This study presents a comprehensive analysis aimed at validating the use of an innovative nanosensor based on graphitic nanomembranes for the smart monitoring of industrial wastewater. The validation of the potential of the nanosensor was carried out through the development of advanced analytical methodologies, a direct experimental comparison with commercially available electrode sensors commonly used for the detection of chemical species, and the evaluation of performance under conditions very similar to real-world field applications. The investigation involved a series of controlled experiments using an organic pollutant—benzoquinone—at varying concentrations. Initially, data analysis was performed using classical linear regression models, representing a conventional approach in chemical analysis. Subsequently, a more advanced methodology was implemented, incorporating machine-learning techniques to train a classifier capable of detecting the presence of pollutants in water samples. The study builds upon an experimental protocol previously developed by the authors for the nanomembranes, based on electrochemical impedance spectroscopy. The results clearly demonstrate that integrating the nanosensor with machine-learning algorithms yields significant performance. The intrinsic properties of the nanosensor make it well-suited for potential integration into field-deployable platforms, offering a real-time, cost-effective, and high-performance solution for the detection and quantification of contaminants in wastewater. These features position the nanomembrane-based sensor as a promising alternative to overcome current technological limitations in this domain.

This paper investigates the feasibility of a multi-purpose use of thin films of industrial-grade graphene, adopted initially to realize advanced coatings for thermal management or electromagnetic shielding. Indeed, it is demonstrated that such coatings can be conveniently used as distributed temperature sensors based on the sensitivity of their electrical resistance to temperature. The study is carried out by characterizing three nanomaterials differing in the percentage of graphene nanoplatelets in the temperature range from −40 °C to +60 °C. The paper demonstrates the presence of a reproducible and linear negative temperature coefficient behavior, with a temperature coefficient of the resistance of the order of $-1.5·{10}^{-3}$${°\mathrm{C}}^{-1}$. A linear sensor model is then developed and validated through an uncertainty-based approach, yielding a temperature prediction uncertainty of approximately ±2 °C. Finally, the robustness of the sensor concerning moderate environmental variations is verified, as the errors introduced by relative humidity values in the range from 40% to 60% are included in the model’s uncertainty bounds. These results suggest the realistic possibility of adding temperature-sensing capabilities to these graphene coatings with minimal increase in complexity and cost.

This work presents the design, the development and the experimental validation of a portable, low-cost sensing system for the detection of waterborne pollutants. The proposed system is based on Electrochemical Impedance Spectroscopy and PPF+Ni nanomembrane sensors. Designed in response to the increasing demand for in situ water quality monitoring, the system integrates a simplified, scalable EIS acquisition architecture compatible with microcontroller-based platforms. The sensing configuration utilises the voltage divider principle, ensuring simplicity in signal conditioning by allowing compatibility with different electrode types through passive impedance matching. In addition, new merit figures have been proposed and implemented to analyse the measures. The proposed platform was experimentally characterised for its measurement stability, accuracy and environmental robustness. Sensitivity tests using benzoquinone as a target analyte demonstrated the capability of detecting concentrations as low as 0.1 mM with a monotonic response over increasing concentrations. A comparative study with a commercial electrochemical system (PalmSens4) under identical conditions highlighted the higher resolution and practical advantages of the proposed method despite operating with a lower impedance range. Additionally, the system exhibited reliable discrimination across tested concentrations and greater adaptability for integration into field-deployable environmental monitoring platforms. Future developments will focus on optimising selectivity through new sensor materials and analytical modelling of uncertainty propagation in the analysis based on defined figures of merit.

This work presents an innovative smart system for monitoring ice formation on photovoltaic systems (PV) based on capacitive sensors made of graphene nanoplatelet (GNP) nanocomposites and a low-cost conditioning circuit. At the heart of the methodological approach is integrating a multi-gap sensor configuration, designed to maximise sensitivity to dielectric variations, and an oscillator circuit that converts capacitance variations into frequency signals, ensuring robust and noise-free transmission even in extreme environments. The sensor design includes selecting innovative materials and an optimised electrode configuration. The metrological characterisation of the system shows a sensitivity down to almost 6 Hz/pF and a resolution of less than about 1.3 pF, confirming the system’s excellent performance in variable environmental conditions and ensuring high accuracy in ice detection. Experimental tests with ice thicknesses of up to 10 mm showed perfect alignment with theoretical models. In addition, data analysis using machine learning techniques produced accurate results in classifying the presence of ice, with an accuracy of over 95% and remarkable robustness even in the presence of noise. The results confirm the system’s high performance and versatility for PV, energy and aerospace applications, proposing a smart and energy-efficient solution for ice management in PV systems.

Photovoltaic (PV) systems are increasingly being installed in cold and harsh climates, where ice and snow accumulation can significantly compromise energy $e$ and structural integrity. Traditional de-icing solutions often prove inefficient, costly, or environmentally unsustainable. This study explores the use of graphene nanoplatelet (GNP)-based nanocomposites as a high-performance, energy-efficient alternative for thermal management in PV systems. These materials, produced through scalable industrial processes, demonstrate superior thermal diffusivity and favorable electrothermal behavior compared to conventional copper under identical volumetric and electrical conditions. Experimental analyses on G-PREG composite strips with varying GNP concentrations reveal fast thermal response times and high temperature increases, highlighting their suitability for both continuous and on-demand de-icing operation modes. Results confirm that GNP-based materials outperform copper in terms of dynamic thermal performance, a volume parity, offering a promising route for efficient, low-volume and cost-effective snow and ice mitigation in PV applications.