‐coated system containing ZnO nanoflowers.

In this work, the fabrication of composites consisting of piezoelectric ZnO ceramic nanostructures and nanocellulose fillers in a UV-cured acrylic matrix has been exploited for the design of new functional coatings for green energy generation. The piezoelectric behavior was investigated at different accelerations applied to cantilever beams. The piezoelectric signal generated by the different ZnO nanostructures was improved by aluminum nitride film integration on the beam and proof mass insertion at the tip.

A fast and reliable identification of foot pressure loads and temperature distributions changes on the plantar surface allows to prevent and reduce the consequences of ulceration of the diabetic foot. This work presents a smart insole in which both temperature and pressure data in 8 reading points are monitored in remote way for the assessment of the health foot conditions by a caregiver. Minimally invasive and low power temperature and force sensors have been chosen and integrated into two antibacterial polyurethane-based layers architecture. In this work the attention was focused on the heat transfer between the insole and the foot. Finite element simulations were performed to evaluate the effectiveness of the sensor array to detect, from thermal gradients measured on the plantar surface, inflammatory events that can be attributed to early signs of foot ulceration. The results demonstrated that small differences of temperature between the eight sensor nodes of the array can be discriminated and used to prevent the onset of ulcerative lesions, also giving a supplementary information about the position closer to a potential inflamed region of the foot.