Monitoring of ions in real-time directly in cell culture systems and in organ-on-a-chip platforms represents a significant investigation tool to understand ion regulation and distribution in the body and ions’ involvement in biological mechanisms and specific pathologies. Innovative flexible sensors coupling electrochemical stripping analysis (square wave anodic stripping voltammetry, SWASV) with an ion selective membrane (ISM) were developed and integrated in Transwell™ cell culture systems to investigate the transport of zinc and copper ions across a human intestinal Caco-2 cell monolayer. The fabricated ion-selective sensors demonstrated good sensitivity (1 × 10⁻¹¹ M ion concentration) and low detection limits, consistent with pathophysiological cellular concentration ranges. A non-invasive electrochemical impedance spectroscopy (EIS) analysis, in situ, across a selected spectrum of frequencies (10–10⁵ Hz), and an equivalent circuit fitting were employed to obtain useful electrical parameters for cellular barrier integrity monitoring. Transepithelial electrical resistance (TEER) data and immunofluorescent images were used to validate the intestinal epithelial integrity and the permeability enhancer effect of ethylene glycol-bis(2-aminoethylether)-N,N,N’,N’-tetraacetic acid (EGTA) treatment. The proposed devices represent a real prospective tool for monitoring cellular and molecular events and for studies on gut metabolism/permeability. They will enable a rapid integration of these sensors into gut-on-chip systems.

This work investigates the physical properties of Al1−xScxN thin films sputtered at low temperatures by varying the process conditions. Specifically, the films were deposited at room temperature by applying a radio frequency power equal to 150 W to an AlSc alloy (60:40) target, varying the nitrogen flux percentage in the (Ar + N2) sputtering atmosphere (30%, 40%, 50%, and 60%) and keeping constant the working pressure at 5 × 10−3 mbar. The structural and chemical properties of the Al1−xScxN films were studied by x-ray diffraction and Rutherford backscattering spectrometry techniques, respectively. The piezoelectric response was investigated by piezoresponse force microscopy. In addition, the surface potential was evaluated for the first time for Sc-doped AlN thin films by Kelvin probe force microscopy, providing piezoelectric coefficients free from the no-piezoelectric additional effect to the mechanical deformation, i.e., the electrostatic force. By alloying AlN with scandium, the piezoelectric response was strongly enhanced (up to 200% compared to undoped AlN), despite the low deposition temperature and the absence of any other additional energy source supplied to the adatoms during thin film growth, which generally promotes a better structural arrangement of polycrystalline film. This is a strategic result in the field of microelectromechanical systems completely fabricated at low temperatures.

In this paper, a wireless potentiostat code-named ElectroSense, for interfacing of wearable electrochemical biosensors, will be presented. The system is devoted to non-invasive monitoring of glucose in wearable medical applications. Differently from other potentiostats in literature, which use digital-to-analog converters (DACs) as discrete components or integrated in high-end microcontrollers, in this work the pulse width modulation (PWM) technique is exploited through PWM-DAC approach to generate signals. The ubiquitous presence of integrated PWM peripherals in low-end microcontrollers, which generally also integrate analog-to-digital converters (ADCs), enables both the generation and acquisition of read-out signals on a single cheap electronic device without additional hardware. By this way, system’s production costs, power consumption, and system’s size are greatly reduced with respect to other solutions. All these features allow the system’s adoption in wearable healthcare Internet-of-things (IoT) ecosystems. A description of both the sensing technology and the circuit will be discussed in detail, emphasizing advantages and drawbacks of the PWM-DAC approach. Experimental measurements will prove the efficacy of the proposed electronic system for non-invasive monitoring of glucose in wearable medical applications.

Recently, the scientific community has shown a great interest about the Organ-on-Chip (OoC) devices, a special kind of micro-fabricated platforms capable of recapitulating the human physiology implementing the traditional cell culture methods and the concept of in vivo studies. Copper ions represent a cellular micronutrient that must be monitored for its potential hazardous effects. The application of electrochemical analysis for heavy metal ions detection and quantification in commercial cell culture media presents several issues due to electrolyte complexity and interferents. In fact, to the best of our knowledge, there is a lack of applications and OoC devices that implement the Anodic Stripping Voltammetry as an ion dosing technique due to the reasons reported above. In fact, considering just the peak intensity value from the measurement, it turns out to be challenging to quantify ion concentration since other ions or molecules in the media may interfere with the measurement. With the aim to overcome these issues, the present work aims to develop an automated system based on machine learning algorithms and demonstrate the possibility to build a reliable forecasting model for copper ion concentration on three different commercial cell culture media (MEM, DMEM, F12). Effectively, combining electrochemical measurements with a multivariate machine learning algorithm leads to a higher classification accuracy. Two different pH media conditions, i.e., physiological (pH 7.4) and acidic (pH 4), were considered to establish how the electrolyte influences the measurement. The experimental datasets were obtained using square-wave anodic stripping voltammetry (SWASV) and were used to carry out a machine learning trained model. The proposed method led to a significant improvement in Cu²⁺ concentration detection accuracy (96.6% for the SVM model and 93.1% for the NB model in MEM) as well as being able to monitor the pH solution.